Evaluation of the Dynamic Lane Management System at the SR-110 North / I-5 North Connector Ramp Using Paramics and TOPL

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1 CALIFORNIA PARTNERS FOR ADVANCED TRANSPORTATION TECHNOLOGIES INSTITUTE OF TRANSPORTATION STUDIES UNIVERSITY OF CALIFORNIA, BERKELEY Evaluation of the Dynamic Lane Management System at the SR-11 North / I-5 North Connector Ramp Using Paramics and TOPL François Dion, PhD, Senior Research Engineer, PATH Xiaofei Hu, Graduate Student Researcher, PATH California PATH Research Report UCB-ITS-PRR The California Partners for Advanced Transportation TecHnologies work with researchers, practitioners, and industry to implement transportation research and innovation, including products and services that improve the efficiency, safety, and security of the transportation system.

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3 STATE OF CALIFORNIA DEPARTMENT OF TRANSPORTATION TECHNICAL REPORT DOCUMENTATION PAGE TR3 (REV. 1/98) 1. REPORT NUMBER 2. GOVERNMENT ASSOCIATION NUMBER 3. RECIPIENT S CATALOG NUMBER 4. TITLE AND SUBTITLE Evaluation of Dynamic Lane Management System at the SR-11 North / I-5 North Connector Ramp Using Paramics and TOPL 5. REPORT DATE June PERFORMING ORGANIZATION CODE 7. AUTHOR(S) François Dion, Xiaofei Hu 9. PERFORMING ORGANIZATION NAME AND ADDRESS California Partners for Advanced Transportation TecHnologies (PATH) University of California, Berkeley 215 Bancroft Way, Suite 3 Berkeley, CA SPONSORING AGENCY AND ADDRESS California Department of Transportation Division of Traffic Operations, MS N Street Sacramento CA SUPPLEMENTAL NOTES 8. PERFORMING ORGANIZATION REPORT NO. UCB-ITS-PRR WORK UNIT NUMBER 11. CONTRACT OR GRANT NUMBER 51A TYPE OF REPORT AND PERIOD COVERED Final Project Report 3/24/211 to 6/3/ SPONSORING AGENCY CODE 16. ABSTRACT This project evaluates the operational and safety benefits obtained from a dynamic lane management system implemented at the entrance of the left-side SR-11 North/I-5 North Connector in Caltrans District 7. A few years ago, the number of lanes on the Connector was reduced from two to one to reduce collisions occurring at its entrance, where a sharp curves with low visibility forces traffic to slow down to 3-mph. However, this change resulted in long queues developing along SR-11. To alleviate these queues while containing the impacts on safety, a system opening the Connector s shoulder lane between 15: and 19: was activated on January 19, 21. Evaluations were to assess the operational and safety impacts of the implemented system, as well as the additional benefits that could be obtained from dynamically opening and closing the shoulder lane based on observed local traffic conditions. Evaluations were conducted using PeMS data, video recordings from CCTV cameras along SR-11, travel time runs with a GPSequipped vehicle, simulation results from a Paramics microscopic modeling of the corridor, and collision records from Caltrans Transportation System Network. Unfortunately, the uniqueness of traffic behavior along SR-11 prevented evaluations to be conducted with the TOPL macroscopic simulation models. Evaluations first revealed that between 15% and 38% of vehicles traveling on the Connector illegally utilize the shoulder lane at its entrance when it is closed because of traffic demand exceeding the entry capacity of the Connector with a single lane open. While the non-complying behavior was assessed to reduce significantly delays along the corridor independently of the dynamic lane management system, the simulation results indicate that operational benefits are still obtained from the system. However, relatively small benefits would be obtained from enabling a dynamic opening and closing of the Connector shoulder lane based on the observed traffic conditions. From a safety standpoint, collision records finally indicate that while the current system has increased the frequency of collisions at the entrance of the Connector it has also resulted in a greater reduction of collisions upstream of the Connector along SR-11, thus yielding overall net safety benefits. 17. KEY WORDS Intelligent Transportation Systems, Microscopic Simulation, Macroscopic simulation, traffic Operations, Safety Analysis 19. SECURITY CLASSIFICATION (of this report) Unclassified 18. DISTRIBUTION STATEMENT No restrictions. 2. NUMBER OF PAGES 15 pages Reproduction of completed page authorized 21. PRICE N/A

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5 CALIFORNIA PARTNERS FOR ADVANCED TRANSPORTATION TECHNOLOGIES INSTITUTE OF TRANSPORTATION STUDIES UNIVERSITY OF CALIFORNIA, BERKELEY Evaluation of the Dynamic Lane Management System at the SR-11 North / I-5 North Connector Ramp Using Paramics and TOPL François Dion, PhD, Senior Research Engineer, PATH Xiaofei Hu, Graduate Student Researcher, PATH California PATH Research Report UCB-ITS-PRR This work was performed by the Partners for Advanced Transportation TecHnologies, a research group at the University of California, Berkeley, in cooperation with the State of California Business, Transportation, and Housing Agency s Department of Transportation, and the United States Department of Transportation s Federal Highway Administration. This document is disseminated in the interest of information exchange. The contents of this report reflect the views of the authors who are responsible for the facts and accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of the State of California or the Federal Highway Administration. This publication does not constitute a standard, specification or regulation. This report does not constitute an endorsement by the Department of any product described herein. Final Report for Caltrans Task Order 51A391 TO 4 June 212 Partners for Advanced Transportation TecHnologies UC Berkeley v

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7 TABLE OF CONTENTS List of Figures... ix List of Tables... xii 1. Introduction Study corridor Existing Dynamic Lane Management System Evaluation of Current System Operation... 7 Traffic Conditions along SR Traffic Conditions on Connector Non-Authorized Use of Connector Shoulder Lane when Closed Traffic Conditions Near Merge Point on I Scan of Existing Dynamic Should Lane System Paramics Simulation Model Development Network Geometry Demand Modeling Demand Data Sources Traffic Demand at Network Entry Points Traffic Demand for Connector Development of Origin-Destination Matrices Vehicle Mix Simulation Warm-up Period Modeled Traffic Monitoring Elements Modeled Traffic Control Elements Estimation of Performance Measures Modeling of Dynamic Lane Management System Model Calibration... 3 Calibration Targets... 3 Calibration of Driver Behavior Modeling of Exit Conditions on I-5 North Calibration Results TOPL Simulation Model Development Network Geometry Modeling Demand Modeling Modeling of Dynamic Lane Management System Model Calibration Partners for Advanced Transportation TecHnologies UC Berkeley vii

8 Calibration Elements Calibration Targets... 5 Estimation of Jam Density... 5 Calibration of I-5 North Links... 5 Calibration of Connector Links Calibration of SR-11 Links Simulation Performance Evaluations Evaluation Scenarios Reference Traffic Movements... 6 Impacts of Non-Compliance on System Performance Benefits of Current Time-of-Day System Potential Benefits from Permanent Shoulder Lane Opening... 7 Potential Benefits from Dynamic Shoulder LAne Opening/Closing Safety Impacts Analysis Approach Collisions along SR Collisions At Entrance of I-5 North Connector Ramp Overall Safety Assessment Conclusions and Recommendations References Partners for Advanced Transportation TecHnologies UC Berkeley viii

9 LIST OF FIGURES Figure 1 Study Area... 3 Figure 2 Road Geometry on SR-11 Upstream of I-5 North Connector Exit... 4 Figure 3 Road Geometry on I-5 North near Connector Merge... 4 Figure 4 Elements of Existing SR-11 North/I-5 North Dynamic Lane Management System... 6 Figure 5 View from CCTV near Hill Street On-Ramp (Camera 191)... 7 Figure 6 View from CCTV near I-5 North Connector Exit (Camera 194)... 7 Figure 7 Corridor Performance (PeMS Data, Sept/Oct 211)... 8 Figure 8 Traffic Flow Performance on SR-11 near I-5 North Connector Exit... 9 Figure 9 Observed Traffic Flow Entering SR-11 from Hill Street Figure 1 Observed Traffic Flow on I-5 North Connector Figure 11 Observed Lane 1 and Lane 2 Utilization on Entrance of I-5 North Connector Figure 12 Unauthorized Utilization of Lane 2 on I-5 North Connector Figure 13 Paramics Modeling of Study Corridor Figure 14 Paramics Modeling of SR-11 Connector Split Figure 15 Paramics Modeling of I-5 Connector Merge Figure 16 Main Data Sources within Study Area Figure 17 Modeled Traffic Flow at Major Network Entry Points Figure 18 Estimated Connector Traffic Demand Figure 19 Origin-Destination Flow Matrix for the 14: to 15: Period Figure 2 Demand Profile for the Hill Street On-Ramp Flow Figure 21 Traffic Detection Stations in Paramics Network Figure 22 Paramics Lane Restriction Modeling Figure 23 Simulation Snapshots with Partial and Full Shoulder Close Compliance Figure 24 Logic for Dynamic Opening and Closing of Connector Shoulder Lane Figure 25 I-5 Speeds Downstream of Connector Merge (VDS , October 211 weekdays) Figure 26 Comparison of Paramics Simulated Speed Provides and Observed Profiles Figure 27 Comparison of Paramics Simulated Flow Rates to Observed Data Figure 28 Comparison of Paramics Simulated Point Speeds to PeMS and Observed Data Figure 29 Comparison of Paramics Simulated Travel Times to Probe Vehicle Data Figure 3 TOPL Network Modeling Figure 31 TOPL Modeling of Connector Approach on SR-11 North Partners for Advanced Transportation TecHnologies UC Berkeley ix

10 Figure 32 TOPL Modeling of Connector Merge on I Figure 33 TOPL Traffic Demand Modeling Time-of-Day System without Enforcement Figure 34 TOPL Traffic Demand Modeling Time-of-Day System with Enforcement Figure 35 TOPL Traffic Demand Modeling Shoulder Lane Always Open Figure 36 TOPL Traffic Demand Modeling Shoulder Lane Always Closed without Enforcement Figure 37 TOPL Traffic Demand Modeling Shoulder Lane Always Closed with Enforcement Figure 38 TOPL Calibration Parameters Figure 39 Observed Speed, Flow and Density along I Figure 4 TOPL Modeling Parameters for Link 23 and Link 25 along I Figure 41 Defined Time-Dependent Link Capacities for Link Figure 42 Final TOPL Simulation Results for I-5 Links Figure 43 Observed Speed, Flow and Density along SR-11 Mainline Figure 44 Observed Speed, Flow and Density along SR-11 Lane 1 near the Connector Exit Figure 45 TOPL Modeling Parameters for SR-11 Links Figure 46 Final TOPL Simulation Results for SR-11 Links Figure 47 Evaluation Scenarios Figure 48 Evaluation Traffic Groups Figure 49 Incurred Delays for Time-of-Day Scenarios with and without Enforcement Figure 5 Traffic Stream Delays for Current Time-of-Day Scenarios with and without Enforcement Figure 51 Speed Profiles for Current Time-of-Day System with and without Enforcement Figure 52 Speed Differentials between Current Time-of-Day System with and without Enforcement Figure 53 Delays for Non-Complying Scenarios with Full Closure, Time-of-Day Operation and Full Opening Figure 54 Delays for Non-Complying Scenarios with Full Lane Closure, Current Time-of-Day Operation, and Full Lane Opening Figure 55 Speed Profiles: Full Closure vs. Current Time-of-Day, without Enforcement Figure 56 Speed Differentials: Full Closure vs. Current Time-of-Day System, without Enforcement Figure 57 Range of Tested Flow and Speed Threshold for Dynamic Control Figure 58 Performance of Dynamic Opening/Closing Alternatives Figure 59 Dynamic Opening and Closing of Connector Shoulder Lane under Alternative Flow Thresholds and Non-Complying Behavior Figure 6 Dynamic Opening and Closing of Connector Shoulder Lane under Alternative Speed Thresholds and Non-Complying Behavior Figure 61 Dynamic Opening and Closing of Connector Shoulder Lane under Complying and Non-Complying Behavior for 18 vph and 4-mph Thresholds Partners for Advanced Transportation TecHnologies UC Berkeley x

11 Figure 62 Dynamic Opening and Closing of Connector Shoulder Lane under Complying and Non-Complying Behavior for 18 vph and 4-mph Thresholds Figure 63 Monthly Collision Rates on SR-11 and Connector Entry (1/27-6/21) Figure 64 Temporal Distribution of Collisions along SR Figure 65 Frequency of Collisions along SR Figure 66 Location of Collisions along SR Figure 67 Primary Collision Factors along SR Figure 68 Types of Collision along SR Figure 69 Monthly Collision Rates on SR-11 Approach to Connector (1/27-12/21) Figure 7 Temporal Distribution of Collisions on Connector Entry Figure 71 Frequency of Collisions on Connector Entry Figure 72 Primary Collision Factors at Connector Entrance Figure 73 Types of Collision at Connector Entrance Figure 74 Monthly Collision Rates on Connector Entry (1/27-6/21) Partners for Advanced Transportation TecHnologies UC Berkeley xi

12 LIST OF TABLES Table 1 Examples of Temporary Shoulder Uses in the United States and Abroad Table 2 Data Retrieved from Various Data Sources... 2 Table 3 Percentages of Complying and Non-Complying Vehicles Table 4 Calibration Targets Table 5 Comparison of Simulated Link Flows to Observed Traffic Counts Table 6 GEH Statistics for Paramics Model Table 7 Number of SR-11 Mainline Collisions in 29 and Table 8 Number of Collisions on Connector Entry in 29 and Partners for Advanced Transportation TecHnologies UC Berkeley xii

13 1. INTRODUCTION Current traffic flow levels were not envisioned in 194 when the SR-11 freeway was built. Originally, both travel directions were entirely comprised within the right-of-way that currently carries the northbound traffic. As traffic demand grew, the freeway was expanded with the construction of a separate alignment to carry the southbound traffic. However, because of the surrounding environment, there were limited opportunities to expand the ramp carrying traffic seeking to reach I-5 North from SR- 11 North. For many years, the connector between these two important regional freeways operated as a single-lane ramp. As traffic grew, the single-lane ramp became unable to carry all the SR-11 North traffic seeking to access the I-5 North freeway, resulting in long queues, often exceeding one mile in length, developing along SR-11 upstream of the Connector exit. To alleviate these queues, a second lane was then added on the Connector within the existing right-ofway. While this second lane alleviated some of the congestion on SR-11, it also increased the number of collisions occurring at the gore point of the Connector exit, where a sharp curve with limited forward visibility and shoulders forces traffic to slow down to a speed of approximately 3 mph. The increase in collisions thus led to a decision to keep only one lane open at the entrance of the Connector, but to maintain two lanes further downstream. As expected, the decision to close one traffic lane at the entrance of the SR-11 North/I-5 North Connector caused queues of vehicles to re-appear along SR-11, particularly during the afternoon peak travel periods. However, the decision also caused an increase in rear-end and sideswipe collisions. This increase in collisions was attributed to vehicles seeking to access the Connector avoiding merging onto Lane 1 as long as possible to reduce the time they would spend in the queue and forcing their way onto the Connector traffic stream just before the gore point. In an attempt to alleviate the above problems, a decision was made in 29 to deploy a pilot system that would open and close the shoulder lane within the initial 3-mph curve on the Connector on a timeof-day schedule. During the afternoon peak period, when traffic demand is the greatest, two lanes would be made available to vehicles seeking to reach I-5 North from SR-11 North. During off-peak periods, a single lane would remain available. Based on historical traffic data, it was determined that the shoulder lane should be opened at 15: and closed at 19:, on weekdays only. This system was activated on January 19, 21. To this day, this Dynamic Lane Management System (DLMS) remains the only system of this nature permanently deployed on a freeway ramp within California. As part of Technical Agreement 51A391, commonly known as the Hybrid Traffic Data Collection Roadmap, executed in September 29 between Caltrans and the University of California Regents, the California Center for Innovative Transportation (CCIT), now Partners for Advanced Transportation Technologies (PATH), was tasked with evaluating the effectiveness of the lane management system that had been implemented at the entrance of the SR-11 North/I-5 North Connector and to assess the potential for further system improvements. More specific objectives of the project were to: Assess the operational efficiency of the time-of-day lane management system that had been in operation since January 21. Assess the impacts that the current lane management system may have had on traffic safety at near the Connector exit on SR-11 and upstream of this location. Identify and evaluate potential strategies that could be implemented to convert the current time-of-day operation into a system that would dynamically open and close the shoulder lane on the Connector based on observed traffic conditions upstream of the exit. 1

