Addressing Deficiencies HCM Bike Level of Service Model for Arterial Roadways

Transcription

1 Petritsch, et al Addressing Deficiencies HCM Bike Level of Service Model for Arterial Roadways Submitted July 31, 2013 Word Count: 2,690 plus 11 tables and 7 figures at 250 words each = 7,190 words By Theodore A. Petritsch Corresponding author Bruce W. Landis Sprinkle Consulting, Inc U.S. Highway 41 North, Suite 600 Lutz, FL Phone: (813) Fax: (813) s: tap@sprinkleconsulting.com Landis@sprinkleconsulting.com Tyrone Scorsone Cambridge Systematics, Inc Village Square Blvd, Suite 2 Tallahassee, FL Phone: (850) Fax: (850) tscorsone@camsys.com

2 Petritsch, et al ABSTRACT The 2010 Highway Capacity Manual (HCM) includes methodologies for calculating Bicycle Level of Service (BLOS) as part of Multimodal LOS analysis. The methodology provides a model for calculating a pseudo-academic letter grade scaled from A to F that represents bicyclists perceptions of safety and comfort. It is the standard for transportation engineering analysis in numerous locations throughout the United States. It combines the link bicycle LOS model with a bicycle intersection model, both developed by the Florida Department of Transportation (FDOT). Practitioners have found the HCM methodology does not provide intuitive results. The HCM methodology provides scores which represent a roadway as worse than it actually is, does not provide enough sensitivity to bike improvements, and does not provide an adequate range of responses. It is difficult to achieve an A of B LOS score using the HCM methodology. Adding bike lanes does not have a significant impact on the LOS. It is also very difficult to achieve an LOS of worse than E regardless of how bad a roadway is. This paper describes an effort by FDOT to create an alternative model that better represents how well roadways meet the needs of bicyclists. It is based upon the time exposed and relative LOS value for each individual LOS component (intersection/link). It was developed with the input of a panel of practitioners from around the country. The resulting model represents an LOS methodology that provides more intuitive values (than the HCM methodology) for those evaluating their roadway networks.

3 Petritsch, et al BACKGROUND The 2010 Highway Capacity Manual (1) (HCM) includes methodologies for calculating Bicycle Level of Service (BLOS) as part of Multimodal LOS analysis. The intent of the BLOS score is to provide a way of measuring the perceived levels of safety and comfort of bicyclists riding in a roadway environment. The methodology provides for readily measurable roadway and traffic values to be entered in a model that provides a numerical BLOS value. This numerical score is then translated into a pseudo-academic letter grade scaled from A to F using the stratification shown in Table 1. The HCM methodology is essentially the standard for transportation engineering analysis in numerous locations throughout the United States. NCHRP 3-70 Multimodal Level of Service Analysis for Urban Streets The HCM BLOS model was developed as part of NCHRP 3-70 Multimodal Level of Service Analysis for Urban Streets.(2) Phase III of this NCHRP project included the evaluation of eleven roadways to assess how well the multimodal LOS methods worked on real roadway. This analysis included evaluations of roadways in Atlanta, GA (four roadways) Austin, TX (three roadways), and San Antonio, TX (four roadways). FDOT supplemented these roadways with eight additional roadways: four in Tallahassee, and four in Tampa. When reviewed by the local communities and a national group of practitioners, the results of these analyses were found to be lacking. The cyclist and practitioners found the HCM methodology for calculating BLOS does not provide intuitive results. The HCM methodology suffers in three primary areas: 1) it does not provide an adequate range of LOS scores, 2) it does not provide enough sensitivity to the addition of roadway improvements for bicycles 3) the model does not properly weight the poorest performing portion of a facility. The HCM model, because it has a relatively high constant, makes it difficult to achieve an LOS score of either an A or a B regardless of how low volume or low speed a roadway might be. Additionally, an LOS of F is difficult to achieve even on high-speed, high-volume roadways. Practitioners also feel that the addition of bike lanes should have a greater impact on the LOS. Lastly, the BLOS model produces link LOS letter grades that are worse than intersection LOS letter grades for a facility. This project, and this paper, is intended to address these shortcomings. Additionally the current model uses a weighted average method of determining the overall level of service for a segment (combination of multiple link-intersection analyses sections). Some practitioners felt that this understated the impact of roadway links with very poor level of service. It is hypothesized that particularly bad links of roadway have a more pronounced effect on the perception of the segment. Consequently, some weighting factor that considers the actual LOS grade is included as a consideration in this project. The Existing HCM Model The exiting form of the HCM BLOS model is as follows:

4 Petritsch, et al HCM BikeSegLOS = a1*link LOS + a2*intersection LOS + a3*driveways per mile +C Bike LOS Model Parameters a1 = 0.16 a2 = a3 = C = 2.85 In addition to the base form of the equation, an equation that was developed to increase the variation in LOS scores (result in more A, B, and F roadways) was also developed. The form of the equation is the same, but the parameters were changed. Bike LOS Stretched Model Parameters a1 = 0.2 a2 = 0.03 a3 = 0.05 c = 1.4 T The equations above are applied on a singular intersection and segment combination. The results are then combined as a length weighted average: where Σ ArtBikeLOS = Arterial Bike LOS SegBikeLOS = Segment Bike LOS SegLength = Length of segment ASSUMPTIONS The Components of the Segment Model The component models of the HCM BLOS methodology are the FDOT BLOS (3,4) and the FDOT BLOS for the intersection through movement (5). Both of these models were developed with input from actual cyclists riding courses in urban/suburban areas. They have been used in many communities around the country and provide results that practitioners have felt are intuitive. They were use as the link and intersection components of the HCM methodology and will be used as the link and intersection components for this proposed revision to the HCM methodology. Other Assumptions The evaluation spreadsheets used for the FDOT s 3-70 testing were reviewed so that the programming could be used in the comparison of HCM results to results obtained from potential modifications to the model.

