Corpus Christi Metropolitan Planning Organization Regional Travel Speed Study FINAL REPORT

Transcription

1 Corpus Christi Metropolitan Planning Organization 2010 Regional Travel Speed Study FINAL REPORT Prepared for: Corpus Christi Metropolitan Planning Organization 5151 Flynn Parkway, Suite 404 Corpus Christi, TX Prepared by: 7950 Elmbrook Drive Dallas, Texas TBPE Firm No March 17, 2011 Project Number WFXK2600

2 "The preparation of this report has been financed in part through grant[s] from the Federal Highway Administration and Federal Transit Administration, U.S. Department of Transportation, under the State Planning and Research Program, Section 505 [or Metropolitan Planning Program, Section 104(f)] of Title 23, U.S. Code. The contents of this report do not necessarily reflect the official views or policy of the U.S. Department of Transportation." i

3 Table of Contents Page No. 1. Executive Summary Introduction Methodology Mapping and Travel Time Data Pavement Roughness The CCMPO Linear Referencing System Evaluation of Congestion Travel Speeds Congestion Index Roadway Pavement Assessment Conclusions ii

4 List of Tables Page No. Table E1 Top 20 Worst Segments By Congestion Index in Table 1 Congestion Index Criteria... 8 Table 2 Summary of Congestion Index for Table 3 Top 10 Segments With Largest Drop in Performance Relative to Table 4 Top 10 Segments With Largest Improvement in Performance Relative to Table 5 Top 20 Worst Segments By Congestion Index in Table 6 Total Study Run Miles by Area Type and Facility Type Table 7 Total Congested Miles by Area Type and Facility Type Table 8 Worst 5% of Route segments for Pavement Roughness List of Figures Page No. Figure 1 Fall 2010 Study Limits... 5 Figure 2 Intersections Controls Figure 3 Posted Speed Limits Figure 4 School Zones Figure 5 Number of Lanes Figure 6 Median Type Figure 7 Bike Lanes Figure 8 Construction Zones Figure 9 Jurisdictions Figure 10 Model Area Type Figure 11 Model Facility Type Figure 12 AM Average Speed Figure 13 Mid-day Average Speed Figure 14 PM Average Speed Figure 15 AM Congestion Index Figure 16 Mid-day Congestion Index Figure 17 PM Congestion Index Figure 18 AM Percent Change In Congestion Index From Figure 19 PM Percent Change In Congestion Index From Figure 20 Congestion Index during AM on 0.1 Mile Segments Figure 21 Congestion Index during PM on 0.1 Mile Segments Figure 22 Pavement Roughness Figure 23 Example Pavement Roughness Profiles iii

5 1. Executive Summary It is necessary for the Corpus Christi Metropolitan Planning Organizations (CCMPO) to maintain an accurate, up to date regional transportation model in order to conform to State and Federal regulations for air quality and transportation projects. The MPO updates and calibrates its model using current information on the roadway network, area development, and other relevant characteristics such as travel time and speed data. The primary purpose of the 2010 Travel Speed Study is to evaluate the transportation system and prepare a report as part of the Congestion Management Process (CMP) in compliance with the SAFETEA-LU requirements. The secondary purpose of the study was to identify trends in congestion and travel time in order to identify problem locations for possible improvements. This year, the relative pavement roughness was also evaluated to identify the pavement segments with the worst roughness within the MPO boundaries. A congestion mitigation plan will be developed in the next phase of this study after review of the congestion results by CCMPO. The 2010 Travel Speed Study was conducted to update the conditions found in performing the 2006 study with enhancements to the data collection, data management, and analytical methods. The roadways were mapped to establish centerlines and record relevant roadway features. Features located in the mapping process included: speed limits, school zones, lanes, active construction zones, bike lanes, median type and intersection control. Travel speed data was collected during the months of August, September and October 2010 on Tuesdays, Wednesdays, and Thursdays, during the morning, Mid-day and afternoon peak period as follows: Morning Peak Period: 7:00 AM to 9:00 AM 3 runs in each direction Mid-day Peak Period: 11:00 AM to 1:00 PM 2 runs in each direction Afternoon Peak Period: 4:00 PM to 6:00 PM 3 runs in each direction Travel time runs were conducted using the floating car method. Roadways included arterials and freeways. Intersection delay for through vehicles was recorded at signalized intersections and compared with criteria in the Highway Capacity Manual (HCM) to determine level of service. In order to differentiate between congested roadways and roadways with low speed limits, a method for illustrating the data was continued in the 2010 Travel Speed Study, consistent with the 2006 and 2003 studies. This method uses a ratio of actual travel speed to posted speed limit and is referred to as the Congestion Index (CI). Of the 660 directional miles studied in AM and PM, it was observed that 24% were congested in AM, and 26% were congested in PM. Of the 130 directional miles studied in the Mid-day peak, it was observed that 46% were congested. In 2006, approximately 22% of the roadway miles studied were congested in AM and 20% were 1