14 Provide recommendations regarding the potential utilization of similar dynamic lane management systems at other freeway ramps within in California. A key aspect of the tasked evaluation was that no modifications were allowed to be made to the existing system and that no new instrumentation could be installed. This meant that all operational and safety evaluations were to be conducted using data provided by existing traffic monitoring systems and/or performance metrics produced by simulation models. It also meant that any suggested improvements made to the system had to be developed within the confines of the existing operational environment. While data provided by traffic detectors connected to Caltrans Performance Measurement System (PeMS) could allow evaluating current system performance, use of traffic simulation models was required to evaluate traffic performance along the freeway corridor under alternate operating conditions. To conduct these evaluations it was initially proposed to use the Tools for Operational Planning (TOPL), a software suite being developed at the University of California, Berkeley, explicitly for the purpose of evaluating operational control strategies. Use of this suite offered the prospect of conducting the evaluations with a newly developed macroscopic traffic simulation model that promised to simplify input data collection and processing. However, because this model was still under development and had not yet been vetted by the professional community, it was assessed that use of this model alone would carry significant risks for the evaluations. A decision was therefore made early in the project to also conduct evaluations with Paramics, a well-established microscopic traffic simulation model. This decision proved important, as the presence of unique traffic behavioral patterns along SR-11 resulted in an inability for the version of TOPL that was available to the project team to adequately replicate observed traffic conditions along the current corridor. All simulation evaluations were thus eventually conducted with Paramics. The remainder of this report describes various elements related to the evaluation project outlined above. Elements described include: Section 2 Description of study corridor. Section 3 Description of the lane management system that has been deployed at the entrance of the SR-11 North/I-5 North Connector. Section 4 Assessment of current system operations based on field data. Section 5 Scan of existing dynamic shoulder lane management systems currently in use in the United States, Europe and elsewhere. Section 6 Description of activities leading to the development of a calibrated microscopic traffic simulation model of the SR-11 North/Connector corridor. Section 7 - Description of activities surrounding the attempted modeling of the study corridor in the TOPL software suite. Section 8 Simulation-based evaluation of corridor performance under the current time-of-day operation and alternative control algorithms dynamically opening and closing the Connector shoulder lane based on observed traffic conditions. Section 9 Assessment of the safety impacts of the current time-of-day system based on an analysis of collision records for the corridor. Section 1 Summary of main conclusions and recommendations. 2

15 2. STUDY CORRIDOR Figure 1 illustrates the study area. As indicated in the introduction, the focus of the study is on the ramp allowing traffic from SR-11 North to access I-5 North. This ramp is located a short distance from downtown Los Angeles, near Dodger Stadium. To adequately support a comprehensive operational evaluation, the study area includes the full length of the SR-11 North/I-5 North Connector, a one-mile section of SR-11 upstream of the Connector exit, as well as relevant sections of I-5 upstream and downstream the Connector merge point. I-5 North Connector - SR-11 Off-Ramp Dodger Stadium I-5 North Connector - Merge with I-5 Downtown Los Angeles Figure 1 Study Area As shown in Figure 1, the I-5 North Connector exit on SR-11 is a left-side ramp featuring a sharp curve with low forward visibility. Due to its sharpness, the Connector entry curve is marked with a 3-mph advisory speed. As a result of this curve, vehicles seeking to enter the Connector have to significantly slow down prior to entering it. This need to slow down creates an effective capacity reduction that often lead to queues developing on the leftmost lane of SR-11 upstream of the Connector exit during peak travel periods. The sharp curve is also considered as a safety hazard, as evidenced by the higher frequency of collisions occurring at or near the curve compared to surrounding freeway sections. Figure 2 illustrates the geometry of the northbound traffic lanes along SR-11, from the Hill Street interchange to the Connector exit. Upstream of the Hill Street on-ramp, which typically carries traffic from downtown Los Angeles, the freeway has three general purpose traffic lanes. From the Hill Street on-ramp to the Connector exit there are four traffic lanes, with the leftmost lane (Lane 1) marked as an exit lane for almost the entire length of the section. There are no HOV or special purpose lane on this section of the freeway. Due to the constraining environment, the freeway further features ramps with sharp turns, limited deceleration or acceleration lanes, as well as narrow shoulders. There are also four short tunnels with no shoulder between the Stadium Way on-ramp and the Connector exit. 3

16 SR 11 VDS VDS VDS VDS Hill St Hill St Stadium Way Tunnel 1 Tunnel 2 Tunnel 3 Tunnel 4 Solano Ave Approximate Distance to I-5 North Connector (ft) Figure 2 Road Geometry on SR-11 Upstream of I-5 North Connector Exit Stadium Way Riverside Drive I-5 North PeMS PeMS I-5 North Approximate Distance to I-5 North Connector Merge Point (ft) Figure 3 Road Geometry on I-5 North near Connector Merge Similar to Figure 2, Figure 3 presents a schematic of the road geometry at the I-5 merge point. Upstream of the merge, there are 4 general purpose traffic lanes on the I-5 freeway mainline. The number of lanes increases to five downstream of the merge as one of the two Connector lanes is kept. There are no HOV or special purpose lanes on this section of I-5. While the relatively straight alignment of the merge allows the Connector traffic to enter I-5 at near freeway speeds in the absence of congestion, the short distance between the Connector merge point and the location where the rightmost lane is dropped downstream of the Connector creates a bottleneck that often causes queues to build up on the Connector under heavy ramp and freeway traffic. Another particularity of the Connector is the presence of an on-ramp within the Connector itself near the merge point with I-5. However, since this ramp generally carries only light traffic, it does not significantly affect operations on the Connector. 4

17 3. EXISTING DYNAMIC LANE MANAGEMENT SYSTEM Figure 4 illustrates the various components of the dynamic lane management system that was activated on SR-11 at the I-5 North Connector exit on January 19, 21. The system includes the following elements: Extinguishable Message Signs (EMS) Three EMSs are placed approximately 33 ft, 2 ft and 6 ft from the I-5 North Connector gore point on SR-11. The first sign is installed a short distance downstream of the Stadium Way on-ramp, before the first tunnel, while the second and third signs are installed before the second and fourth tunnels respectively. Electronic Lane Assignment Sign An electronic lane assignment sign placed at the gore point of the I-5 North Connector exit is used to inform drivers when the shoulder lane on the Connector entrance could be used. The sign lights up to show a left-turn arrow when the shoulder lane is open, and is extinguished when it is closed. Lighted pavement markers Lighted pavement markers are used to help enforce the use of Lane 2 on the Connector entrance as an optional lane. The markers are embedded in the pavement between Lane 1 and Lane 2, between the normal painted lane delimiters. When utilization of the shoulder lane is authorized, the markers are turned off, leaving the painted line as the only lane delimiters. In this configuration, motorists see a wide dotted white line marking an exit lane starting approximately.8 mile from the Connector exit, and a single continuous white line starting approximately 65 ft from the exit. When use of the shoulder lane is prohibited, the pavement markers are turned on to mimic a double continuous white line between Lane 1 and Lane 2 on the section of SR-11 where a continuous solid white line is painted, and a dotted line over the section marked with the exit-type wide dotted white line. The pavement markers further work in conjunction with the EMSs. When the markers are turned off, the EMSs are turned on to inform motorists of the ability to use Lane 2 as an optional lane to access the Connector. When the markers are turned on, the EMSs are correspondingly turned off. Roadside signs Two roadside signs were added as part of the Dynamic Lane Management System. The first sign, located upstream of the Hill Street interchange warns drivers that they should watch for the pavement markers (lane lights) ahead. The second sign, located just before the last tunnel upstream of the I-5 North Connector exit informs motorists that they should not cross from Lane 2 to Lane 1 when a double white line is displayed by the pavement markers. Energy absorbent crash attenuators The sand barrel crash attenuators that were previously installed at the Connector gore point were replaced by energy-absorbent REACT-35 attenuators. These attenuators were specifically designed for use at locations where impacts are expected to occur more than three times per year or where traffic congestion and maintenance management is a concern for work crews. The barrels are made of high molecular weight, high-density polyethylene plastic that allows them to typically regain 9% of their original shape after having being impacted without maintenance or the need to repair major components. 5

18 Electronic Lane Assignment Sign Energy Absorbent Crash Attenuator Lighted Pavement Markers EMS 3 Lane Marking Regulation Sign EMS 2 Lane Marking Warning Sign EMS 1 Figure 4 Elements of Existing SR-11 North/I-5 North Dynamic Lane Management System 6

19 4. EVALUATION OF CURRENT SYSTEM OPERATION This section presents an operational evaluation of the current time-of-day Dynamic Lane Management System based on field observations. The data used for this evaluation include: PeMS Data Data collected by PeMS stations along SR-11 North and I-5 North were retrieved to characterize typical traffic conditions during normal weekdays along both freeways. Data were more specifically collected for all weekdays in September and October 211, and filtered to remove any erroneous measurements due to sensor malfunction and data reflecting unusual traffic conditions when compared to the average observed daily behavior. GPS Data 16 travel time runs that were conducted along SR-11 and the Connector on August 22, 211 to obtain sample travel times and help identify locations where queuing is typically observed. During these runs, vehicle location and speed were recorded every second using a portable GPS device. CCTV Video Recordings Video recordings from two CCTV cameras located along SR-11 were captured on October 4 and October 6, 211. The first recording is from a camera located near the Hill Street on-ramp and which allowed observing traffic on both the SR-11 mainline and Hill Street on-ramp (see Figure 5). The second camera is located between the third and fourth tunnel on SR-11, about 7 ft from the I-5 North Connector exit. In addition to allowing observation of traffic movements in the last tunnel prior to the Connector exit, this camera provided a clear line of sight to the traffic entering the Connector at the end of the tunnel (see Figure 6). This allowed observing both lane changes immediately upstream of the exit, particularly when a single lane is open at entrance of the Connector, and count vehicles entering the Connector from Lane 1 and Lane 2. I-5 North Connector Off Ramp Hill Street On-Ramp Traffic SR 11 Traffic Figure 5 View from CCTV near Hill Street On- Ramp (Camera 191) Figure 6 View from CCTV near I-5 North Connector Exit (Camera 194) Figure 7 presents a summary of observed traffic conditions along SR-11, from the Hill Street on-ramp to the I-5 North Connector exit, as well as along I-5 near the merge point with the Connector. The illustrated graphs present 5-minute interval compilations of the traffic flow rate and speed data collected from PeMS stations along the corridor. To facilitate data interpretation, the shaded portion within each graph illustrates the period during which utilization of the Connector shoulder lane is currently authorized (15: to 19:). Figure 8 further presents lane-by-lane compilations of the data 7

20 Flow Rate (vph) Speed (mph) Traffic Direction Traffic Direction Flow Rate (vph) Speed (mph) Evaluation of SR-11 North / I-5 North Connector Dynamic Lane Management System VDS (SR-11) Flow Rate (vph) Time of Day (hour) Speed (mph) Time of Day (hour) 75% observed data - High Value error on Lane VDS (SR-11) Flow Rate (vph) Speed (mph) % observed data Time of Day (hour) VDS (SR-11) Time of Day (hour) 8 8 Flow Rate (bph) Speed (mph) % observed data - Card off on Lane Time of Day (hour) Time of Day (hour) VDS (SR-11) 8 8 Flow Rate (vph) Time of Day (hour) Speed (mph) Time of Day (hour) % observed data Card off on Lanes 1 and Lane 2; no data on Lane 3 VDS (I-5) Time of Day (hour) Time of Day (hour) 1% observed data VDS (I-5) Time of Day (hour) Time of Day (hour) 1% observed data Figure 7 Corridor Performance (PeMS Data, Sept/Oct 211) 8

21 Flow Rate (vph) Speed (mph) Flow Rate (vph) Speed (mph) Flow Rate (vph) Speed (mph) Evaluation of SR-11 North / I-5 North Connector Dynamic Lane Management System VDS Lane 1 Flow Rate (vph) Time of Day (hour) Speed (mph) Time of Day (hour) VDS Lane Time of Day (hour) Time of Day (hour) Lane sensor consistently returning High Value message VDS Lane Time of Day (hour) Time of Day (hour) VDS Lane Time of Day (hour) Time of Day (hour) Figure 8 Traffic Flow Performance on SR-11 near I-5 North Connector Exit 9

22 that was collected from the PeMS station located closest to the I-5 North Connector exit on SR-11 (VDS ). No information is provided on traffic conditions on the Connector since no VDS station is installed along the ramp. In each figure, the note on the right side provides information about the quality of the data that was collected. While PeMS stations along I-5 generally provided good data (1% observed data), many stations along SR-11 had operational problems. The most reliable station is the one at the Solano Avenue interchange (VDS ). This station is the only one along the section of freeway considered that typically provided 1% observed data. The stations closest to the Connector (VDS ) and near the Stadium Way on-ramp (VDS ) generally only provided 75% observed data, as both had persistent problems with one of their lane sensors. Finally, the station upstream of the Hill Street onramp (VDS 71846) generally did not return any observed value, only data inputted from nearby sensors. For the station closest to the Connector, which had a particular significance for the corridor evaluations, the problem with the collected data was specifically attributed to the sensor on Lane 2. As shown in the traffic flow diagrams of Figure 8, there were generally good agreements over Lane 1, Lane 3 and Lane 4 between the collected PeMS data and data that had been extracted from the recorded videos over the same days. While the comparisons are made based on only two days of observations, it appears as if the PeMS sensor on Lane 2 consistently underestimated the number of vehicles passing over it. Adjustments were therefore made to the collected PeMS data to correct this discrepancy. The presence of inaccurate data also resulted in viewing data from collected from VDS near the Solano Avenue as the primary source of information for characterizing traffic behavior along SR-11. The following sub-sections present a characterization of traffic conditions along various sections of the corridor that was developed based on the collected data. Specific characterizations are made for: Traffic on SR-11 North between the Hill Street on-ramp and the Connector exit; Traffic on the Connector between SR-11 and I-5; and Traffic on I-5 North near the merge with the Connector. TRAFFIC CONDITIONS ALONG SR-11 Along SR-11, the congestion that develops during the afternoon peak, when traffic demand reaches its maximum, can clearly be seen in the graphs of Figure 7 and Figure 8. The most notable effect is a reduction in traffic speeds along SR-11 from a 65-mph to 7-mph free-flow speed average to a congested speed varying between 3 mph and 45 mph depending on the location. A more detailed analysis of observed freeway speeds further indicates that traffic speed would also on occasion dip as low as 2 mph near the Connector exit. Near the Connector exit, speed reductions along SR-11 are largely due to the need for vehicles to slow down in order to take the low visibility, 3-mph sharp curve at the entrance of the Connector. Shockwaves created by vehicles changing lane at the last minute, particularly when a single lane is open at the entrance of the Connector, further contribute to the observed speed reductions. While only vehicles seeking to enter the Connector technically need to slow down, speed reductions are also observed on Lane 3 and Lane 4 as many drivers instinctively slow down when observing slow moving traffic on adjacent lanes. Vehicles slowing down to change lanes towards the median of the freeway may also contribute to some of the observed speed reductions on Lane 3 and Lane 4. 1

23 Flow Rate (vph) Evaluation of SR-11 North / I-5 North Connector Dynamic Lane Management System Further upstream along SR-11, most of the speed reductions can be attributed to frictions caused by lane changes, particularly from vehicles entering SR-11 at the Hill Street on-ramp and seeking to reach I-5 North via the Connector. As shown in Figure 9, the Hill Street on-ramp carries significant traffic during peak hour. The discrepancies between the PeMS and video data are due to sensor problems with the two nearby PeMS stations used to estimate the entry flow from the Hill Street on-ramp (VDS and VDS ). Since the on-ramp is the only traffic entry point between the two PeMS stations, data from the VDS sensors and video recordings should normally match reasonably well. However, this is not the case here since all the data from VDS are imputed, as well as data from one lane sensor from VDS Because of these imputations, the video data is generally considered to be a more accurate reflection of the flows on the Hill Street on-ramp, even if the data covers only two days PeMS Data VDS and , Sept/Oct 211 weekdays 1 5 Video Data Direct counts Oct. 4 & 6, Time of Day (hour) Figure 9 Observed Traffic Flow Entering SR-11 from Hill Street While there is no direct observation of the proportion of vehicles entering SR-11 from the Hill Street on-ramp and which subsequently travel onto the Connector, this proportion is assumed to be fairly high given that Hill Street carries traffic coming from downtown Los Angeles. A particularly challenging situation for vehicles entering SR-11 from there is the need to make two to three lane changes in less than a mile and in heavy traffic in order to access the Connector. TRAFFIC CONDITIONS ON CONNECTOR Figure 1 presents an estimate of the traffic volume carried by the Connector during a typical weekday afternoon. The illustrated data are estimates since there is no traffic monitoring station installed along the Connector. These estimates were obtained using the two following methods: Calculating the difference in flow rates between VDS and along I-5. Since there is no outflow of traffic between the two stations and the Connector is the only inflow between them, any difference in measured flow rates between the two stations can be attributed to traffic entering I-5 from the Connector. Vehicle counts extracted from the recorded videos of SR-11 traffic near the Connector exit on August 22,

24 Flow Rate (vph) Flow Rate (vph) Evaluation of SR-11 North / I-5 North Connector Dynamic Lane Management System While there is an interchange within the Connector (Riverside Drive), traffic exiting and entering the Connector at that location is relatively light. Consequently, in the absence of errors within the data, a relatively good match could be expected between the flow estimated from the PeMS and video data. As can be observed in Figure 1, the video data indeed matches reasonably well the collected PeMS data over the same month. This match thus validates the general accuracy of the PeMS data collected at the stations surrounding the Connector (with the exception of data from Lane 2 from VDS immediately upstream of the Connector on SR-11 as explained in the introduction of this section). Flow Rate (vph) Video Data Direct counts, Oct. 4 and 6, 211 PeMS Data Difference between flow rates at VDS and , Sept/Oct 211 weekdays Time of Day (hour) Figure 1 Observed Traffic Flow on I-5 North Connector Based on the data shown in Figure 1, it can be assessed that the Connector typically carries between 2,5 and 3, vph between 12: and 2:. This is an important observation, as the data indicates that a single traffic lane would theoretically not be sufficient to handle the traffic demand for the Connector within this period. Based on the data of Figure 11, particularly the data describing flow behavior on Lane 1, the capacity of Lane 1 at the entrance of the Connector, where the sharp curve is located, is assessed to be around 2 vph Connector Lane 1 Connector Lane Unauthorized Lane Utilization 1 5 Video data, Oct 4 and 6, 211 Effective Capacity (approximately 2 vph) Time of Day (hour) Time of Day (hour) Figure 11 Observed Lane 1 and Lane 2 Utilization on Entrance of I-5 North Connector 12

25 NON-AUTHORIZED USE OF CONNECTOR SHOULDER LANE WHEN CLOSED From a theoretical standpoint, significant queues should develop on SR-11 between 12: and 22: when only single a lane is open, as traffic demand during this period continuously exceeds 2, vph. However, extensive queues do not develop due to a significant proportion of vehicles illegally using the Connector shoulder lane (Lane 2) when it is officially closed. The data of Figure 12 suggests that the proportion of vehicles doing so is linked to the proportion of traffic exceeding the capacity of the open single lane. It can indeed be observed in that the non-compliance rate appears to increase with the proportion of volume along the Connector exceeding 2 vph, i.e., the assumed capacity of a single traffic lane at the Connector s entrance. Non-compliance rates peak at around 35-38%, in the 3-minute interval before the shoulder lane is officially open. The presence of a high level of non-complying vehicles brings the question of what can be done to enforce system operations. While patrol cars could be instructed to watch for non-complying vehicles and issue tickets, such a solution cannot easily be implemented due to the current road geometry. The relatively narrow shoulders along SR-11, particularly in the tunnels, do not provide space for patrol vehicles to park alongside of the road near the Connector to watch traffic. Furthermore, the narrow shoulders on the first part of the Connector would not allow patrol cars to stop vehicles to issue tickets without causing significant traffic disruptions along the Connector and SR-11. Non-Compliance Rate 4% 35% 3% 25% 2% 15% 1% 5% % Shoulder lane Open Time of Day (hour) 1, Flow Rate above 2 vph (vph) Lane 2 Non- Compliance Rate Flow Rate Above Assumed Single- Lane Capacity Source: Video data, October 4 and 6, 211 Figure 12 Unauthorized Utilization of Lane 2 on I-5 North Connector TRAFFIC CONDITIONS NEAR MERGE POINT ON I-5 Traffic conditions on I-5 North near the merge with the Connector are relevant to the evaluations as changes in traffic conditions at the entrance of the Connector on SR-11 can affect the queues forming at the I-5 merge, and thus travel conditions on the I-5 freeway itself. As mentioned earlier, the bottom two diagrams shown on Figure 7 illustrate the average hourly flow rate and average speeds over successive 5-minute intervals that were collected from the two PeMS station closest to the merge for every normal weekday in September and October 211. As can be observed, the data indicate that there is a significant drop in speed along I-5 North between 15: and 2: both upstream and downstream of the Connector merge. The drop is particularly significant upstream from the merge, as average traffic speeds quickly drop from 6 mph to 3 mph around 15:, and then stay at this level until at least 19:. 13

26 Interestingly, the period during which reduced speeds are observed at the PeMS station immediately upstream the Connector corresponds almost exactly to the period during which the shoulder lane is open at the entrance of the Connector. This could suggest that the degraded traffic conditions on I-5 North could be linked to the ability of the Connector to send more traffic towards the freeway. However, as indicated in the previous section, a significant proportion of vehicles illegally use the second Connector entrance lane when this lane is officially closed. Once 15: is reached, there is therefore no significant change in the traffic flow going through the Connector, as evidenced by the data of Figure 1. The primary cause of the observed speed drop may therefore be associated with other factors. There is no HOV lane on the section of I-5 being considered that could explain a sudden change in capacity. However, the PeMS station upstream from the merge shows a notable increase in traffic on I- 5 between 14: and 2:. This increase could explain the degraded performance observed during this period. However, data from the downstream station also shows a significant drop in speed between 15: and 19:. Congestion developing downstream of the station, either partly or wholly due to the observed increase in traffic along I-5, and propagating upstream towards the merge could thus explain in part or in entirety the degraded flow performance between 15: and 19: along the freeway. 14

27 5. SCAN OF EXISTING DYNAMIC SHOULD LANE SYSTEM A review of the professional literature was conducted to assess current practice regarding the utilization of shoulders along freeways and freeway ramps as temporary traffic lanes. This review primarily consisted in a scan of project reports and journal articles that could be retrieved from online sources. Within the scan, neither systems permanently converting shoulder lanes to traffic lanes were considered nor systems only allowing buses to use shoulder lanes. Table 1 identifies key locations around the world where shoulders are reportedly used as temporary traffic lanes, generally to help reduce congestion. In the United States, the most commonly known example is along I-66 in Northern Virginia, near Washington, D.C., where the right shoulder is open to general traffic in the eastbound direction between 5:3 and 11: to facilitate the morning commute towards downtown Washington, and between 14: and 2: in the westbound direction to facilitate the reverse afternoon commute. Similar systems are also in place along sections of I-93, I-95, SR-128, SR-3 in the Boston metropolitan area in Massachusetts, as well as on US-2 in Seattle in State of Washington and H1 in Honolulu, Hawaii. A shoulder lane utilization system that was deployed along I- 35W in Minneapolis, Minnesota further distinguishes itself from the other systems in the fact that it opens the freeway s left shoulder lane during peak hours as a HOT lane with variable pricing. All systems enabling the use of freeway shoulders found within the United States can be considered as static systems in the sense that they all open and close the shoulder on a time-of-day basis. This operational scheme is similar to how the I-5 North Connector shoulder is currently opened and closed. Another important observation is that all documented systems focus on the utilization of shoulders along the freeway mainline. The lane management system deployed at the entrance of the I-5 North Connector on SR-11 appears to be the only one that was specifically designed to operate on a freeway ramp. European experiences with shoulder lane utilization are more extensive than in the United States, as several freeways in Germany, Great Britain and the Netherlands currently allow traffic to use mainline shoulders during congestion periods. However, similar to the United States, there is no documented system specifically opening or closing shoulder lanes on freeway ramps. In addition to providing added capacity, temporary shoulder use is often considered in Europe in conjunction with speed harmonization strategies. In most cases, shoulders are opened and closed based on traffic conditions, not a fixed time schedule. Typically, shoulders are opened when traffic speeds along the freeway mainline drop below a certain threshold or when the traffic demand nears capacity. Along the M42 freeway in Great Britain, the right shoulder is for instance only opened when the mainline freeway speed drops below 6 mph. The opening of the shoulder is further accompanied by the imposition of a mandatory speed limit reduction on the freeway to provide speed harmonization. While the system that was originally deployed imposed a maximum speed of 5 mph, changes implemented in 28 have enabled the imposition of a maximum speed 6 mph. In the Netherlands, use of the left shoulder in addition to the right shoulder is also authorized on some freeway when monitored traffic volumes indicate that congestion is growing. Generally, the opening and closing of shoulders is at the discretion of the traffic management center operator. While there are mentions that operators may rely on various indicators to determine when to open or close shoulder lanes, or that suggestions may be automatically generated by the traffic monitoring system used, none of the reviewed documents provided specific descriptions of the indicators or control algorithms that are used to determine when to open or close freeway shoulder lanes. 15

28 Table 1 Examples of Temporary Shoulder Uses in the United States and Abroad Country Freeway Implementation Date United States Germany Great Britain Netherlands Italy of I-66 in Northern Virginia I-35W in Minneapolis, Minnesota I-93, I-95, SR-128 and S-3 in Massachusetts US-2 in Seattle, Washington H1 Freeway in Honolulu, Hawaii Various locations covering over 125 miles of roadways in congested corridors 11 mile section of M42 Freeway, and 7-mile section of M6 Freeway near Birmingham Various locations, covering over 75 miles of freeways 8-mile section of A22 Freeway Control Strategy 1995 Time-of-day only (Eastbound, 5:3 am-11:am; Westbound, 14: 2:) 29 Conversion of left freeway shoulder to a HOT lane that can be used from 6: AM to 1: AM and from 14: to 19:. n/a Shoulder typically open to traffic from 5:3 to 11: eastbound and from 14: to 2: westbound to increase capacity n/a Freeway shoulder opened to traffic from 15: to 19:, Monday to Friday. n/a Temporary use of shoulder in eastbound direction during the morning peak period to provide added capacity and congestion relief First section implemented in for M42 freeway 211 for M6 freeway For section implemented in 23 Under Development Temporary use of shoulder when speeds are low or traffic demand is high, often in conjunction with the imposition of lower speed limits on freeway mainlines (speed harmonization) Shoulder use authorized when speed drops below a certain threshold. When the shoulder is opened, a speed limit of 6 mph or less is imposed on the general freeway lane for speed harmonization. While the system initially in place on M42 required drivers to go back to the main traffic lanes at interchanges, use of shoulders at interchanges has been authorized since 29. Temporary use of shoulder when speeds are low or traffic demand is high, often in conjunction with variable speed limits (speed harmonization) Planned opening of emergency lane when traffic demand nears or exceeds capacity Reference(s) Ungemah and Kuhn (29) Texas Transportation Institute (21) Texas Transportation Institute (21) Ungemah and Kuhn (29) Texas Transportation Institute (21) Texas Transportation Institute (21) Texas Transportation Institute (21) Ungemah and Kuhn (29) Mirshahi et al. (27) Waller et al. (29) Easy Way (212) Ungemah and Kuhn (29) Mirshahi et al. (27) Waller et al. (29) Easy Way (212) Ungemah and Kuhn (29) Mirshahi et al. (27) Waller et al. (29) Easy Way (212) Bergmaister (26) Waller et al. (29) 16

29 6. PARAMICS SIMULATION MODEL DEVELOPMENT This section describes project activities that led to the development of a calibrated traffic simulation model in version of the Paramics microscopic simulation tool. Elements described in the following subsections include: Modeling of road network; Modeling of traffic monitoring systems; Modeling of traffic demand along corridor; Modeling of traffic control elements; Modeling of dynamic lane management system; and Simulation model calibration. NETWORK GEOMETRY Figure 13 presents a general view of the road network that was coded in Paramics. The network includes a 2-mile section of SR-11 North and 2-mile section of I-5 North, as well as the full length of the connecting ramp between the two freeways. Along SR-11, the core modeling extends from the Hill Street on-ramp to the Connector exit. Along I-5, the core modeling extends from 3 ft upstream of the merge point with the Connector to 42 ft downstream of the merge. Additional freeway sections were coded upstream and downstream of the core areas along each freeway to help assess queuing effects that may occur at network entry and exit points. Figure 14 provides a more detailed view of the modeling of the I-5 North Connector exit on SR-11, while Figure 15 illustrates the merge between the Connector and I-5 North. Zone 8 I-5 Exit Zone 7 Riverside Rd. Zone 6 SR-11 Exit Zone 3 Stadium Way Zone 4 Solano Ave. Zone 5 I-5 Entry Zone 1 SR-11 Entry Zone 2 Hill Street Figure 13 Paramics Modeling of Study Corridor 17

30 Connector Active Lane Restriction SR-11 SR-11 Figure 14 Paramics Modeling of SR-11 Connector Split Lane Drop Merge Point I-5 North Connector Riverside On-Ramp Figure 15 Paramics Modeling of I-5 Connector Merge 18

31 DEMAND MODELING The demand modeling for the study corridor sought to replicate traffic flow patterns typically observed between 12: and 22: on a normal weekday during the month of September and October. In this context, the following subsections describe the: Data used for the demand modeling; Estimation of traffic flows at network entry points; Estimation of traffic flow demand on Connector; Modeling of vehicle fleet; and Development of origin-destination matrices in Paramics. DEMAND DATA SOURCES Given the relatively small scale of the study area, traffic flow patterns within the modeled network were estimated solely based on observed traffic counts. This approach was possible due to the limited number of origin-destination pairs within the modeled network. As was illustrated in Figure 13, the coded network only includes eight traffic zones. Four of these zones are further modeled as entry-only zones while two are modeled as exit-only zones. When considering all possible trip combinations, flow rates thus only needed to be provided for 14 pairs of origin-destination zones. The data sources that were used for determining flow patterns along the corridor are shown Figure 16. These includes four PeMS stations along SR-11, two PeMS stations along I-5 North, two PeMS stations located on freeway ramps, video recordings at two locations along SR-11, and 28 Census data. VDS Connector Merge VDS (Riverside On-ramp) VDS (Riverside Off-ramp) VDS High Values on Lane 2 VDS VDS CCTV 194 Card Off on Lane 1 % Observed Data VDS VDS VDS (Stadium Way On-Ramp) CCTV 191 Figure 16 Main Data Sources within Study Area 19

32 For each data source, Table 2 further lists the period for which data was available. As indicated earlier, the primary goal of the modeling effort was to characterize traffic conditions on normal weekdays in September and October 211. While it was generally possible to retrieve data covering this period for PeMS stations along SR-11 and I-5, this was not always possible for other sources. For instance, the VDS stations on the Riverside off-ramp, located in the middle of the Connector, only allowed retrieving data captured between May and August 21. For the Riverside on-ramp, only data captured in August 21 were available. Video from CCTV cameras 191 and 194 along SR-11 could finally only be capture for a few selected days. Data from a single camera could further only be obtained for each day as a single data feed could only be forwarded to the video recording device used by Caltrans District 7 staff. Source Type PeMS Stations CCTV video recordings Census data Table 2 Data Retrieved from Various Data Sources Data Source Dates Days Type VDS 71846, , , along SR-11 Sept.-Oct. 211 Weekdays only VDS , along I-5 Sept.-Oct. 211 Weekdays only VDS on Stadium Way on-ramp Sept.-Oct. 211 Weekdays only VDS on Riverside off-ramp within May-August 21 Weekdays Connector only VDS on Riverside on-ramp within August 21 Weekdays Connector only Camera 191 near Hill Street Oct. 4 and Oct. 6, Tuesday and 211 Thursday Camera 194 near Connector exit Oct. 11 and Oct. Tuesday and 12, 211 Wednesday Hill Street on-ramp May-June 28 Weekdays only Stadium Way on-ramp May-June 28 Weekdays only Solano Avenue on-ramp May-June 28 Weekdays only 5-min flow rates 5-min flow rates 5-min flow rates Hourly flow rates Hourly flow rates 15-min flow rates 15-min flow rates Hourly flow rates Hourly flow rates Hourly flow rates To ensure that only data characterizing normal weekday traffic flow patterns were used, all collected data were filtered to remove observations pertaining to Saturdays and Sundays, as well as holidays falling on weekdays. Data from regular weekdays but characterizing unusual situations, such as flows or speeds significantly lower than values typically observed during other weekdays at the same period, were also removed based on a visual inspection. In addition to removing data pertaining to weekends and unusual weekdays, some data were also removed or adjusted to account for detector malfunction. As shown in Figure 16, some PeMS stations along SR-11 had faulty detectors or consistently produced warning messages. These technical problems notably caused some PeMS stations to produce high proportions of inputted data, i.e., data that are indirectly derived from observations from surrounding stations. The least reliable station was VDS 71846, located immediately upstream of the Hill Street on-ramp. This station only returned inputted data. Two other stations had problems with one lane detector. Health reports from VDS constantly indicated that the controller card was off on Lane 1 or that no data were being collected. For VDS , the health reports continuously indicated that high values were being 2

33 Traffic Demand (vph) Evaluation of SR-11 North / I-5 North Connector Dynamic Lane Management System produced by the sensor on Lane 2. This is the lane shared by both vehicles seeking to take the Connector and vehicles seeking to keep traveling along SR-11 past the Connector exit. Where possible, the data from the problematic sensors were adjusted based on data collected at surrounding PeMS stations or extracted from the video recordings. If adjustment was not possible, the data was simply removed from consideration. TRAFFIC DEMAND AT NETWORK ENTRY POINTS Figure 17 illustrates the rates at which vehicles were estimated to enter the modeled network from the southern end of SR-11, the eastern end of I-5 North, the Hill Street on-ramp, and the Stadium Way onramp. Flow rates at which vehicles were assumed to enter the network from the Solano Avenue and Riverside on-ramps are not shown, as these rates never exceed 15 vph at the height of the peak period and are therefore negligible when compared to the flow rates at other entry points. While no PeMS station provided direct flow observations for the important Hill Street on-ramp, data from nearby PeMS stations along SR-11 were initially used to obtain a rough estimate of traffic volumes on this ramp. Since the ramp is the only entry point between VDS and VDS , and since there is no exit point between them, differences in observed flow rates between the two VDS stations could thus be directly be attributed to the Hill Street on-ramp traffic. However, the facts that VDS only provided inputted data and that the controller on Lane 2 at VDS was off casted doubt on the validity of this approach. This led to using instead ramp flows estimated from the video recordings captured from CCTV Camera 194 on October 4 and 6, 211, to estimate Hill Street on-ramp flows within the modeling period. 7, 6,5 I-5 Entry 6, 5,5 SR-11 Entry 5, 4,5 4, 3,5 3, 2,5 Hill Street On-Ramp 2, 1,5 1, 5 Stadium Way On Ramp 12: 13: 14: 15: 16: 17: 18: 19: 2: 21: 22: Time of Day Figure 17 Modeled Traffic Flow at Major Network Entry Points 21