6 congested in PM. Of the segments that were common to both 2006 and 2010 studies, 535 segments (359 directional miles) worsened in Congestion Index and 435 segments (238 directional miles) experienced an improvement in Congestion Index in the AM peak period in In the PM peak period, 601 segments (416 directional miles) worsened in the Congestion Index while 382 segments (190 directional miles) improved in The table below shows the Top 20 congested segments in this study based on CI. 2

7 Rank RouteID Route Name Table E1 Top 20 Worst Segments By Congestion Index in 2010 Intersection Segment Peak Period 2010 Congestion Index 2010 Average Speed (mph) 2010 Weighted Average Speed Limit (mph) STAPLES - SB/WB MCARDLE TO SPID WBFR PM EVERHART - SB MCARDLE TO SPID WBFR PM RODD FIELD - NB WILLIAMS TO SPID EBFR AM STAPLES - SB/WB SHOPPING WAY TO MCARDLE PM EVERHART - SB SPID EBFR TO CORONA PM STAPLES - EB/NB HEB TO SPID EBFR PM GOLLIHAR - WB AYERS TO 286 NB AM SPID FRONTAGE - EB STAPLES TO AIRLINE PM AYERS - NB BROWNLEE TO ALAMEDA/STAPLES MD GREENWOOD - SB TROJAN TO SPID WBFR PM STAPLES - EB/NB FIRESTATION TO HOLLY AM WALDRON - NB COMPTON/KNICKERBOXER TO SPID EBFR MD AYERS - NB KOSAR TO BALDWIN AM HOLLY - EB SH 286 NB TO AYERS PM AIRLINE - SB MCARDLE TO SPID WBFR PM LEOPARD - EB LANTANA TO SPID EBFR PM HORNE - EB PORT TO AYERS MD SPID FRONTAGE - EB WEBER TO EVERHART MD CALLICOATE - SB REDRIVER TO LEOPARD PM STAPLES - EB/NB WILLIAMS TO HEB PM Note: Short Segments (< 500ft), segments containing school zones and active construction were not considered for this table 2006 Rank 3

8 2. Introduction It is necessary for Corpus Christi Metropolitan Planning Organization (CCMPO) to maintain an accurate, up to date regional transportation model in order to conform to State and Federal regulations for air quality and transportation projects. The MPO updates and calibrates its model using current information on the roadway network, area development, and other relevant characteristics. The MPO updates their travel time and speed data periodically. For this 2010 study, the MPO contracted with Jacobs Engineering Group Inc. (Jacobs) to collect roadway characteristics, field-measured travel time, and speed data for use in calibrating and validating the regional transportation model. The primary purpose of this year s 2010 Travel Speed Study was to expand on the 2006 effort to cover more of the network and to develop a trend for those previously included. Also, the Mid-day peak period way studied on select corridors identified by the MPO for the first time. Mapping and travel time runs were conducted on arterials and freeways. The breakdown of mileage by peak period is shown below: 330 centerline miles AM and PM peak periods 65 centerline miles of routes in the Mid-day peak period. The routes that were studied in 2010 are shown in Figure 1. Apart from this, the 2010 study also included an assessment of relative roughness of the pavements in the study network using the 3D accelerometer method developed by Jacobs. As part of the 2010 study, the roadway attributes that were collected in the field were Intersection Controls, Speed Limits, School Zones, number of lanes, median type, bike lanes, and construction zones. Travel time runs were performed on the selected routes using GPS equipment which recorded the position and speed of the vehicle every second. Digital video of peak conditions was also recorded as part of the floating car runs apart from 1-second GPS speed information. Through the methodology developed and the additional data assembled, the data collected in this study has a variety of additional uses. Because the information is all housed in the GIS system, queries can group data by area for use in individual planning processes. The database can be used for background information for signal timing projects, signing and pavement marking projects, school zone issues, and other transportation related projects. 4

9 Figure 1 Fall 2010 Study Limits 5

10 3. Methodology 3.1 Mapping and Travel Time Data The 2010 Travel Speed Study included enhancements to the data collection, data management, and analytical methods. First, the roadways were mapped to verify the system attributes that were collected in 2006 on routes included in both studies, document changes since 2006, establish centerlines for new routes and record relevant roadway features using the newly developed Jacobs LRS Assessment field software. This data was used to update the CCMPO Geographic Information System (GIS) database developed by Jacobs as part of the 2003 and 2006 studies. The Jacobs GIS utilizes a linear reference system (LRS) as the basis of all roadway and travel speed data. Features and data within an LRS use position along a route instead of an x,y coordinate system. The route features contain measures or distance along the route. After mapping all routes, the travel time runs were collected. The 2010 Study used both GPS equipment and digital video for a thorough analysis. The roadway segments were videotaped during the mapping runs in order to provide a reference of operational conditions for possible mitigation. The digital videos were later linked to the GIS results for future reference. This provides a video log of Digital Video Camera most primary roadways within Nueces and San Patricio County. The photograph on the right shows the typical setup for the field vehicle. The roadways were broken down into 1079 directional segments in GIS for analyzing the travel time data at a segment level. The segments are defined by controlled intersection locations, in general. This helps to better identify areas of localized congestion. GPS and PDA Video Encoder After the field mapping was completed, travel runs were conducted using the floating car method. The floating car method is described in detail in the Manual of Traffic Engineering Studies published by the Institute of Transportation Engineers. The test vehicle travels within the flow of traffic, passing as many vehicles as pass the test vehicle. In this way, the test vehicle is representing the average vehicle. During the travel time runs, the Haicom BT GPS equipment recorded position and time at one-second intervals into a Dell Personal Digital Assistant (PDA) using Bluetooth technology. The data is saved through a customized travel speed program developed by Jacobs. The driver of the test vehicle drove the speed limit if no other cars were present and at the school zone speed limit if a school zone speed limit was in effect at the time of the travel time run. 6

11 For the 2010 Travel Speed Study, travel run data was collected during the months of August, September and October The data was collected on Tuesdays, Wednesdays, and Thursdays, during the morning and afternoon peak periods on all the routes and during the Mid-day period on selected routes. The study time periods were as follows: Morning Peak Period: 7:00 AM to 9:00 AM 3 runs in each direction Mid-day Peak Period: 11:00 AM to 1:00 PM 2 runs in each direction Afternoon Peak Period: 4:00 PM to 6:00 PM 3 runs in each direction After the travel runs completed, the data was analyzed with the help of the LRS. The data analysis included various levels of review, including automated QA/QC, and manual QA/QC. The primary steps taken to process the large amounts of data are shown below. Assign segments for aggregation purposes intersection segment speed limit school zone speed (if applicable) Calculate travel time Calculate average Space Mean Speed and Time Mean Speed. Calculate frequency of stops within each segment Calculate the stop delay as the count of one second GPS points where the speed <=3 MPH Calculate the segment delay as the difference between travel time and free flow travel time Calculate free flow travel time Average by intersection segment Because data was recorded every one second using GPS equipment, the intersection approach delay calculation was made feasible for all signalized intersections within the study area. Delay calculations were provided for through vehicles only. No analyses were conducted for turning movements. The delay in seconds was then compared with the HCM criteria for level of service for approaches to signalized intersections. These criteria categorize vehicle delay into levels of service ranging from LOS A, meaning less than or equal to 10 seconds delay, to LOS F, meaning more than 80 seconds of delay. The intersections with poor levels of service (i.e. long delay) were marked in GIS. Summarizing the results purely by average speed may indicate slow speeds when in reality, traffic may be traveling according to a low posted speed limit. In order to differentiate between congested roadways and roadways with low speed limits, a method for illustrating the data that was introduced in 2003 and used in 2006 was once again used in the 2010 Travel Speed Study. This method uses a ratio of actual travel speed to posted speed limit called the Congestion Index (CI). A CI of 1.0 or greater indicates free flow speed, where traffic is traveling at the speed limit or higher. Municipalities can define 7