34 Flow Rate (vph) Evaluation of SR-11 North / I-5 North Connector Dynamic Lane Management System TRAFFIC DEMAND FOR CONNECTOR Similar to the Hill Street on-ramp, there were no direct traffic flow observations on the Connector. On SR-11, the closest PeMS station to the Connector was VDS , located roughly 75 ft upstream of the Connector entrance. While it could reasonably be assessed that traffic flow captured by the sensor installed on Lane 4 only included vehicles destined to the Connector, the same could not be said for the flow captured by the sensor on Lane 3, as vehicles traveling on the this lane could either take the Connector or stay on SR-11 after reaching the Connector. Data from the sensor installed on this particular lane also consistently showed a High Value error in the PeMS system, thus casting additional doubt on its accuracy. A solution to above problem was to use the data recorded by VDS and on I-5 to estimate the traffic volumes on the Connector. As was shown in Figure 16, one station is located upstream of the merge between the Connector and I-5, while the other is located downstream of the merge. Since the Connector is the only traffic entry point between the two stations and since there is no exit point between them, any difference in recorded traffic flows between the two stations could be attributed to traffic coming from the Connector. Furthermore, since both PeMS stations generally provided good data over the sampling period (1% observed data), a good confidence could be assigned to the estimates. Figure 18 shows the hourly flow rates that were estimated for successive 5-minute intervals based on the I-5 PeMS data. One element of uncertainty that remained with the estimates was the lack of recent information about the number of vehicles leaving and entering the Connector from the Riverside onramp and off-ramp located midpoint within the Connector. Available data from 21 suggested that flows on these two ramps are generally small, typically less than 15 vph. Discussions with Caltrans staff confirmed this assessment and led to assuming that the observed 21 flow rates could be retained for the simulation demand modeling Video Data Direct counts, Oct. 4 and 6, 211 PeMS Data Difference between flow rates at VDS and , Sept/Oct 211 weekdays Time of Day (hour) Figure 18 Estimated Connector Traffic Demand 22

35 To further validate the estimated Connector flow rates, vehicle counts were extracted from the video recordings from CCTV Camera 194 near the Connector exit on SR-11. The dotted line in the graph of Figure 18 shows the estimated flow rates that were extracted from the videos. As can be observed, there is a good agreement between the flow rates estimated from the video and PeMS data. The small observed differences can be explained by the fact that the PeMS data is based on an average derived from a two-month observation period while the video data is based on only two days of observations. Some differences can also be attributed to a net inflow or outflow of traffic at the Riverside ramps within the Connector. DEVELOPMENT OF ORIGIN-DESTINATION MATRICES The final step in the modeling of traffic demand was the coding of origin-destination matrices in Paramics. Separate matrices were developed for each simulation hour based on the traffic zones illustrated in Figure 13. The O-D matrix that was developed for the 14: to 15: period is shown as an example in Figure 19. As indicated in Section 7.3.1, since only one traffic direction is simulated along SR- 11, the Connector and I-5, only 14 flow rates needed to be estimated within each matrix. The availability of flow estimates for each network entry and exit point further allowed calculating manually the likely flow rates associated with each pair of origin and destination zones. Figure 19 Origin-Destination Flow Matrix for the 14: to 15: Period To account for the fact that traffic demand typically does not remain constant within each hour, optional demand profiles were provided to Paramics to enable flow rate adjustments within each onehour simulation period. The profiles were developed to allow Paramics to adjust every 5-minute the rate at which vehicles are released from each origin. The profiles do not change total number of vehicles released within an hour, just the number of vehicles released within each successive interval. The release rates to code within each 5-minute interval were determined based on an analysis of 5- minute PeMS data from VDS 71846, and Three profiles were developed to reflect specific flow patterns between along major traffic movements: one for the traffic entering SR-11, one for the traffic entering I-5, and one for the Hill Street on-ramp traffic. As an example, Figure 2 shows the profile that was developed for the Hill Street on-ramp. The numbers shown in each cell within the profile are percentages multiplied by 1. Each row sums up to a total of 1 percent. 23

36 Figure 2 Demand Profile for the Hill Street On-Ramp Flow VEHICLE MIX The fleet of vehicles that was modeled for the corridor assumes that nearly all traffic is composed of passenger cars, SUVs, pickup trucks, and delivery trucks. No heavy trucks are assumed to travel along the corridor during peak periods. While a few buses travel the corridor, their proportion and impact on traffic was deemed negligible for the purpose of the study. The specific vehicle types and proportions of vehicles belonging to each type that were modeled are based on other simulation work recently conducted within the Los Angeles area (notably for the Corridor System Management Plan evaluations). SIMULATION WARM-UP PERIOD A 3-minute warm-up period is used to ensure that evaluations always start with a network loaded with representative traffic. For evaluations periods starting at 13:, this means that individual simulation runs actually start at 12:3. To ensure that representative queues develop at congestion hotspots within the network, the full traffic demand corresponding to the warm-up is further simulated. Contrary to what may be done in other modeling efforts, there is no reduction factor applied to the simulated traffic. Finally, to avoid biasing the simulation results, no operational statistics are compiled during the warm-up period. 24

37 MODELED TRAFFIC MONITORING ELEMENTS In addition to roadway links, several traffic monitoring stations were modeled along the corridor. As shown in Figure 21, this includes four PeMS stations along SR-11 North, two PeMS stations along I-5 North, and five dummy stations along the Connector. All traffic monitoring stations are assumed to be standard single loop detectors. While all detectors replicating PeMS stations were placed at their exact locations in the real network, the virtual detectors coded along the Connector were placed at strategic locations for system performance analysis. PeMS Connector Merge Connector Virtual Station 5 (55 ft from merge) Connector Virtual Station 4 (143 ft from merge) Connector Virtual Station 3 (237 ft from merge) PeMS PeMS 3 (771667) PeMS 4 (771673) Connector Virtual Station 1 (entry curve) Connector Virtual Station 2 (31 ft from merge) PeMS 1 (71846) PeMS 2 (772513) Hill Street Ramp Virtual Station Figure 21 Traffic Detection Stations in Paramics Network MODELED TRAFFIC CONTROL ELEM ENTS The modeled Paramics network does not include signalized intersections or ramp meters. While the Riverside on-ramp within the Connector is metered, the very low traffic volumes that are normally observed on this ramp (typically less than 1 vph) did not warrant the modeling of the metering system in use on the ramp, particularly when considering that no other ramp along the corridor is metered. In this case, it was assessed that this omission should not significantly affect simulation results due to the very low ramp volumes. Due to the presence of sharp entry and exit curves, as well as very short acceleration lane, vehicles utilizing the Solano Avenue on-ramp are normally required to make a stop before entering SR-11. However, Paramics does not allow stop signs to control traffic movements on freeway on-ramps. To 25

38 correctly replicate the observed behavior of traffic at the ramp, a surrogate solution was to require all vehicles entering SR-11 from Solano Avenue to slow down to a speed of 5 mph before entering the ramp. Since Paramics only allows vehicles on freeway ramps to merge with the mainline traffic when a suitable gap is found, this forced slowdown reduced the number of gaps perceived as suitable and thus allowed Paramics to correctly mimic the effects of the stop sign on the behavior of the entry traffic. ESTIMATION OF PERFORMANCE MEASURES Paramics normally estimates delays against the desired free-flow speed of each vehicle. This is a characteristic that normally varies from one simulated vehicle to the next. While a vehicle s desired speed is influenced by the speed limit defined for each roadway link, it is often set above the speed limit. The degree to which the desired speed will exceed the coded speed limit will depend on the aggressiveness level assigned to each driver by Paramics. This is a parameter that is randomly determined. Aggressive drivers will generally have a desired speed above the speed limit, while less aggressive drivers will have a desired speed close or below the speed limit. To provide delay estimates matching more closely the statistics reported by PeMS and other Caltrans systems, custom delay estimation functions were developed and implemented within Paramics using the software s Application Programming Interface (API). These functions enable the automated compilation of the following statistics for each vehicle at each simulation time step: 6-mph threshold delay (6-mph delay): Delay estimated against a 6-mph reference speed. Any vehicle going above 6 mph does not have delay estimated against it. For vehicles traveling at a speed below 6 mph, the reference speed for the delay calculations is the lowest between 6 mph and the vehicle s desired speed on the link on which it is traveling. 35-mph threshold delay (35-mph delay): Delay estimated against a 35-mph reference speed. Any vehicle going above 35 mph does not have delay estimated against it. For vehicles traveling at a speed below 35 mph, the reference speed for the delay calculations is taken to be the lowest between 35 mph and the vehicle s desired speed on the link on which it is traveling. Stopped delay: Time a vehicle remains immobilized. Unlike many other traffic simulation tools, where network-wide statistics are updated only after a vehicle has reached its destination, the custom plug-in functions that were developed for this project automatically update network-wide incurred delays at each time step. This approach was adopted to allow the evaluations to take into consideration the delays that have been cumulated by vehicles still traveling within the network at the end of a simulation run. However, similar to other simulation tools, delays cumulated by vehicles waiting to enter the simulated road network due to congestion at the selected entry points are not retained. These delays are discarded based on the rationale that they do not reflect travel behavior on actual roadway elements. MODELING OF DYNAMIC LANE MANAGEMENT SYSTEM Modeling of the dynamic lane system in use at the I-5 North Connector entry was achieved by using within Paramics a lane restriction preventing vehicles from using the Connector shoulder lane when it is active. When the restriction is active, simulating a closed shoulder, all vehicles coded to respond to the 26

39 restriction seek to merge onto Lane 1 before entering the Connector. When it is deactivated, simulating an open shoulder lane, vehicles are then free to enter the Connector using either Lane 1 or Lane 2. An unforeseen problem was how to simulate vehicles using the shoulder when it is officially closed. This problem was solved by defining two sets of vehicles: a first set models vehicles with drivers always respecting the rules regarding the shoulder opening and closing, while a second set models vehicles with drivers disregarding the rules. Table 3 shows the proportions of complying and non-complying vehicles that were modeled based on 15-minute flow data extracted from the videos captured by CCTV Camera 194 near the Connector exit on SR-11. Between 12: and 13:, the proportion of non-complying vehicles is assumed to correspond to nearly 2% of all vehicles seeking to take the Connector. The proportion then gradually increases with each new simulation interval until 17: is reached, at which point the proportion is assumed to decrease again. While there is no direct observation of non-complying behavior between 15: and 19:, as the Connector shoulder lane is officially open, it was required to model a proportion of non-complying vehicles within this interval to allow evaluations under operational alternatives that may close the shoulder lane during this period. For intervals within this period, the proportions of non-complying behavior were simply estimated based on the proportion of Connector traffic exceeding 2 vph. To adequately replicate the observed non-complying behavior at the Connector entrance, lane restrictions were applied to prevent all vehicles of type 1 through 16 (complying vehicles) to use the Connector shoulder lane when the restriction is active, thus effectively forcing them to merge onto the leftmost traffic lane on links upstream of the Connector. Figure 22 illustrates the implementation of the lane restriction in Paramics. When the restriction is inactive, the vehicles are free to use either lane on the Connector entry. Since non-complying vehicles are modeled as vehicle of type 17 to 22, these vehicles are not affected by the restriction. These vehicles are free at all time to use both lanes at the Connector entry point. Vehicle Type Table 3 Percentages of Complying and Non-Complying Vehicles Period : 14: 15: 16: 17: 18: 19: 2: 21: to to to to to to to to to 14: 15: 16: 17: 18: 19: 2: 21: 22: 12: to 13: Complying Vehicles Mustang Crown Victoria Focus Sedan F-15 Pickup Windstar Minivan Ford Explorer Small Delivery Truck Small Delivery Truck Non Complying Vehicles Mustang Crown Victoria Focus Sedan F-15 Pickup Windstar Minivan Ford Explorer

40 Figure 22 Paramics Lane Restriction Modeling Connector with Full Compliance Connector with Partial Compliance Noncomplying vehicles Figure 23 Simulation Snapshots with Partial and Full Shoulder Close Compliance 28

41 Figure 23 presents two snapshots of the modeled traffic behavior. The snapshot on the left illustrates a situation in which all vehicles comply with the shoulder lane closure. This situation was simulated by simply instructing Paramics to generate no vehicle belonging to the non-complying group (vehicles of type 17 and above). In the snapshot on the right, some non-complying vehicles are observed using the Connector shoulder lane when the lane restriction is active (as shown by the hatched pattern on the right lane along the Connector entry curve). Enabling Paramics to dynamic open and close the Connector shoulder lane within the course of a simulation further required the development of custom plug-in module using the software s Application Programming Interface (API). The interface was used to develop a lane management control module activating or deactivating the modeled lane restriction in Paramics at specific points in time or when defined conditions are met. Four control options were coded within the lane management control module: Option 1: Keep the shoulder lane closed at all times. Option 2: Open and close the shoulder lane at the times specified by the model user in a textbased input file Option 3: Open and close the shoulder lane based on measured flow rate and speed at VDS upstream of the Connector exit on SR-11. Option 4: Keep the shoulder lane open at all times. Figure 24 Logic for Dynamic Opening and Closing of Connector Shoulder Lane Figure 24 illustrates the logic for dynamically opening and closing the Connector shoulder lane based on traffic conditions that was tested with Paramics. The logic is based on the premise that the shoulder lane needs to be opened to provide additional capacity in periods of high traffic demand but needs to be kept closed at other times for safety reasons. The speed-flow profile shown in Figure 24 illustrates typical traffic behavior on Lane 1 at the PeMS stations closest to the Connector exit on SR-11 (VDS ). Within the diagram, data points in the upper-left corner represent free-flow or near-flow conditions, and thus operational regimes for which the Connector shoulder lane can remain closed. 29

42 From this region, increases in flow rate above a certain threshold or speed reductions below a certain thresholds are indicators that traffic conditions along SR-11 are approaching congestion or that a congested state has already been reached, and thus indicators that the Connector shoulder lane should be opened. Based on the above premises, the following logic was coded within the Paramics dynamic lane management plug-in module to open and close the Connector shoulder lane: Open the Connector shoulder lane if the observed flow rate at VDS has exceeded the defined flow threshold in at least 9 of the last 1 one-minute intervals OR if the observed traffic speed at VDS has been below the defined speed threshold in at least 9 of the last 1 one-minute intervals. Close the Connector shoulder lane if the observed flow rate at VDS has remained below the defined flow threshold in at least 9 of the last 1 one-minute intervals AND the observed traffic speed at VDS has remained above the defined speed threshold in at least 9 of the last 1 one-minute intervals. To avoid opening or closing the shoulder lane too frequently in response to variations in traffic flows and speed, no action is to be made on the status of the shoulder lane for at least 15 minutes following an action to open or close the lane. MODEL CALIBRATION This section summarizes activities that have led to the development of a calibrated Paramics model for the study area. Elements discussed in this section include: Calibration targets; Calibration of driver behavior; Modeling of exiting conditions on I-5 North; and Calibration results. CALIBRATION TARGETS Table 4 describes the targets that were used to calibrate the developed Paramics simulation model and ensure that this model truly represents typical weekday afternoon and early evening traffic patterns. These targets correspond to the guidelines recommended by FHWA and Caltrans and respectively outlined in the Traffic Analysis Toolbox III: Guidelines for Applying Traffic Simulation Modeling Software (Dowling, Skabardonis and Alexiadis, 24) and Guidelines for Applying Traffic Microsimulation Modeling Software (Dowling, Holland and Huang, 22) documents. An important note regarding these guidelines is that they are meant to be calibration stopping criteria examples, not criteria that must absolutely be respected. In particular, the FHWA document indicates that specific calibration targets should be developed for each modeling effort to reflect expected minimum performance requirements and available resources. The guidelines generally seek to have individual link flows and the summation of all link flows within a network matching observed values. Typically, link flows are to have deviations of 15% or less in 85% of the cases. To assist with the determination of the degree to which a particular model replicates field observations, the GEH statistics is frequently used. The GEH is a standardized comparison measure that 3

43 was proposed by Geoffrey E. Havers in the 197s to avoid some of the pitfalls that occur when using simple percentages to compare sets of volumes that may exhibit a wide range of variability. This statistics is calculated as follows: (E V)2 GEH =. 5 (E + V) where: E = Model estimated volumes V = Field counts Because the GEH statistic is self-scaling, it allows to compare in a single test links with high-volume, such as freeway segments that may carry over 6 vph, and links with low-volumes, such as ramps that may carry only 15 vph. Table 4 Calibration Targets Measure Calibration Criteria Acceptance Target Link flows Individual link flows: Flow within 1 vph for links with flow < 7 vph Flow within 15% for links with flow between 7 and 27 vph Flow within 4 vph for links with flow > 27 vph GEH statistic < 5 > 85% of cases > 85% of cases > 85% of cases > 85% of cases Travel Times Visual Audits Sum of all link flows: Flow within 5% GEH < 4 Journey times within network: Within 15% or 1 minute, whichever criterion is higher Individual link speeds: Visually acceptable speed-flow relationships Bottlenecks: Visually acceptable queuing For all link counts For all link flows > 85% of cases To analyst s satisfaction To analyst s satisfaction CALIBRATION OF DRIVER BEHAVIOR Calibration of driver behavior first consisted in adjusting network-wide car-following parameters. This included: Setting the mean target headway to 1. s; and Setting the mean driver reaction time to.6 s. Following the adjustment of the global parameters, link-specific parameters were adjusted to enable the simulated car-following and lane-changing behavior to match observed traffic conditions along the corridor. Adjustments that were made to link-specific parameters include: Adjusting the link headway multiplication factor on SR-11 links downstream of the Hill Street on-ramp, near the Connector exit on SR-11, and on links on I-5 near the Connector merge. Setting the reaction factor multiplier for the last Connector link upstream of the I-5 merge to replicate the fact that motorists entering the merge often start planning lane changes on their approach to the merge. 31