12 levels of CI to indicate free flow, average flow, and congested flow. This information can be used in the planning process to better appropriate funds for needed improvements. Congestion Index = Actual Average Speed / Weighted Average Posted Speed Limit Actual Average Speed = Average speed of all runs on a segment in a peak period Weighted Average Posted Speed Limit = Average of all posted speed limits on the segment weighted by length According to the CCMPO criteria established with the 2003 study, a CI less than 0.75, indicates a congested section. For example, this would be traveling less than 30 mph when the posted speed limit is 40 mph. A CI of 0.75 to 0.99, or approximately 30 mph to 39 mph, indicates a section of stable flow. And a CI greater than 0.99, or 40 mph or higher, indicates free flow conditions. Table 1 defines the congestion index criteria. The travel speeds on congested segments are slower than drivers typically want to drive, and there may be less opportunity for lane changing and maneuvering. Stable sections are accommodating volumes less than capacity. Travel speeds are somewhat slower than the speed limit, but generally acceptable to drivers. Lane changing and maneuvering is less difficult than in congested segments. Free-flow sections are operating well below capacity. Travel speeds that equal or exceed the speed limit indicate that traffic can maneuver without interference. Table 1 Congestion Index Criteria Congestion Index (CI) Congestion Stable Flow Free Flow < to 0.99 > Pavement Roughness Pavement roughness was evaluated for the first time in Fall 2010 and presented in GIS. This was included to assist the CCMPO staff to objectively evaluate pavement condition along corridors. Each route was driven in one direction. The data was collected and processed as described below. 1. GPS unit records a position, time and speed every second. 8

13 2. A 3D-accelerometer was mounted to the vehicles axle. It captures the 3-axis acceleration of the axle 20 times per second (Hz) and the position is interpolated based on speed and time of the last GPS reading. 3. As shown in the illustration, the 20 Hz acceleration data are filtered using a high pass digital filter that removes the lower frequency components (less than 1.5 Hz) of the raw vertical acceleration values (blue line). A common source of these low frequency components are centrifugal forces around curves. The filtered data (red line) are visually inspected and compared to the original, unfiltered data to ensure proper performance of the filter. 4. The area under the red acceleration curve represents the relative pavement roughness and is calculated by multiplying the distance traversed by the vertical accelerometer value. 5. Each acceleration value is then grouped into a 0.1 mile segment and the total area under the curve per foot and percent above each standard deviation is summarized for each 1/10 th of a mile segment. 9

14 4. The CCMPO Linear Referencing System The traffic elements that were captured during mapping including intersection control, speed limits school zones limits, number of lanes, median type, bike lanes and construction areas are shown in the following figures. Other elements that were coded in GIS using data provided by the MPO included: jurisdictional boundaries, model area type, and model facility type. This information was used to determine the segment lengths and theoretical travel times, and to provide better insight into the resulting travel time runs and improvement recommendations. 10

15 Figure 2 Intersections Controls 11

16 Figure 3 Posted Speed Limits 12

17 Figure 4 School Zones 13

18 Figure 5 Number of Lanes 14

19 Figure 6 Median Type 15

20 Figure 7 Bike Lanes 16

21 Figure 8 Construction Zones 17

22 Figure 9 Jurisdictions 18

23 Figure 10 Model Area Type 19

24 Figure 11 Model Facility Type 20

25 5. Evaluation of Congestion There was a large amount of data collected for the 2010 Travel Speed Study. The best way to present and assimilate the information is in tables, charts, and graphics. This section presents the results of the study in visual format, allowing the reader to reach individual judgments at their discretion, and offering summary conclusions, by section. The summary data has been aggregated on various levels. Data can be viewed from levels as detailed as the raw 1-second point data to the intersection segments. This allows the data to be presented in various forms depending on the audience. The following sections display the resulting speed for the 330-centerline miles of roadway included in the study. This section displays all information collected for this study for evaluation, interpretation, and use in developing and prioritizing future projects. 5.1 Travel Speeds Figures summarize the average speed data for each of the three time periods. 21

26 Figure 12 AM Average Speed 22

27 Figure 13 Mid-day Average Speed 23

28 Figure 14 PM Average Speed 24

29 5.2 Congestion Index Figures 15, 16 and 17 include the summary data for congestion index. Using the mapping effort and subsequent LRS that was developed, the CI (percent of posted speed) was calculated to represent the delay encountered. Figures 18 and 19 show the percentage change of CI compared to the 2006 historic values for AM and PM. Since Mid-day runs were not conducted in 2006, a comparison to 2006 is not possible for 2010 Mid-day runs. The table below shows the summary of Congestion Index for AM, Mid-day and PM for Table 2 Summary of Congestion Index for 2010 Peak Period AM Mid-day PM Congested Miles Stable Miles Free Flow Miles Total Miles % 66% 10% 100% % 48% 6% 100% % 63% 11% 100% Tables 3 and 4 list the segments with the greatest change since 2006 based on CI. These summaries have been filtered to include those segments with true congestion and not impacted by construction, school zones, or short segment length. Also, segments with a significant weighted average speed limit change (more than 3 mph) were excluded as the CI is dependent on speed limits. Table 5 shows the worst segments in 2010 by CI. 25