44 In addition to the link-specific parameters that could be modified through standard coding interfaces, driver behavior parameters were further adjusted using the Application Programming Interface. The interface was more specifically used to develop functions performing the following actions: Adjustments to the speed at which vehicles approach the 3-mph curve at the entrance of the Connector while traveling on the two leftmost lanes of the last link upstream of the Connector on SR-11. Adjustments to the gap-acceptance logic used by vehicles seeking to use the Connector when attempting a make a lane change toward either Lane 1 or Lane 2 on the last SR-11 link upstream of the Connector. Adjustments to the gap-acceptance logic used by vehicles from the Connector attempting to merge on the right mainline lane of I-5 at the Connector/I-5 merge point. Adjustments to the range of acceptable lanes onto which individual vehicles can travel along SR- 11 and I-5. This included creating biases towards using left lanes for vehicles along SR-11 seeking to use of the Connector, and enticing vehicles seeking to continue traveling on SR-11 past the Connector exit to avoid using Lane 1, particularly close to the Connector exit. Adjustments to the proportions of vehicles traveling in each lane upstream of the Connector exit on SR-11 and along I-5 to better match observed data from PeMS stations and CCTV videos. Adjustments to the speed at which vehicles follow other vehicles to replicate observed aggressiveness along the corridor. Adjustments to the car-following logic used by individual vehicles on SR-11 links between the Hill Street on-ramp and the Connector exit to reduce or increase, depending on the case, crosslane influences on the determination of the speed at which individual vehicles seek to travel while on a given traffic lane. MODELING OF EXIT CONDITIONS ON I-5 NORTH As part of the calibration process, special consideration was required to adjust traffic operations on the links carrying the I-5 traffic outside the modeled network. As shown in Figure 25, PeMS data indicates a significant drop in traffic speed along I-5 downstream of the merge with the Connector during the afternoon peak period. The blue diamonds in the figure illustrate average 5-minute speed observations from a specific weekday during October 211, while the red squares represent the average of all weekdays in the month. While the observed drop in speed can be partly explained by frictions from lane changes made by vehicles having just entered I-5 from the Connector, traffic data from PeMS stations further downstream along I-5 suggests that some of the observed congestion may also be attributed to causes that are external to the study area. Since modeling a significant portion of I-5 North was outside the scope of the project, the observed congestion along I-5 North downstream of VDS was modeled by imposing the speed at which vehicles would travel on roadway segments downstream of the Connector merge. Similar to other modeling elements, this adjustment was coded using Paramics Application Programming Interface (API). In this case, vehicles traveling on the segment on which VDS is located and segments further downstream have their speed arbitrarily adjusted to match the average traffic speeds measured by the VDS station during each 5-minute interval contained within the simulation period during normal weekdays in September and October

45 Figure 25 I-5 Speeds Downstream of Connector Merge (VDS , October 211 weekdays) 33

46 CALIBRATION RESULTS Table 5 compiles the number of links with observed traffic flow counts for which the absolute difference between the simulated and observed flow rates exceed the threshold listed in Table 4. These comparisons are based on an average of 2 simulation runs with different number seeds. As can be observed, links with observed flows below 27 vph generally satisfy the calibration criteria across all on-hour evaluation periods. While flow differences exceeding the calibration target are observed for a certain number of links carrying more than 27 vph, the proportion of non-complying links remain within the acceptance target. Table 5 Comparison of Simulated Link Flows to Observed Traffic Counts Time Flow <7 vph 7 vph < Flow < 27 vph Flow >27 vph All Links Difference > 1 vph Difference > 15% Difference > 4 vph Links Count % Links Count % Links Count % Links Count % 13: 2 % 2 % % 1 1 1% 14: 2 % % % 1 1 1% 15: 1 % 1 % % 1 1 1% 16: 1 % 2 % 7 % 1 % 17: 1 % 2 % 7 % 1 % 18: 1 % 2 % 7 % 1 % 19: 1 % 2 % 7 % 1 % 2: 1 % 3 % % 1 2 2% 21: 2 % 2 % % 1 2 2% Period 12 % 16 % % 9 7 8% Table 6 GEH Statistics for Paramics Model Time SR-11 Connector I-5 All PeMS 1* Hill St PeMS 2* Stadium PeMS 3 PeMS 4* Entry Exit PeMS 1 PeMS 2 Count Ramp Ramp Stations 13: : : : : : : : : * VDS Stations with technical problems Individual Stations: GEH < 5: 89% of links All Stations Combined: GEH < 4: 89% of links Table 6 further compiles the GEH statistics for the various links for which observed traffic volumes are available. Overall, the table shows that 89% of the links with observed traffic counts have GEH values of less than 5 across all one-hour evaluation periods, and that the sum of all flows within the network produced a GEH below 4 in 8 out of the 9 simulation periods (89% of cases). In this assessment, it should be mentioned that some of the comparisons are made against flow estimates from PeMS stations that had been documented to have sensor problems, in particularly VDS near the Hill Street on-ramp, and VDS between the Hill Street and Stadium Way on-ramps. In Table 6, it can be observed that more than half of the cells with GEH values higher than five are associated with these 34

47 two locations. When removing these two locations from consideration, it can then be assessed that that calibration targets for individual links are met in 95% of the cases. Figure 26 provides another look at the quality of model calibration. This figure compares the simulated speed flow profiles along SR-11 and I-5 against the profiles estimated from PeMS data. To facilitate the evaluation, the figure also shows the speed profile that was generated along the Connector from simulation data, even though there is no traffic monitoring station along the Connector. Overall, the simulated speed profiles show good agreement with field data for both modeled sections of SR-11 and I-5. Along SR-11, the start time, end time, and physical extent of the congestion developing upstream of the Connector exit are replicated with reasonable accuracy. While the field data did not allow assessing the extent of the congestion developing along I-5, the profile comparison shows a reasonable replication of the start and end times of the congestion near the Connector merge along I-5. In Figure 26, the simulated speed profile for the Connector shows that congestion starts to develop along the facility sometime after 14:3 and typically lasts up to 19:3 or 19:45. The data further shows that the queue that develops from the congestion typically reaches the Riverside interchange, and that traffic moves at reduced speeds along the entire length of the Connector between 17: and 19:. The simulation data also indicates that the congestion that develops on the Connector does not typically propagates onto SR-11, as evidenced by the nearly constant 3-mph speeds recorded by the virtual detector placed on the Connector entry curve. This is consistent with field observations, which indicate that traffic speeds on Lane 1 and Lane 2 at VDS generally remain above 3 mph during the afternoon peak period. This suggests that congestion along SR-11 is primarily due to traffic slowing down in preparation to take the Connector entry curve, as well as lane changes made by vehicles attempting to enter Lane 1 or Lane 2 ahead of the Connector exit. Figure 27 and Figure 28 further compare on a lane-by-lane basis the simulated speeds and flow rates for links on which a PeMS station is located against the speeds and flow rates returned by the PeMS sensors. The simulated data are for an average of 2 simulation runs while the field data represent the average of all normal weekdays in September and October 211. The data are further segmented for each simulation interval. In Figure 28, the shaded area also represents the range of observed 5-minute flow rates and speeds between 12: and 22:. As can be observed, the simulated speed and flow rates generally match the field data and fall within the range of observed variability. As explained previously, the larger difference observed for the flow rates on Lane 1 on the link where VDS is located can be explained by the field sensor not recording any observed data due to a controller card being off. Similarly, the differences across all lanes on the link with VDS are explained by the sensor returning only inputted data derived from observations from neighboring PeMS stations. Figure 29 finally compares the time taken by vehicles to travel in the simulations from the SR-11 entry point to the I-5 North exit point against travel times between the same two points that were recorded on August 22, 211 during a series of probe vehicle runs with a GPS-equipped vehicles. While caution should be made when making comparisons against a single day of field observations offering a very limited set of data, it can be nevertheless be observed that the GPS-measured travel times from the probe vehicle runs generally fall within the range of speeds returned by the simulation model, thus further enforcing the validity of the calibrated model. 35

48 SR-11 North I-5 North Connector Observed Simulated Observed Simulated Simulated PeMS 1 (71846) (Hill Street On-Ramp) PeMS 2 (772513) (Stadium Way On-Ramp) PeMS 3 (771667) (Solano Avenue Ramps) PeMS 4 (771673) (Upstream Connector) PeMS 1 (71846) (Hill Street On-Ramp) PeMS 2 (772513) (Stadium Way On-Ramp) PeMS 3 (771667) (Solano Avenue Ramps) PeMS 4 (771673) (Upstream Connector) 13: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : PeMS 5 (716951) (Upstream Connector) Figure 26 Comparison of Paramics Simulated Speed Provides and Observed Profiles PeMS 6 (728371) (Downstream Connector) PeMS 5 (716951) (Upstream Connector) PeMS 6 (728371) (Downstream Connector) Virtual Station 1 (Entry Curve) Virtual Station 2 (31 ft from I-5 Merge) Virtual Station 3 (237 ft from I-5 Merge) Virtual Station 4 (Riverside Ramps) Virtual Station 5 (55 ft from I-5 Merge) 36

49 13: 14: 15: 16: 17: 18: 19: 2: 21: 13: 14: 15: 16: 17: 18: 19: 2: 21: 13: 14: 15: 16: 17: 18: 19: 2: 21: 13: 14: 15: 16: 17: 18: 19: 2: 21: 13: 14: 15: 16: 17: 18: 19: 2: 21: 13: 14: 15: 16: 17: 18: 19: 2: 21: Flow Rate (veh/hr) Flow Rate (vph) 13: 14: 15: 16: 17: 18: 19: 2: 21: 13: 14: 15: 16: 17: 18: 19: 2: 21: 13: 14: 15: 16: 17: 18: 19: 2: 21: 13: 14: 15: 16: 17: 18: 19: 2: 21: 13: 14: 15: 16: 17: 18: 19: 2: 21: Flow Rate (vph) 13: 14: 15: 16: 17: 18: 19: 2: 21: 13: 14: 15: 16: 17: 18: 19: 2: 21: 13: 14: 15: 16: 17: 18: 19: 2: 21: 13: 14: 15: 16: 17: 18: 19: 2: 21: 13: 14: 15: 16: 17: 18: 19: 2: 21: FLow Rate (vph) Flow Rate (vph) Flow Rate (vph) Flow Rate (vph) Evaluation of SR-11 North / I-5 North Connector Dynamic Lane Management System SR-11 North VDS Upstream Hill Street On Ramp Lane 1 Lane 2 Lane 3 All Lanes VDS Between Hill Street and Stadium Way On Ramps Lane 1 Lane 2 Lane 3 Lane 4 All Lanes VDS Solano Avenue Lane 1 Lane 2 Lane 3 Lane 4 All Lanes VDS Upstream Connector Exit Lane 1 Lane 2 Lane 3 Lane 4 All Lanes Paramics, Average of 2 Runs PeMS Sept/Oct 211 Average Conncector 25 2 Lane 1 Lane 2 (Shoulder) Connector Entrance (Video Data) All Lanes I-5 North 25 2 VDS Upstream Connector Merget Lane 1 Lane 2 Lane 3 Lane 4 All Lanes VDS Downstream Connector Merge Lane 1 Lane 2 Lane 3 Lane 4 Lane 5 All Lanes Interval Start Time Figure 27 Comparison of Paramics Simulated Flow Rates to Observed Data 37

50 13: 15: 17: 19: 21: 13: 15: 17: 19: 21: 13: 15: 17: 19: 21: 13: 15: 17: 19: 21: 13: 15: 17: 19: 21: 13: 15: 17: 19: 21: Speed (mph) Speed (mph) 13: 15: 17: 19: 21: 13: 15: 17: 19: 21: 13: 15: 17: 19: 21: 13: 15: 17: 19: 21: 13: 15: 17: 19: 21: Speed (mph) Speed (mph) Speed (mph) Speed (mph) Evaluation of SR-11 North / I-5 North Connector Dynamic Lane Management System SR-11 North VDS Upstream Hill Street On Ramp Lane 1 Lane 2 Lane 3 All Lanes Paramics, Average of 2 Runs PeMS Data, Sept/Oct 211 Average 8 6 VDS Between Hill Street and Stadium Way On Ramps Lane 1 Lane 2 Lane 3 Lane 4 All Lanes VDS Solano Avenue Lane 1 Lane 2 Lane 3 Lane 4 All Lanes VDS Upstream Connector Exit Lane 1 Lane 2 Lane 3 Lane 4 All Lanes 4 2 I-5 North 8 VDS Upstream Connector Merget Lane 1 Lane 2 Lane 3 Lane 4 All Lanes VDS Downstream Connector Merge Lane 1 Lane 2 Lane 3 Lane 4 Lane 5 All Lanes Interval Start Time Figure 28 Comparison of Paramics Simulated Point Speeds to PeMS and Observed Data 38

51 Travel Time (min) Time of Day (Hour) Run 1 Run 2 Run 3 Run 4 Run 5 Run 6 Run 7 Run 8 Run 9 Run 1 Run 11 Run 12 Run 13 Run 14 Run 15 Run 16 Run 17 Run 18 Run 19 Run 2 GPS Data Figure 29 Comparison of Paramics Simulated Travel Times to Probe Vehicle Data 39

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53 7. TOPL SIMULATION MODEL DEVELOPMENT This section describes attempts that were made to develop a calibrated traffic simulation model using the TOPL macroscopic simulation tool currently under development at the University of California, Berkeley. Elements described in the following subsections include: Modeling of road network; Modeling of traffic demand along corridor; Modeling of dynamic lane management system; and Simulation model calibration results. NETWORK GEOMETRY MOD ELING Figure 3 illustrates the road network that was coded into TOPL. The figure is a screenshot of TOPL s Network Editor, which allows networks to be coded directly on top of a Google map. The boundaries of the illustrated network are the same as those for the Paramics network described in Section 6. Similar to other simulation tools, road networks are modeled in TOPL as a series of nodes and links. However, a major distinction with microscopic simulation models is that TOPL does not simulate lanespecific behavior within a link. Vehicles are processed through a link based on the aggregate capacity of all traffic lanes. Figure 3 TOPL Network Modeling 41

54 Node 6 Node 1 Node 9 Node 8 Evaluation of SR-11 North / I-5 North Connector Dynamic Lane Management System The network that was developed for this project includes a total of 17 nodes and 32 links. 13 links were used to model the illustrated section of SR-11, 7 links the illustrated section of I-5, and 6 links the Connector between SR-11 North and I-5 North. Single links were used to model the various freeway on- and off-ramps. Only the ramps of interest to the evaluations were modeled. This includes the Hill Street on-ramp, the Stadium Way on-ramp, the Solano Avenue off- and on-ramps, and the Riverside Drive off- and on-ramps located within the Connector. The Hill Street off-ramp was not modeled. At the south end of SR-11, the four-level interchange was also not modeled since the modeling of this interchange would have required to extend the geographical scope of the modeling to some distance south of the interchange on SR-11, as well as both east and west on US-11 to capture traffic merging on SR-11 from US-11. Downstream of the Connector exit, no off- or on-ramps were modeled as traffic conditions on this part of the freeway generally do not affects operations near or upstream the Connector exit. Figure 31 illustrates in more details the modeling of the area surrounding the Connector exit on SR-11. In this case, multiple links were coded between pairs of nodes to provide a better modeling of lanespecific operations along SR-11. At the entrance of the connector, separate links were coded to allow Node 14 Node 13 Node 8 VDS Node 9 VDS Node 1 Node 6 VDS VDS 673 Link 29 Lane 1 Link 8 Lane 1 Link 2 - Lane 1 Link 5 Lanes 1-4 Link 7 Lanes 2-4 Link 28 Lanes 2-4 Link 16 - Lane 2 Link 9 - Lanes 3-4 Link 12 - Lanes 1-3 Link 1 Solano Off Link 11 Solano On ~43 ft ~8 ft ~92 ft Figure 31 TOPL Modeling of Connector Approach on SR-11 North 42

55 Node 19 Node 26 Node 18 Node 17 Evaluation of SR-11 North / I-5 North Connector Dynamic Lane Management System the modeling of the opening and closing of the shoulder lane. Immediately upstream of the Connector, traffic between Node 8 and Node 9 is split among three links to better reflect the queuing conditions that may develop near the exit. Link 2 models Lane 1 on SR-11, which exclusively carries traffic destined to the Connector. On the other side, Link 9 models Lanes 3 and 4, which are exclusively used by vehicles continuing on SR-11 past the Connector. In the middle, Link 16 models Lane 2, which carries vehicles both destined to the Connector and remaining on SR-11 past the exit. Upstream of Node 9, a dual link modeling was further adopted up to the Solano off-ramp again to allow a better representation of the queues that may develop along Lane 1 on SR-11 when only a single lane is open within the Connector. Similar to Figure 31, Figure 32 details the modeling of the merge between the Connector and I-5 North. In this case, the central element of the modeling approach was how to adequately replicate the bottleneck effect created by the lane-drop a short distance downstream from the merge. This lane drop forces vehicles coming from the Connector to quickly merge into a single lane or to force their way onto the I-5 mainline. As a result of these actions, traffic slows down both along the Connector and I-5, but with a more severe impact along the Connector. The analysis of traffic behavior at the merge led to the decision to code two links between Node 18 and Node 26 instead of a single link covering the 6 lanes that are available there. This dual-link coding allowed defining a lower capacity for the two lanes carrying the Connector traffic, to better reflect the operational frictions caused by the lane-changing activities from the traffic entering I-5 from the Connector. VDS Link 19 Lanes 5-6 Link 26 Lanes 1-2 Link 22 Lanes 1-5 Link 25 Lanes 1-5 Link 27 Lanes 1-4 Link 23 Lanes 1-4 VDS ft VDS Figure 32 TOPL Modeling of Connector Merge on I-5 43

56 DEMAND MODELING Within TOPL, traffic demand is modeled using two key elements: Flow rates on link entry points; and Flow split proportions at nodes where traffic may enter more than one downstream link. For the modeling of the study corridor, the following elements were thus required: Flow rates on first modeled SR-11 link (Link 6); Flow rates on first modeled I-5 link (Link 24); Flow splits at Solano Avenue off-ramp (Node 6); Flow splits at the Riverside Drive off-ramp within the Connector (Node 16); and Flow splits between SR-11 and Connector (Node 6, Node 9, and Node 8). For both SR-11 and I-5, equivalent hourly entry flow rates were coded for each successive 5-minute interval between 12: and 2:. These flow rates correspond to the network entry demands shown in Figure 17 that were extracted from PeMS data for each freeway and which were coded within the microscopic Paramics simulation model. Traffic splits for the Solano Avenue and Riverside Drive offramps were also develop to match the ramp demands that have been coded within Paramics. In this case, however, the splits were defined using one-hour intervals rather than 5-minute intervals since the source data only characterized traffic flows on an hourly basis. Given the relatively low level of exiting traffic using each ramp, this change in modeling scale was not expected to significantly affect the simulation evaluations, if at all. The modeling of the traffic demand for the Connector was a more complex endeavor due to the need to define link assignments upstream of the Connector reflecting specific lane usages. In this case, available PeMS data and video recordings were used to define a series of split rates for Node 6, Node 9, and Node 1 that would allow TOPL to replicate observed lane flow rates on SR-11 upstream of the Connector exit. Figure 33 illustrates the modeling that was developed. The following describes in more details the various aspects of the modeling shown in the figure: Node 6 Between.8% and 2.5% of the traffic entering the node is modeled to exit SR-11 via the Solano Avenue off-ramp. All traffic expected to enter the Connector using Lane 1 (Link 13) is assigned here to Link 29, which models the leftmost lane along SR-11. On the other hand, all traffic expected to enter the Connector using the shoulder lane (Link 3) is assigned to Link 7. All traffic expected to remain on SR-11 past the Connector is also assigned to Link 7. Node 1 All traffic entering the node from Link 29 (Lane 1) is assumed to continue onto Link 8 (Lane 1), while all traffic entering from Link 7 (Lanes 2-4) is assumed to continue onto Link 28 (Lanes 2-4). All traffic entering the freeway from Solano Avenue is further assumed to enter Link 28. Node 9 All traffic entering the node from Link 8 (Lane 1) is assumed to continue onto Link 2 (Lane 1). All traffic expected to enter the Connector entering Node 9 from Link 28 is assigned here to Link 16 (Lane 2), while all traffic remaining on SR-11 past the Connector is split between Link 16 (Lane 2) and Link 9 (Lane 3) to match observed lane flow rates at VDS Node 8 All traffic on Link 2 (Lane 1) is assumed here to continue onto Link 13 (Lane 1 on Connector). Similarly, all traffic on Link 9 (Lanes 3-4) is assumed to continue onto Link 12 (SR- 11 link downstream of Connector exit). Traffic on Link 16 (Lane 2) is split between Link 13 44

57 (Connector Lane 1), Link 3 (Connector shoulder lane) and Link 12 (SR-11) depending on the situation. All traffic expected to continue along SR-11 is assumed to continue onto Link 12. All traffic expected to enter the Connector is assigned to Link 3 when the shoulder lane is open, but to Link 13 when the shoulder lane is closed. No vehicle is assumed to go from Link 16 to Link 13 since this movement would not result in TOPL simulating the shockwaves caused by vehicles forcing their way onto Lane 1 just prior to the Connector exit. These vehicles would be directly transferred to Link 13 without affecting traffic Link 2. It was thus preferred to have all the Link 13 traffic to go through Link 2 at Node 9. Node 6 Link 29 Lane 1 Node 1 Link 8 Lane 1 Node 9 Link 2 - Lane 1 Node 8 Link 5 Lanes 1-4 Node 6 Link 7 Lanes 2-4 Node 1 Link 28 Lanes 2-4 Node 9 Link 16 - Lane 2 Link 9 - Lanes 3-4 Node 8 Link 12 - Lanes 1-3 Link 1 Solano Off-Ramp Link 11 Solano On-Ramp Node 6 Period Links 5 29 Links 5 7 Links :-13: :-14: :-15: :-16: :-17: :-18: :-19: :-2: :-21: :-22: Node 9 Node 8 Period Links 28 2 Links Links :-13: :-14: :-15: :-16: :-17: :-18: :-19: :-2: :-21: :-22: Period Links Links 16 3 Links :-13: :-14: :-15: :-16: :-17: :-18: :-19: :-2: :-21: :-22: Figure 33 TOPL Traffic Demand Modeling Time-of-Day System without Enforcement MODELING OF DYNAMIC LANE MANAGEMENT SYSTEM TOPL currently offers limited capabilities for modeling dynamic lane management systems. Systems that open or close a traffic lane on a time-of-day basis can be modeled by simply altering the split ratios at nodes affected by an operational change in traffic behavior. For the SR-11 corridor, this essentially meant altering the splits ratios at the nodes along SR-11 upstream of the Connector exit to define how traffic uses available lanes along SR-11 when the shoulder lane is open or closed. However, available built-in functionalities within TOPL at the time of this project did not allow modeling a system dynamically opening and closing the Connector shoulder lane based on measured traffic conditions. Doing so would require the ability to develop custom control modules using an Application Programming Interface (API), which was not available. While there were plans to develop such an interface, it was still unclear when such an interface would be made available to model users. 45

58 Node 6 Link 29 Lane 1 Node 1 Link 8 Lane 1 Node 9 Link 2 - Lane 1 Node 8 Node 6 Node 6 Node 1 Node 9 Node 8 Node 6 Node 1 Node 9 Node 8 Link 5 Lanes 1-4 Link 7 Lanes 2-4 Node 1 Link 28 Lanes 2-4 Node 9 Link 16 - Lane 2 Link 9 - Lanes 3-4 Node 8 Link 12 - Lanes 1-3 Link 1 Solano Off-Ramp Link 11 Solano On-Ramp Node 6 Period Links 5 29 Links 5 7 Links :-13: :-14: :-15: :-16: :-17: :-18: :-19: :-2: :-21: :-22: Node 9 Node 8 Period Links 28 2 Links Links :-13: :-14: :-15: :-16: :-17: :-18: :-19: :-2: :-21: :-22: Period Links Links 16 3 Links :-13: :-14: :-15: :-16: :-17: :-18: :-19: :-2: :-21: :-22: No vehicles using Link 3 before 3 PM and after 7 PM All Link 16 -> Link 3 traffic assigned to Link 13 before 3 PM and after 7 PM Figure 34 TOPL Traffic Demand Modeling Time-of-Day System with Enforcement Link 29 Lane 1 Link 8 Lane 1 Link 2 - Lane 1 Link 5 Lanes 1-4 Link 7 Lanes 2-4 Link 28 Lanes 2-4 Link 16 - Lane 2 Link 9 - Lanes 3-4 Link 12 - Lanes 1-3 Link 1 Solano Off-Ramp Link 11 Solano On-Ramp Node 6 Period Links 5 29 Links 5 7 Links :-13: :-14: :-15: :-16: :-17: :-18: :-19: :-2: :-21: :-22: Node 9 Node 8 Period Links 28 2 Links Links :-13: :-14: :-15: :-16: :-17: :-18: :-19: :-2: :-21: :-22: Period Links Links 16 3 Links :-13: :-14: :-15: :-16: :-17: :-18: :-19: :-2: :-21: :-22: Traffic on Links 29, 8 and 2 adjusted before 3 PM and after 7 PM SR-11 Lane 1 traffic assumed to be 156 vph on Links 29 and Link 8, and 162 vph on Link 2 Remainder of Connector traffic assigned to Link 16 Figure 35 TOPL Traffic Demand Modeling Shoulder Lane Always Open 46

59 Node 6 Link 29 Lane 1 Node 1 Link 8 Lane 1 Node 9 Link 2 - Lane 1 Node 8 Node 6 Node 6 Node 1 Node 9 Node 8 Node 6 Node 1 Node 9 Node 8 Link 5 Lanes 1-4 Link 7 Lanes 2-4 Node 1 Link 28 Lanes 2-4 Node 9 Link 16 - Lane 2 Link 9 - Lanes 3-4 Node 8 Link 12 - Lanes 1-3 Link 1 Solano Off-Ramp Link 11 Solano On-Ramp Node 6 Node 9 Node 8 Period Links 5 29 Links 5 7 Links :-13: :-14: :-15: :-16: :-17: :-18: :-19: :-2: :-21: :-22: Period Links 28 2 Links Links :-13: :-14: :-15: :-16: :-17: :-18: :-19: :-2: :-21: :-22: Period Links Traffic flow adjustments between 3-7 PM Link 29 flow increased to 172 vph Link 2 nad Link 13 flow assumed to carry a maximum of 19 vph Remainder of Connector traffic assumed to be non-compliant and assigned to Link 16 / Link 13 Links 16 3 Links :-13: :-14: :-15: :-16: :-17: :-18: :-19: :-2: :-21: :-22: Figure 36 TOPL Traffic Demand Modeling Shoulder Lane Always Closed without Enforcement Shoulder Lane Always Closed / With Enforcement Link 29 Lane 1 Link 8 Lane 1 Link 2 - Lane 1 Link 5 Lanes 1-4 Link 7 Lanes 2-4 Link 28 Lanes 2-4 Link 16 - Lane 2 Link 9 - Lanes 3-4 Link 12 - Lanes 1-3 Link 1 Solano Off-Ramp Link 11 Solano On-Ramp Node 6 Period Links 5 29 Links 5 7 Links :-13: :-14: :-15: :-16: :-17: :-18: :-19: :-2: :-21: :-22: Node 9 Node 8 Period Links 28 2 Links Links :-13: :-14: :-15: :-16: :-17: :-18: :-19: :-2: :-21: :-22: Period Links Links 16 3 Links :-13: :-14: :-15: :-16: :-17: :-18: :-19: :-2: :-21: :-22: No vehicles on Link 3 all day Link 16 -> Link 3 traffic assigned to Link 5 -> Link 29, Link 28 -> Link 2, and Link 16 - > Link 13 using a 1%/1%/8% split Figure 37 TOPL Traffic Demand Modeling Shoulder Lane Always Closed with Enforcement 47

60 Figure 33 presented earlier in Section 7.2, as well as Figure 34 to Figure 37, illustrate the various system operations that could be modeled in TOPL with the software s current capabilities. These alternatives include: Baseline scenario: Current time-of-day system without enforcement (Figure 33) This scenario distributes traffic across Links 7, 8, 9, 13, 16, 2, 28, 29, 3 to reflect the traffic conditions that were observed on the approach to the Connector and its entrance from the collected PeMS and video data. Current time-of-day system with enforcement (Figure 34) This scenario redirects to Link 13 all the Link 16 traffic headed to the Connector before 15: and after 19:. No change is made to the traffic splits between 15: and 19: when compared to the baseline scenario. Permanent opening of shoulder lane (Figure 35) This scenario adjusts the traffic flows on Links 7, 29, 8, 28, 2, 16, 9, 13 and 3 to reflect the fact that motorists have in this case always the choice of entering the Connector using Link 13 (Lane 1) or Link 3 (shoulder lane). Reflective of observed traffic behavior between 15: and 19: when the shoulder lane is open, it is assumed that Link 29 and Link 8, which models Lane 1 around the Solano interchange, carries a maximum of 156 vph, and that Link 2, which models Lane 1 immediately upstream of the Connector exits, carry a maximum of 162 vph. All connector traffic above these flow levels are then assigned to other traffic lanes, i.e., Link 7, Link 28 and Link 16. Permanent closure of shoulder lane without enforcement (Figure 36) Adjusts simulated traffic flows on Links 7, 29, 8, 28, 2, 16, 9, 13 and 3 between 15: and 19: to reflect that the shoulder lane is only used by vehicles non-complying to its operational rules. The proportion of non-complying vehicles is determined based on observed lane usage at the Connector entrance from the video recordings from CCTV Camera 194 prior to the opening of the shoulder lane. The TOPL modeling typically assumes that there is between 4% and 45% of all connector traffic that would illegally use the shoulder lane between 15: and 19: while it is closed. Permanent closure of shoulder lane with enforcement (Figure 37) Assumes that Link 3 is never available for use and that all Connector traffic must use Link 13. MODEL CALIBRATION This section summarizes activities that have been conducted to try to develop a calibrated TOPL model for the study area. Elements discussed in this section include: Calibration elements; Calibration targets; Estimation of jam density; Modeling on exit condition on I-5 North; Modeling of traffic conditions on I-5 North; Modeling of traffic conditions on Connector; Modeling of traffic conditions along SR-11; and Calibration results. 48

61 CALIBRATION ELEMENTS Calibration of the TOPL model generally consists in defining for each freeway link the shape of the fundamental diagram describing the traffic behavior on the link. Key elements to define include: Critical density (density at which the highest flow rate typically occurs); Jam density (density at which traffic would stop moving); Maximum flow rate; and Capacity drop following the onset of congestion. For each link, calibration of the fundamental flow diagram is typically accomplished by plotting a flowdensity diagram from available field data. In most cases, the shape of the scatter plot will readily identify the parameters that need to be calibrated. As an example, the diagram of Figure 38 illustrates the flow-density relationship for VDS on I-5 upstream of the Connector merge that can be developed from the flow and occupancy data collected by the station s traffic sensors. In this case, the maximum flow rate can easily be assessed to be around 19 vph. The critical density can be found where the line representing the free-flow speed intersects with the maximum flow line. While no direct observations were made of the jam density at this particular location, this parameter can generally be assessed using visual observations of the density at which vehicles accumulate in a queue. The capacity drop is a parameter that allows reflecting a drop in maximum rate at which vehicles can flow through following the onset of congestion. Such a drop, if it occurs, would result in a noticeable break or change in behavior in the flow-density diagram. Figure 38 TOPL Calibration Parameters As with any simulation model, a particular difficulty of the calibration effort is to account for variations in traffic conditions. As can be observed in the example of Figure 38, a perfect relationship between field data and TOPL s underlying modeling approach cannot be expected. A key goal of the calibration is therefore to identify within the available field data the most appropriate trends that will allow setting adequate values to the various calibration parameters. 49

62 CALIBRATION TARGETS The main target of calibration is to have TOPL reasonably replicating observed traffic conditions within the modeled network. To achieve this goal, calibration targets similar to those listed in Table 4 in Section can be applied. These targets assess the quality with which a simulation model replicates observed flow rates on individual links, travel times within a networks, observed speeds on individual links, and bottlenecks contributing to congestion and delays. However, as will be explained later, a full calibration of the TOPL model could not be achieved due to some modeling limitations. Consequently, the adopted calibration targets were eventually not applied. ESTIMATION OF JAM DENSITY The available PeMS data did not provide direct measurements of the jam density along any modeled link. However, the video recordings from CCTV Camera 194 allowed observing several queues of stopped vehicles building up upstream of the Connector exit on SR-11. A number of these queues notably extended across the entire length of the last tunnel on SR-11. Knowing the length of the tunnel and the number of vehicles in the queue it was possible to obtain some measurements of the jam density along SR-11. After analyzing a few queuing events, an average jam density of 16 vehicles per mile per lane was estimated. Since there are little variations in traffic composition between SR-11 and I-5 during peak traffic periods, it was then assumed that the 16 vehicles per mile per lane jam density value could be applied to all links being modeled in TOPL. CALIBRATION OF I-5 NORTH LINKS The calibration of the TOPL model for the study corridor started with an attempt to calibrate traffic conditions along I-5 North. Considering the causes of congestion upstream and downstream of the Connector merge point, as well as congestion on the Connector itself, calibration of the modeled section of I-5 mainly concentrated on adjusting the parameters pertaining to Links 23, 27, 25 and 22. The two primary sources of information along the modeled section of I-5 were the PeMS stations located on each side the Connector merge point, i.e., VDS upstream of the merge on Link 23 and VDS downstream of the merge on Link 25. The speed, flow and occupancy data retrieved from these two stations can be considered very reliable as the health reports from each station indicated that nearly all of the data supplied were observed and not inputted. For the I-5 corridor, the objective was to reproduce as best as possible observed traffic speeds, link densities and queues developing along Link 25, Link 27 and Link 23. According to the PeMS data compiled in Figure 39, this meant replicating the congestion observed to develop on Link 23 and Link 25 during the afternoon peak. Based on the displayed September and October averages, the increase in flow density typically triggers a drop in speed on Link 23, upstream of the Connector merge, from about 6 mph to approximately 3 mph between 14:3 and 15:3. This speed drop is partly explained by an increase in flow rate along I-5 during the afternoon peak and partly by vehicles entering I-5 from the Connector and forcing their way onto the freeway mainline. The resulting congestion then persists until sometime between 19: and 2:, when free-flow conditions return. On Link 25, downstream of the Connector, the increase in density and resulting speed drop are somewhat less pronounced. 5