30 Figure 15 AM Congestion Index 26

31 Figure 16 Mid-day Congestion Index 27

32 Figure 17 PM Congestion Index 28

33 Figure 18 AM Percent Change In Congestion Index From

34 Figure 19 PM Percent Change In Congestion Index From

35 Table 3 Top 10 Segments With Largest Drop in Performance Relative to 2006 Route ID Route Name IntersectionSegment Peak Period 2010 Congestion Index (CI) 2010 Avg Speed (mph) 2010 Weighted Speed Limit (mph) 2006 Congestion Index (CI) 2006 Avg Speed (mph) 2006 Weighted Speed Limit (mph) CI Change % 4023 EVERHART - SB MCARDLE TO SPID WBFR PM EVERHART - NB SPID WBFR TO MCARDLE PM EVERHART - NB CORONA TO SPID EBFR AM MORGAN AVE - BROWNLEE TO 286 WB NB AM EVERHART - SB SPID EBFR TO CORONA PM RODD FIELD - NB WILLIAMS TO SPID EBFR AM AIRLINE - SB GOLLIHAR TO MCARDLE PM SARATOGA - WB AYERS TO SH 286 NB PM EVERHART - SB SPID EBFR TO CORONA AM ALAMEDA - EB EVERHART TO ROBERT PM Note: Short Segments (< 500ft), segments with speed limit changes between 2006 and 2010 and segments containing school zones have not been considered for this table 31

36 Table 4 Top 10 Segments With Largest Improvement in Performance Relative to 2006 Route ID Route Name ALAMEDA - WB ALAMEDA - WB 4129 Horne - EB 4076 Morgan Ave - WB IntersectionSegment Peak Period 2010 Congestion Index (CI) 2010 Avg Speed (mph) 2010 Weighted Speed Limit (mph) 2006 Congestion Index (CI) 2006 Avg Speed (mph) 2006 Weighted Speed Limit (mph) CI Change % Clifford TO Ayers PM Robert TO Everhart PM PRESCOTT TO CROSSTOWN SB AM SB TO 19TH PM AIRLINE - SB Cimarron TO Wooldridge PM STAPLES - SPID WBFR TO EB/NB McArdle AM Buddy Ganem 181 NBFR TO EB/NB/WB SBFR PM Buddy Ganem 181 SBFR TO EB/SB/WB NBFR AM LANG - WB Cedar TO Wildcat PM McArdle - WB MALL ENTRANCE TO STAPLES AM Note: Short Segments (< 500ft), segments with speed limit changes between 2006 and 2010 and segments containing school zones have not been considered for this table 32

37 Rank RouteID Table 5 Top 20 Worst Segments By Congestion Index in 2010 Peak Period 2010 Congestion Index 2010 Average Speed (mph) 2010 Weighted Average Speed Limit (mph) Route Name Intersection Segment STAPLES - SB/WB MCARDLE TO SPID WBFR PM EVERHART - SB MCARDLE TO SPID WBFR PM RODD FIELD - NB WILLIAMS TO SPID EBFR AM STAPLES - SB/WB SHOPPING WAY TO MCARDLE PM EVERHART - SB SPID EBFR TO CORONA PM STAPLES - EB/NB HEB TO SPID EBFR PM GOLLIHAR - WB AYERS TO 286 NB AM SPID FRONTAGE - EB STAPLES TO AIRLINE PM AYERS - NB BROWNLEE TO ALAMEDA/STAPLES MD GREENWOOD - SB TROJAN TO SPID WBFR PM STAPLES - EB/NB FIRESTATION TO HOLLY AM COMPTON/KNICKERBOXER TO SPID EBFR MD WALDRON - NB AYERS - NB KOSAR TO BALDWIN AM HOLLY - EB SH 286 NB TO AYERS PM AIRLINE - SB MCARDLE TO SPID WBFR PM LEOPARD - EB LANTANA TO SPID EBFR PM HORNE - EB PORT TO AYERS MD SPID FRONTAGE - EB WEBER TO EVERHART MD CALLICOATE - SB REDRIVER TO LEOPARD PM STAPLES - EB/NB WILLIAMS TO HEB PM Note: Short Segments (< 500ft) and segments containing school zones have not been considered for this table 2006 Rank 33