63 Speed (mph) Speed (mph) Density (veh/mi/lane) Density (veh/mi/lane) Flow Rate (vph/lane) Flow Rate (vph/lane) Evaluation of SR-11 North / I-5 North Connector Dynamic Lane Management System VDS Link VDS Link Time of Day (hours) Time of Day (hours) Sept 211 Weekday Average Oct. 211 Weekday Average Time of Day (hours) Time of Day (hours) Time of Day (hours) Time of Day (hours) Figure 39 Observed Speed, Flow and Density along I-5 It should be noted that speed and density transitions along the study corridor are not in reality as smooth as illustrated in Figure 39. On a given day, traffic conditions tend instead to change rather quickly. However, since the changes do not always happen at the same time each day, the averaging of data over multiple days tends to produce artificially smoothed transitions. Figure 4 illustrates the speed-flow and flow-density plots that were developed based on the September and October 211 data for the Link 23 and 25. Based on the plots, the free-flow speed was estimated to be 7 mph on Link 23 and 72 mph on Link 25. The data further suggests a capacity of 19 vph per lane for Link 23 and 25 vph per lane for Link 25. Based on the estimated free-flow speed and link capacities, critical densities of veh/mi and veh/mi were then respectively assessed for Links 23 and

64 Figure 4 TOPL Modeling Parameters for Link 23 and Link 25 along I-5 According to the GPS runs conducted on August 22, 211, congested conditions were expected to start developing on Link 19, at the end of the Connector, at around 16:3 and to last until at least 17:3 (there is no observation past that point). To replicate the observed congestion, the lane capacity assigned to Link 19 was in this case gradually decreased from 2 vph per lane until congestion was observed to start developing on the link around 16:3. This process resulted in assigning an initial average lane capacity of 138 vph per lane to Link 19. While initial simulations with the above capacity for Link 19 appeared to adequately replicate observed traffic conditions on Link 25 throughout the modeling period, this was not the case for Link 23. On this link, the simulated flows always remained below the link s capacity, thus preventing the traffic stream to reach the defined critical density and to move into a congested state. To correct this situation, the capacity of Link 27, located between Link 23 and Link 25, was gradually reduced to 16 vph per lane. This change led to congestion starting to develop on Link 23 at around 15:1 and lasting until around 19:3, accompanied by a speed drop to 3 mph, much akin to what is observed in reality. However, after decreasing the capacity of Link 27, the inflow into Link 25 decreased as well, resulting in an operational profile differing somewhat significantly than reality. To reproduce the traffic condition on Link 25 while maintaining the congestion on Link 23, variable exit capacities were provided for Link 22. Within TOPL, the definition of variable capacities is only possible for exit links. This is a feature offered to enable the modeling of factors external to the modeled area that may affect traffic conditions on the boundary links. Figure 41 illustrates the time-dependent capacities that were developed for Link 22 between 12: and 22: following an iterative adjustment process. In this case, capacities were allowed to change every 15-minute interval. 52

65 Link Capacity (vph) 11, 1, 9, 8, 7, 6, 5, 4, 3, 2, 1, Time of Day (hours) Figure 41 Defined Time-Dependent Link Capacities for Link 22 Figure 42 illustrates the final flow, density and speed profiles that were obtained for Link 23 and Link 25. As can be observed when comparing the data of Figure 42 to those of Figure 39, there appeared to be a reasonably good replication of observed traffic conditions along I-5 within TOPL. Figure 42 Final TOPL Simulation Results for I-5 Links 53

66 CALIBRATION OF CONNECTOR LINKS Calibrating traffic conditions on the Connector presented a different challenge since there were no direct observations of traffic conditions within the facility other than the few GPS travel time runs that were conducted on August 16, 211. These runs simply indicated that congestion along the Connector starts to develop at the merge point with I-5 at around 16: and that the queue that develops from the congestion does not typically grow to an extent where it would reach SR-11. On the day the observations were made, the queue only grew to reach the Riverside Drive off-ramp in the middle of the Connector. Based on the GPS travel time run data, free-flow speeds varying between 4 and 6 mph were assigned to the different links along the Connector. The only exception is for the Connector entry link, where a free-flow speed of 3 mph has been coded to reflect the presence of the sharp curve located on this link. After setting the free-flow speeds, a standard capacity of 2 vph per lane was assigned to all Connector links based on the video data from CCTV Camera 194. This led to the coding of a critical density of veh/mi for the Connector entry link with the sharp curve and densities varying between 4 and 44 veh/mi for links downstream of the Connector s entry. CALIBRATION OF SR-11 LINKS Calibration of traffic performance along the SR-11 link primarily relied on data provided by the four PeMS stations located along the freeway and video recordings from CCTV Camera 191 and CCTV Camera 194. Figure 43 presents the average flow, density and speed profiles that were developed for the freeway based on available data for September and October 211. The plot for VDS near the Hill Street on-ramp is not shown since all data from this station were inputted. Figure 44 further illustrates the plots that were developed for the lane normally used by vehicles seeking to take the Connector, i.e., leftmost lane along SR-11. Figure 45 presents the flow-density profiles that were developed from the links of Figure 43 and Figure 44, as well as the parameters thought to provide the best fit to the data illustrated in the plots. In this case a significant discordance can unfortunately be observed between the fitted parameters and the observed behavior along SR-11. While TOPL assumes a linear flow-density relationship until the onset of congestion, the field data suggest that such a relationship is not valid for the modeled section of SR- 11. For very low densities, the flow rate appears to normally increase linearly with increasing density. However, above a certain density, increases in traffic density are not matched by proportional increases in flow rate, resulting in a curved flow-density relationship. The plots of Figure 45 generally indicate that traffic conditions start to deteriorate before the critical density is reached, resulting in lower than expected observed average traffic speeds before the congestion state is reached. This behavior can be explained by the relatively constrained geometry of the corridor (narrow shoulders, pronounced curves), as well as the high proportion of lane changes in high density traffic made by vehicles attempting to position themselves in Lane 1 or Lane 2 ahead of the Connector exit. As a result of the observed differences between the field data and assumed speed-density relationship in TOPL, a close replication of observed traffic conditions along SR-11 within TOPL was not expected. An attempt to calibrate the model was nevertheless attempted. In this case, the key to the calibration was thought to be the capacity of Link 12, which models the section of SR-11 immediately downstream of the Connector exit. To reproduce the observed speed drops along the corridor, the capacity of the link was gradually reduced to 146 vph per lane. This resulted in a speed drop occurring around 17:, 54

67 Speed (mph) Speed (mph) Speed (mph) Density (veh/mi/lane) Density (veh/mi/lane) Density (veh/mi/lane) Flow Rate (vph/lane) Flow Rate (vph/lane) Flow Rate (vph/lane) Evaluation of SR-11 North / I-5 North Connector Dynamic Lane Management System much later than the approximate 15: start time showed in the field data. To corridor this divergence, attempts were then made to decrease the capacity of links further downstream, but these attempts typically led to excessive queuing and unacceptable low speeds along the corridor. Capacity adjustments were also attempted for Link 3 and Link 5, respectively located just upstream and downstream of the Stadium Way on-ramp, to try to replicate more closely observed local conditions. However, a problem similar to the one described above was encountered. Changes in capacity producing a speed drop at approximately the right time also generally resulted in excessive queuing and unacceptable speeds elsewhere. 2 VDS Lanes 1-4 Link 3 2 VDS Lanes 2-4 Link 7 2 VDS Lanes 2-4 Link Time of Day (hours) Time of Day (hours) Sept 211 Weekday Average Oct. 211 Weekday Average Time of Day (hours) Time of Day (hours) Time of Day (hours) Time of Day (hours) Time of Day (hours) Time of Day (hours) Time of Day (hours) Figure 43 Observed Speed, Flow and Density along SR-11 Mainline 55

68 VDS Lane 1 Link 29 VDS Lane 1 Link Flow Rate (vph/lane) Flow Rate (vph/lane) Density (veh/mi/lane) Time of Day (hours) Time of Day (hours) 6 5 Density (veh/mi/lane) Time of Day (hours) Time of Day (hours) 6 5 Speed (mph) Speed (mph) Time of Day (hours) Time of Day (hours) Figure 44 Observed Speed, Flow and Density along SR-11 Lane 1 near the Connector Exit 56

69 Figure 45 TOPL Modeling Parameters for SR-11 Links 57

70 Figure 46 Final TOPL Simulation Results for SR-11 Links Figure 46 finally presents the flow, density and speed profiles that were obtained for the best simulation setup that could be achieved after several iterations. As can be observed by comparing the data of Figure 46 to those of Figure 43 and Figure 44, significant differences remain between the simulated and actual traffic conditions along SR-11. In this case, the inability to obtain a close replication of observed conditions similar to what has been achieved for I-5 is directly attributed to TOPL s inability to closely replicate the flow-density relationships that exist along SR-11. It was therefore concluded that an acceptable calibration could not be achieved until revisions to the model could be made by its development team, and thus that further attempts at calibrating the model would be futile. This led to the decision to abandon the possibility of using the software to evaluate dynamic lane management options along the SR-11 corridor. 58

71 8. SIMULATION PERFORMANCE EVALUATIONS This section presents the results of the operational evaluations that were conducted with version of the Paramics microscopic traffic simulation software. While it was also initially intended to present simulation results obtained from the TOPL macroscopic simulation model, it was not possible to conduct evaluations with this model due to its current inability to adequately replicate traffic flow behavior along SR-11 (see Section7.4.6). While the TOPL network that was developed could have been used to produce simulation results, these results would have been meaningless. The analysis presented herein thus solely focuses on the simulation results obtained from Paramics. Elements presented in this section include: Description of evaluation scenarios considered; Definition of traffic movements for which performance measures are compiled; Assessment of impacts of current non-compliance behavior regarding the opening/closing of the Connector shoulder lane; Assessment of benefits provided by current time-of-day system with non-complying behavior; Assessment of potential benefits provided by a continuous shoulder lane opening; and Assessment of potential benefits provided by a system dynamically opening and closing the Connector shoulder lane based on observed traffic conditions along SR-11. EVALUATION SCENARIOS Figure 47 illustrates the scenarios that were considered for evaluating with Paramics the benefits provided by the current time-of-day dynamic lane management system as well as potential benefits that could be obtained by allowing the opening and closing of the Connector shoulder lane based on observed traffic conditions. The scenarios considered include more specifically the following: Scenario 1A Always Closed No Enforcement Scenario 1B Always Closed With Enforcement Scenario 2A Time-of-Day Control No Enforcement Scenario 2B Time-of-Day Control With Enforcement Scenario 3A Dynamic Control No Enforcement Scenario 3B Dynamic Control With Enforcement Scenario 4A Always Open No Enforcement Figure 47 Evaluation Scenarios 59

72 Scenario 1A: Continuous closing of shoulder lane, without enforcement Scenario assuming a permanent closure of the Connector shoulder lane, but with a proportion of vehicles illegally using the lane varying between 15% and 4% depending on the period simulated. Scenario 1B: Continuous closing of shoulder lane, with enforcement Scenario assuming that the Connector shoulder lane is always closed, with all vehicles respecting the rules regarding its utilization. Scenario 2A: Time-of-day system without enforcement (Current situation) Scenario simulating the current time-of-day opening and closing of the Connector shoulder lane, with a proportion of vehicles illegally using the lane when it is closed varying between 15% and 4% depending on the period being simulated. Scenario 2B: Time-of-day system with enforcement Scenario simulating the current time-ofday opening and closing of the Connector shoulder lane, but assuming that all vehicles follow the rules regarding the utilization of the lane. Scenario 3A: Dynamic system without enforcement Scenario dynamically opening and closing the Connector shoulder lane based on defined flow and speed thresholds, with the same assumed proportions of vehicles illegally using the lane as Scenario 1A and Scenario 2A. Scenario 3B: Dynamic system with enforcement Scenario dynamically opening and closing the Connector shoulder lane based on defined flow and speed thresholds and assuming that all vehicles follow the rules regarding the utilization of the lane. Scenario 4: Continuous opening of shoulder lane Scenario assuming that the Connector shoulder lane always remains open. In this case, the enforcement status is irrelevant. Scenario 1B, which considers a full closing of the Connector shoulder lane with full enforcement is shown in a dotted box in Figure 47 to illustrate the fact that meaningful simulation results could not be obtained from this particular case. Within this scenario, the permanent inability for vehicles to use the Connector shoulder lane leads to the development of extremely long queues along SR-11 that eventually spill across the network boundaries. At some point, the number of vehicles queued outside the modeled network causes the simulation to stop, thus preventing the compilation of any meaningful statistics. This scenario is therefore only illustrated for reference purposes. REFERENCE TRAFFIC MOVEMENTS To provide more detailed insights on how various dynamic lane management system setups may affect different users of the modeled SR-11/I-5 corridor, simulation results were typically compiled for the groups of vehicles described below and illustrated in Figure 48: SR-11 / Connector traffic Vehicles starting their journey at the southern end of the modeled section of SR-11 (Zone 1) or the Hill Street on-ramp (Zone 2) and terminating their journey at the western end of I-5 (Zone 8) after having traveled along the Connector. SR-11 only traffic Vehicles starting their journey at the southern end of SR-11 (Zone 1) or the Hill Street on-ramp (Zone 2) and exiting the network at the northern end of SR-11 (Zone 6). I-5 traffic Vehicles modeled to travel solely along I-5 (Zone 5 to Zone 8). Network traffic All vehicles traveling within the simulated network. 6

73 Zone 8 I-5 Exit Network Traffic: Vehicles traveling between all origindestination pairs SR-11 / Connector Traffic SR-11 Only Traffic Zone 6 SR-11 Exit I-5 Traffic Zone 5 I-5 Entry Zone 1 SR-11 Entry Zone 2 Hill Street Figure 48 Evaluation Traffic Groups IMPACTS OF NON-COMPLIANCE ON SYSTEM PERFORMANCE As indicated in Section 4.2, a significant proportion of motorists currently ignore the opening and closing of I-5 North Connector shoulder lane. PeMS and video data collected along SR-11 suggest that this behavior is the result of the traffic demand for the Connector exceeding the capacity of a single traffic lane at its entrance. Entry lanes on the Connector have a lower capacity than the SR-11 mainline due to the need for vehicles to negotiate a sharp 3-mph curve with low visibility. Based on the data of Figure 11 in Section 4.2, the effective capacity of each traffic lane at the Connector entrance can be assumed to be 2 vph approximately. The link between capacity and non-authorized use of the Connector shoulder lane was also suggested by the collected data. As was shown in Figure 12 in Section 4.3, the proportion of vehicles illegally using the shoulder lane when it is closed was observed to trend higher with the portion of volume on the Connector exceeding 2 vph. To quantify the impacts of the observed non-compliance on system performance, the delays incurred by traffic under Scenario 2B were compared to those incurred under Scenario 2A. The first scenario implements the current time-of-day lane management system but with full enforcement of the shoulder utilization rules, while Scenario 2A simply replicates the current operational conditions. Figure 49 compiles the 6-mph delays that were simulated to be incurred by vehicles under both scenarios. The illustrated statistics show the total delay incurred within successive 5-minute intervals and are for an average of 2 simulation runs. As can be expected, the non-complying behavior leads to a significant reduction in incurred delays along the corridor. As indicated by the large gap between the two illustrated curves in Figure 49, most of the delay reductions are obtained before 15: and after 19:, when the Connector shoulder lane is officially closed. During these two periods, the unauthorized utilization of the shoulder lane by a certain proportion of vehicles produces an effective capacity increase at the entrance of the Connector. This capacity increase allows better servicing the traffic demand for the Connector and, as a consequence, reducing the queues that would otherwise develop on Lane 1 along SR-11 upstream of the Connector and by cross-influence on the other lanes. 61