38 In order to further pin-point the congested segments and provide a common unit length for equitable comparison of segments, the intersection segments were divided into shorter 0.1 mile (~528 ft) segments and the congestion statistics were generated for these 0.1 mile segments in AM and PM peak periods. These are presented graphically in the following figures. A total of 6,647 such 0.1 mile segments were analyzed in AM and PM and 1324 were analyzed in Mid-day peak period. It was found that 972 segments in AM (approximately 15%), 316 segments in Mid-day (approximately 24%) and 977 segments in PM (approximately 15%) were congested. Of the congested segments, between 60-65% include a controlled intersection (Signal, Stop Sign etc.) as the downstream node in all three peak periods while the other 35-40% were midblock links. This observation shows that majority of the delays are localized within 0.1 miles of a controlled intersections and do not occur mid-block. These delays can be reduced by either signal timing improvements or intersection geometric changes. 34

39 Figure 20 Congestion Index during AM on 0.1 Mile Segments 35

40 Figure 21 Congestion Index during PM on 0.1 Mile Segments 36

41 The power of LRS is further illustrated by summarizing the study mileage and congested mileage by Area Type, Facility Type as shown in tables below. Table 6 Total Study Run Miles by Area Type and Facility Type Facility Type Peak Period Area Type Interstate Freeways Other Freeways Principal Arterial Minor Arterial Collector Frontage Road Ramp Unassigned Total AM Mid-day PM CBD CBD (Fringe) Urban Suburban Rural Other Unassigned Total CBD CBD (Fringe) Urban Suburban Unassigned Total CBD CBD (Fringe) Urban Suburban Rural Other Unassigned Total As can be seen from Table 6, over 50% of the directional miles of roadways studied in 2010 are arterials. Also, most of the roadways are in CBD (Fringe) areas and Suburbs. 37

42 Table 7 Total Congested Miles by Area Type and Facility Type Facility Type Peak Period Area Type Interstate Freeways Other Freeways Principal Arterial Minor Arterial Collector Frontage Road Ramp Unassigned Total AM Mid-day PM CBD CBD (Fringe) Urban Suburban Rural Other Unassigned Total CBD CBD (Fringe) Urban Suburban Unassigned Total CBD CBD (Fringe) Urban Suburban Rural Other Unassigned Total As can be seen from Table 7, most of the congested segments are arterial segments (Principal and Minor) in all peak periods. The freeways are relatively congestion free. While arterials make up approximately 53% of the routes studied in 2010 (347.9 miles of miles), 73% (109.4 of miles) of congested segments in AM are arterials. Similarly, 75% (118.6 of miles) of congested segments in PM are arterials. Arterials in CBD fringe area have the most number of congested segments. 38

43 6. Roadway Pavement Assessment The list of worst 5% of the pavement segments found to be rougher than others in the CCMPO area in the Fall 2010 season and the corresponding lengths of rough segments within these are shown in Table 8. Pavement roughness results are shown in Figure 22 for visual reference. The map illustrates the relative roughness of the roadway 0.1 mile segments driven. Only the worst 5% of segments are shown in this map. Figure 23 shows a couple of examples of pavement roughness profiles. These can be constructed by plotting the attribute Total Area Over Length which represents the area under unit length of the roughness curve. A Total Area Over Length greater than 0.33 represents the worst 5% of the segments for this year s routes studied. It should be noted that in general, only one direction of the route was driven to obtain the roughness profiles. Table 8 Worst 5% of Route segments for Pavement Roughness Route Id Route Name Total Length of Rough Segments (miles) 4021 STAPLES - SB/WB WEBER -SB/WB MCKINZIE - SB MCKINZIE - NB FM EB FM WB US EB SH 44 (Cesar Chavez) - EB FM 665 (Old Brownsville) - NB/EB Flour Bluff - EB SH NB SH SB

44 Figure 22 Pavement Roughness 40

45 SH 286 SBFR Palm Nueceses Villa SANTA FE THIRD Lantana SPID WBFR STAPLES 19TH 286 SB Corn Products Rd PORT BALDWIN AIRPORT Bear Lane CLIFF MAUS Warpath SPID EBFR Area Under Roughness Curve Leopard -EB River East Dr Wildcat Entrance ramp to IH 37 Callicoate Violet Area Under Roughness Curve FM 665 (Old Brownsville)/Morgan -NB/EB JCT 763 SH 357 Cross-street County Rd 69 - New Signal RIVER HILL DR FM 1889 / Trinity River 73A County Rd 81 FM mile area Cross-street FM mile area Figure 23 Example Pavement Roughness Profiles 41

46 7. Conclusions This report on the 2010 Travel Speed Study presents the congestion trends in AM, Midday and PM peak periods. The study also pin-points congestion within the roadway network and helps prioritize improvements. The next phase of this CMP process will address congestion mitigation plans and traffic studies on select corridors which will be identified based on the travel speed study. The study also presented the areas needing pavement improvements or further pavement evaluations. Of the 660 directional miles studied in AM and PM, it was observed that 24% were congested in AM, and 26% were congested in PM based on the Congestion Index metric i.e. they had a CI of less than Of the 130 directional miles studied in the Mid-day peak, it was observed that 46% were congested. In 2006, approximately 22% of the miles studied were congested in AM and 20% of the miles studied were congested in PM. Of the segments that were common to both 2006 and 2010 studies, 535 segments (359 directional miles) worsened in Congestion Index and 435 segments (238 directional miles) experienced an improvement in Congestion Index in the AM peak period in In the PM peak period, 601 segments (416 directional miles) worsened in the Congestion Index while 382 segments (190 directional miles) improved in In order to further pin-point congestion, 0.1 mile segments were created and the travel speed data were analyzed at this level. Of the congested segments at this 0.1 mile segment level, between 60-65% include a controlled intersection (Signal, Stop Sign etc.) as the downstream node in all three peak periods while the other 35-40% were midblock links. This observation shows that majority of the delays are localized within 0.1 miles of a controlled intersections and do not occur mid-block. These delays can be reduced by either signal timing improvements or intersection geometric changes. Comparison to other Metro areas in Texas: The travel speed studies are conducted every two years in Austin area (CAMPO). The Fall 2008/Spring 2009 study was conducted on approximately 925 miles of roadways in the CAMPO region. The study included 82 different roadways divided into 3002 directional links bound by a traffic signal, stop sign, or major cross street. Of the 1831 directional miles of roadways studied in the AM peak period, 510 miles were free-flow, 824 miles were stable, and 497 miles were congested. Therefore, for the Fall 2008/Spring 2009 season, 72% of the roadways operated within an acceptable range during the AM peak period. Of the 1,831 directional miles of roadways studied in the PM peak period, 524 miles were free-flow, 794 miles were stable, and 513 miles were congested. Therefore, for the Fall 2008/Spring 2009 season, 72% of the roadways operated within an acceptable range during the PM peak period. 42

47 In Hidalgo County, traffic studies are conducted each year, rotating among the seasons. In 2010, the Fall season was studied. Past CMP studies include Spring 2001, Fall 2002, Summer 2003, Spring 2004, Winter 2005, Fall 2006, Spring 2007, Winter 2008/2009 and Summer Of the 668 directional miles of roadways studied in Fall 2010, 10 miles were free-flow, 314 miles were stable, and 344 miles were congested. Of the 687 directional miles of roadways studied in Winter 2008/2009, 24 miles were free-flow, 322 miles were stable, and 341 miles were congested. Therefore, for the Fall 2010 season, 49% of the roadways operated within an acceptable range (compared to 61-68% for previous studies between ). When comparing the congestion levels in Corpus Christi metro area to other metro areas, it should be noted that an objective comparison may not be feasible as the percentage of congested roads will vary depending on the extents of the networks studied and the functional classes/area types of roadway segments chosen to be included in the study. For example even though historically the Hidalgo MPO included 500 centerline miles of roadways in the CMP, the 2010 study focuses on 350 miles of roadways which are determined to be of more concern by the MPO. A direct comparison of numbers yields a conclusion that since 25% of roadways in Corpus Christi area are congested, Corpus Christi area roadways have less congestion than Austin area roadways studied in 2008 (28% congested) and Hidalgo area roadways studied in 2010 (51% congested). But the caveats involved in this conclusion should be understood as discussed above. 43