74 6-mph Delay (vehicle-hours) Scenario 2A Time of Day (15:-19:) Non-Complying Scenario 2B Time of Day (15:-19:) Full Enforcement Time of Day (hours) Figure 49 Incurred Delays for Time-of-Day Scenarios with and without Enforcement The data of Figure 49 further indicate a decreasing trend over time in incurred delays between 15: and 19: under the full enforcement scenario, when traffic demand for the Connector is actually growing. This is explained by the gradual dissipation of the queues that would normally build up on SR- 11 upstream of the Connector before the shoulder lane is opened. Because significant queues do not develop, the delays incurred under the non-complying scenario tend instead to increase with the gradual increase in traffic demand for the Connector that occurs between 15: and 19:. Figure 5 provides a more detailed look at the delays that are incurred by various traffic groups within the simulated network under alternative control scenarios. The figure compares the total delays (delay estimated against free-flow speed), 6-mph delays, 35-mph delays and stopped delays that have been incurred by vehicles traveling along the various paths shown in Figure 48, including statistics for the overall network traffic. The illustrated statistics are the cumulative delays incurred by each group of vehicles between 13: and 22: averaged over 2 simulation runs. Below is a summary of the main observations that can be made from the simulation results: SR-11/Connector traffic For the SR-11 traffic taking the I-5 North Connector, the total incurred delay is reduced by 78% while the 6-mph delay is reduced by 79% and 35-mph-delay reduced by 89%. SR-11 only traffic Vehicles traveling exclusively along SR-11 also benefit from the improved operational conditions brought by the non-complying behavior at the Connector entrance. For these vehicles, the total delay under the non-complying scenario is reduced by 7%, the 6-mph delay by 74%, and the 35-mph by 91%. I-5 traffic The simulation results show no significant impacts for the I-5 traffic. While the noncomplying behavior reduces congestion on SR-11 and increase the length of queues developing on the Connector at the merge with I-5, these changes do not significantly alter the duration of the period during which queuing at the merge affects traffic on I-5. Overall network performance Overall, non-complying behavior at the Connector entrance is assessed to contribute to a 67% reduction in total delay across the simulated network, a 7% reduction in 6-mph delay, and an 85% reduction in 35-mph delay. 62

75 4, 3,5 SR-11 / Connector SR-11 Only I-5 Traffic Network -67% -7% Delay (vehicle-hours) 3, 2,5 2, 1,5 1, -78% -79% -89% -7% -74% -85% 5-97% -91% -95% +1% +1% -3% -16% -96% Total Delay 6-mph Delay 35-mph Delay Stop Delay Total Delay 6-mph Delay 35-mph Delay Stop Delay Total Delay 6-mph Delay 35-mph Delay Stop Delay Total Delay 6-mph Delay 35-mph Delay Stop Delay Scenario 2B - Time of Day (15:-19:) - Fully Enforced Scenario 2A - Time-of-Day (15:-19:) - Non Complying Figure 5 Traffic Stream Delays for Current Time-of-Day Scenarios with and without Enforcement Figure 51 and Figure 52 present an operational look at corridor performance under Scenarios 2A and 2B. Figure 51 presents the SR-11, Connector and I-5 speed profiles that were extracted from the simulations, while Figure 52 highlights the differences between the two profiles. The following are observations that can be made from the data presented in both figures include: The current non-complying behavior leads to speed improvements along SR-11 throughout the entire simulation period. Before 15: and after 19:, when the shoulder lane is closed, speed increases along SR-11 are attributable to the effective capacity increase associated with the non-authorized use of the lane. Between 15: and 19:, when the shoulder lane is open, the operational benefits stem from the absence of a need to service long queues of vehicles that would otherwise develop along SR-11 with a closed shoulder lane. As expected, unauthorized use of the Connector shoulder lane results in a slight degradation of traffic performance along the Connector before 15: and after19: due to the increase in flow rate on the Connector and longer queues developing at the I-5 merge point. Unauthorized use of the shoulder lane is further observed to improve traffic operations on the Connector itself between 15: and 19:, when the lane is normally open. This is again due to the absence of a need to service a surge in traffic that would result from queues developing along SR-11 before 15: when a single lane is open and used. Traffic along I-5 is only slightly affected by the non-complying behavior. The increase in flow rate along the Connector results in slightly slower speeds on I-5 upstream of the merge with the Connector before 15: and after 19:. However, shortly after 15:, improved operational conditions are observed along I-5 under the non-complying scenario when compared to the fully enforced scenario due again to the absence of a surge in traffic along the Connector following the opening of the shoulder lane. 63

76 SR-11 North Connector I-5 North Scenario 2B Scenario 2A Scenario 2B Scenario 2A Scenario 2B Scenario 2A TOD, Enforced TOD, Non Compliant Enforced TOD, Non Compliant Enforced Non-Compliant PeMS 1 (71846) (Hill Street On-Ramp) PeMS 2 (772513) (Stadium Way On-Ramp) PeMS 3 (771667) (Solano Avenue Ramps) PeMS 4 (771673) (Upstream Connector) PeMS 1 (71846) (Hill Street On-Ramp) PeMS 2 (772513) (Stadium Way On-Ramp) PeMS 3 (771667) (Solano Avenue Ramps) PeMS 4 (771673) (Upstream Connector) Virtual Station 1 (Entry Curve) Virtual Station 2 (31 ft from I-5 Merge) Virtual Station 3 (237 ft from I-5 Merge) 13: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : Virtual Station 4 (Riverside Ramps) Virtual Station 5 (55 ft from I-5 Merge) Virtual Station 1 (Entry Curve) Virtual Station 2 (31 ft from I-5 Merge) Virtual Station 3 (237 ft from I-5 Merge) Virtual Station 4 (Riverside Ramps) Virtual Station 5 (55 ft from I-5 Merge) PeMS 5 (716951) (Upstream Connector) PeMS 6 (728371) (Downstream Connector) PeMS 5 (716951) (Upstream Connector) PeMS 6 (728371) (Downstream Connector) Figure 51 Speed Profiles for Current Time-of-Day System with and without Enforcement 64

77 SR-11 North Connector I-5 North PeMS 1 (71846) (Hill Street On-Ramp) PeMS 2 (772513) (Stadium Way On-Ramp) PeMS 3 (771667) (Solano Avenue Ramps) PeMS 4 (771673) (Upstream Connector) Virtual Station 1 (Entry Curve) Virtual Station 2 (31 ft from I-5 Merge) Virtual Station 3 (237 ft from I-5 Merge) Virtual Station 4 (Riverside Ramps) Virtual Station 5 (55 ft from I-5 Merge) PeMS 5 (716951) (Upstream Connector) PeMS 6 (728371) (Downstream Connector) 13: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : Figure 52 Speed Differentials between Current Time-of-Day System with and without Enforcement 65

78 BENEFITS OF CURRENT TIME-OF-DAY SYSTEM To assess the benefits provided by the current time-of-day system, comparisons were made between Scenarios 1A, Scenario 2A and Scenario 4A. Similar to the current situation, all scenarios assume that a proportion of the Connector traffic disregards the shoulder lane closure, with a non-compliance proportion varying based on the traffic demand for the Connector. The first scenario implements a permanent closure of the shoulder lane, while the second scenario replicates current opening and closing on a time-of-day basis and the third scenario implements a permanent opening. Figure 53 illustrates the 6-mph incurred delays that were estimated by Paramics for all vehicles traveling within the network in successive 5-minute intervals between 13: and 22: for all three scenarios. Similar to other evaluations, the statistics presented are averages based on 2 simulation runs. 6-mph Delay (vehicle-hours) Time of Day Scenario 1A - Always Closed - Non Compliant Scenario 2A - Time-of-Day - Non Compliant Scenario 4A - Always Open Figure 53 Delays for Non-Complying Scenarios with Full Closure, Time-of-Day Operation and Full Opening As expected, the current time-of-day system is found to provide noticeable delay reductions along the corridor during the peak period. Peak reductions are observed between 17: and 19:, when the delays incurred with the current time-of-day system are nearly 2% below those that would be incurred under a permanent lane closure. As expected, only marginal changes in incurred delays are observed before 15: and after 19: between the full closure and current time-of-day operations as both scenarios feature identical lane operations and driver behavior over these two periods. Figure 54 further quantifies the benefits associated with specific traffic streams. The data shown in the figure indicate that the current time-of-day system provide a 3% reductions in 6-mph delay for the SR- 11 traffic using the Connector, a 14% reduction for the SR-11 only traffic, a 1% reduction for the I-5 traffic, and an overall 6-mph delay reduction of 7% for all simulated vehicles. Both the simulated impacts for the SR-11 traffic (14% reduction) and overall traffic (7% reduction) are statistically significant at the 95% confidence level based on the variability of the simulation results across 2 runs. 66

79 However, the simulated delay reductions for the SR-11 traffic taking the Connector and I-5 traffic were found to be not statistically significant, meaning that there is a good probability that the observed changes are not due to the opening of the Connector shoulder lane. Delay (vehicle-hours) 1,6 1,4 1,2 1, Total Delay SR-11 / Connector SR-11 Only I-5 Traffic Network Changes: 1A to 2A 2A to 4A -3% -9% -3% -1% 6-mph Delay -3% -18% 35-mph Delay +34% -3% Stop Delay -12% -2% Total Delay -14% -3% 6-mph Delay -41% -9% 35-mph Delay +6% -1% Stop Delay -1% -1% Total Delay -1% -1% 6-mph Delay -2% -1% +11% -7% 35-mph Delay Stop Delay Scenario 1A - Always Closed - Non Complying Scenario 2A - Time-of-Day (15:-19:) - Non Complying Scenario 4A - Always Open -6% -5% Total Delay -7% -5% 6-mph Delay -14% -12% 35-mph Delay -52% -33% Stop Delay Figure 54 Delays for Non-Complying Scenarios with Full Lane Closure, Current Time-of-Day Operation, and Full Lane Opening As indicated in the speed profiles of Figure 55 and speed differentials of Figure 56, the opening of the Connector shoulder lane between 15: and 19: is further found to improve speeds along SR-11, particularly upstream of the Solano Avenue interchange. The speed improvements at around the Stadium Way and Hill Street on-ramps are a strong indication that the opening of the Connector shoulder lane allows to reduce the length of queues developing along SR-11 as the demand for the Connector builds up, despite the fact that both scenarios assume that a certain proportion of traffic still use the Connector shoulder lane when it is closed. The simulation results also indicate notable speed improvements near the merge point with I-5 between 15: and 17:15. This result is somewhat counterintuitive as it would normally be assumed that the ability to send more vehicles along the Connector with two lanes open should negatively impact traffic conditions at the I-5 merge, particularly when considering the bottleneck created by the lane drop a short distance downstream of the merge. The improved performance appears to be mainly the result of better simulated operations at the Connector merge in the first 45 minutes following the opening of the shoulder lane. This situation may be the result of the underlying assumptions regarding the number of vehicles illegally using the Connector shoulder lane when it is closed. It may also have been influenced by Paramics internal simulation logic. While the improvement is unexpected, it can be noted that it gradually disappears, and even becomes negative, as time passes. This is consistent with a system able to send more traffic towards the merge. As was indicated above, the simulation results indicate an overall insignificant impact from the opening the shoulder lane between 15: and 19: for the Connector traffic when a significant proportion of motorists do not following the lane closure rules. 67

80 SR-11 North Connector I-5 North Scenario 1A Scenario 2A Scenario 1A Scenario 2A Scenario 1A Scenario 2A Closed, Non Compliant TOD, Non Compliant Closed, Non Compliant TOD, Non Compliant Closed, NC TOD, NC PeMS 1 (71846) (Hill Street On-Ramp) PeMS 2 (772513) (Stadium Way On-Ramp) PeMS 3 (771667) (Solano Avenue Ramps) PeMS 4 (771673) (Upstream Connector) PeMS 1 (71846) (Hill Street On-Ramp) PeMS 2 (772513) (Stadium Way On-Ramp) PeMS 3 (771667) (Solano Avenue Ramps) PeMS 4 (771673) (Upstream Connector) Virtual Station 1 (Entry Curve) Virtual Station 2 (31 ft from I-5 Merge) Virtual Station 3 (237 ft from I-5 Merge) 13: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : Virtual Station 4 (Riverside Ramps) Virtual Station 5 (55 ft from I-5 Merge) Virtual Station 1 (Entry Curve) Virtual Station 2 (31 ft from I-5 Merge) Virtual Station 3 (237 ft from I-5 Merge) Virtual Station 4 (Riverside Ramps) Virtual Station 5 (55 ft from I-5 Merge) PeMS 5 (716951) (Upstream Connector) PeMS 6 (728371) (Downstream Connector) PeMS 5 (716951) (Upstream Connector) PeMS 6 (728371) (Downstream Connector) Figure 55 Speed Profiles: Full Closure vs. Current Time-of-Day, without Enforcement 68

81 SR-11 North Connector I-5 North PeMS 1 (71846) (Hill Street On-Ramp) PeMS 2 (772513) (Stadium Way On-Ramp) PeMS 3 (771667) (Solano Avenue Ramps) PeMS 4 (771673) (Upstream Connector) Virtual Station 1 (Entry Curve) Virtual Station 2 (31 ft from I-5 Merge) Virtual Station 3 (237 ft from I-5 Merge) Virtual Station 4 (Riverside Ramps) Virtual Station 5 (55 ft from I-5 Merge) PeMS 5 (716951) (Upstream Connector) PeMS 6 (728371) (Downstream Connector) 13: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : Figure 56 Speed Differentials: Full Closure vs. Current Time-of-Day System, without Enforcement 69

82 POTENTIAL BENEFITS F ROM PERMANENT SHOULDER LANE OPENING In addition to assessing current system benefits, the simulation results of Figure 53 and Figure 54 allow evaluating the benefits that could be obtained from a permanent opening of the Connector shoulder lane. Comparing the results from Scenario 4A against those from Scenario 2A indicates that additional benefits could be obtained by fully opening the Connector shoulder lane under the current noncomplying behavior. System-wide, an additional 5% reduction in 6-mph delay could be achieved. This result is based on an average of 2 simulation runs and is statistically significant at the 95% confidence level. When considering the impacts on specific traffic movements, continuously opening the shoulder lane would reduce 6-mph delays for the SR-11 traffic taking the Connector by nearly 1% and by an additional 3% for the SR-11 only traffic. For the Connector traffic, the assessed impacts are again valid at a 95% confidence level. For the SR-11 only traffic, the observed changed is also statistically valid, but at a reduced 9% confidence level. While the simulation results also indicate a marginal 1% reduction in 6-mph delay incurred by the I-5 traffic, this observed change is not statistically significant based on the variability of simulation runs across the 2 runs conducted. Larger delay reductions can be expected if a fully enforced system would be used as the reference since opening the shoulder lane would have in this case a much larger impacts on capacity than under the current non-complying behavior. However, as indicated in Section 4.3, enforcing the rules regarding the shoulder lane utilization is relatively difficult due to the current road geometry along SR-11 and the Connector. Both the freeway and Connector have very narrow shoulders. These narrow shoulders do not provide sufficient space for a patrol car to be parked on the side. Stopping non-complying vehicles to issue tickets would also be problematic, as this would imply temporarily blocking one traffic lane, which would in turn likely negatively affect traffic operations along the corridor. POTENTIAL BENEFITS F ROM DYNAMIC SHOULDER LANE OPENING/CLOSING Figure 57 presents the range of flow and speed thresholds that were tested to evaluate the operation of a lane management system dynamically opening and closing the Connector shoulder lane based on measured traffic conditions around the Connector entrance. These thresholds are associated to the control rules used to determine when to open and close the shoulder lane outlined in Section 6.6. The specific flow and speed thresholds that were considered in the experiment include all possible combinations of the following two sets of flow and speed values: Flow thresholds: 16 vph, 17 vph, 18 vph, 19 vph and 2 vph. Speed thresholds: 35 mph, 4 mph and 45 mph. As outlined in Section 6.6, the shoulder lane is opened and closed based on the following rules: Open the shoulder lane if the observed flow rate at VDS has exceeded the defined flow threshold in at least 9 of the last 1 one-minute intervals OR if the average traffic speed at VDS has been below the set speed threshold in at least 9 of the last 1 one-minute intervals. Close the lane if the observed flow rate at VDS has remained below the defined threshold in at least 9 of the last 1 one-minute intervals AND the average traffic speed at this location has remained above the threshold in at least 9 of the last 1 one-minute intervals. To avoid opening or closing the shoulder lane too frequently, no change is allowed for 15 minutes following the opening or closing of the lane. 7

83 6-mph Delay (veh-hours) Evaluation of SR-11 North / I-5 North Connector Dynamic Lane Management System Figure 57 Range of Tested Flow and Speed Threshold for Dynamic Control Figure 58 illustrates the 6-mph delays that were assessed for the various combinations of flow and speed thresholds simulated. Similar to other simulation results, each data point represents an average of 2 simulation runs. Figure 59 and Figure 6 further present for selected combinations of flow and speed thresholds the specific time intervals during which the Connector shoulder lane was open or closed across the 2 simulation runs execute. Figure 59 compares results for scenarios varying the flow threshold between 16 vph and 2 vph under a fixed 45 mph threshold speed, while Figure 6 compares results for scenario varying the speed threshold between 35 mph and 45 mph for a fixed 18 vph flow threshold Non-Complying Full Enforcement Time-of-day delays: veh-hrs Full closure delay: Not assessed due to excessive congestion Permanent Closure Time of Day (15:-19:) Permanent Opening 35 mph Threshold 4 mph Threshold 45 mph Threshold Flow Threshold (vph) Figure 58 Performance of Dynamic Opening/Closing Alternatives 71

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