5 Petritsch, et al When beginning this project, a sample roadway was evaluated using the HCM methodology. Using a sample of five segments, with various inputs the results shown in Table 2 were reported by the FDOT 3-70 testing spreadsheet. Each line represents one link and a single downstream signal. Please note the following: Line 1 has a truck percentage of 25% and poor pavement. This explains the 17.6 value for the segment LOS score. It does not seem reasonable that a good intersection would improve bicyclists overall perception of the roadway as much as this suggests. While still an LOS of F, it is a much better F as a result of the intersection. Lines 2 and 3 suggest a second issue. The overall levels of service are three and two letter grades worse than either of the component scores. Segment 2 has link and intersection LOSs of A; Segment 2 a link LOS grade of B and an intersection LOS of A. Both have a composite score of LOS D for the overall section. While a numerous driveways could conceivably account for this shift, the constant of 2.85 is the primary cause of this shift. Lines 4 and 5 suggest that encountering a good intersection improves the overall perception of the roadway segment. After discussion with the review panel, the researchers made the following recommendations for base assumptions of the level of service: 1. The link BLOS should control the BLOS on the segment. That is, the BLOS for the segment should never be better than the BLOS for the link. One possible exception might be in the absence of driveways (seen point 3 below). 2. Except as modified by driveways per mile the Segment BLOS should not be worse that the worse of either the link or intersection BLOS. 3. Some base driveways per mile should be assumed. Zero driveways per mile is not a typical base condition for an arterial roadway. We recommend looking at the original Ride for Science data and determining a assumed base number of driveways and applying the driveway factor coefficient to the actual number of driveways minus the assumed number. The result could be a slightly improved BLOS, however given the coefficient and the likely assumed value, the improvement would not be that great. The review panel agreed with these base assumptions. DEVELOPMENT OF THE REVISED METHODOLOGY During discussions of the Transportation Research Board Highway Capacity and Quality of Service Committee Pedestrians and Bicycles Subcommittee, a recommendation was made to consider time-based exposure weighted coefficients for the ArtBikeLOS model. Because intersections do not represent a length along a facility, it was suggested that time would be a better weighting factor.

6 Petritsch, et al The additional consideration was the impact of links with varying badness on the perception of the overall arterial segment. It is hypothesized that particularly bad links of roadway have a more pronounced effect on the perception of the segment. An exponential weighted average was considered to compensate for this hypothesized phenomenon. However, applying an exponent to the BLOS would be problematic as it can have a negative value. Applying an exponent of less than one to the exposure was recommended as an alternative by a panel member and this is the approach taken forward. Thus, the revised model form would be as follows: where Σ Σ CompBLOS = either link or intersection bicycle BLOS (the driveway factor would become a term in the link BLOS model) CompTime = time exposed to link or intersection bicycle LOS n = exponent modifier for weighted average CompTime for links was calculated using the segment length and an assumed bicyclist s speed of 12 mph. for intersections was calculated using the simple delay equation 2 where C = cycle length g = green time This proposed time-based exposure model also allows sensitivity to the speed of bicyclists. This would allow the user to select an appropriate bicyclist cohort and thus better represent the relative times spent riding on links or delayed at intersections. Additionally, the speeds could be adjusted based upon the grade of the roadway. Discussion of the Exponents For this discussion we ask the reader participate in a thought experiment. Consider an eight-mile bike ride on a very pleasant facility, perhaps an extremely low speed, extremely low volume roadway or a park access used only by one or two service vehicles a day road (shared use path like in all respects except that it was built as an access road for service vehicles). Such a roadway could score a negative link level of service value, but for the purposes of this thought experiment, assume a value of zero, a very good A. To get to this eight mile bike ride one needs only to ride on a quiet lane, BLOS numerical score of 1, an A. Likewise the intersections linking the path to the roadway are excellent, assume BLOS value Ignoring the conflicts per mile term of the Bike Segment LOS equations for now, and assuming an average bicycle speed of 12 mph, in tabular format the trip would be represented in Table 3:

7 Petritsch, et al Consider the Figure 1. The solid LOS line indicates the link or intersection LOS experienced at any given point during the trip (trip time has been converted to % of trip time so all charts in this paper will have a similar horizontal scale). The HCM (dotted) line represents the ArtBikeLOS as calculated using the base HCM method. The HCM Stretched (dashed) line represents the ArtBikeLOS as calculated using the base HCM method adjusted to expand the range of scores. The LinExposure (long dash-dot) line represents the time weighted average LOS with no exponent applied (linear). The Exp 0.5 (short dash-dot) line represents the time weighted average LOS with an exponent of 0.5 applied. The Exp 0.25 (long dash-dot-dot) line represents the time weighted average LOS with an exponent of 3 applied (cubed). The values associated with each of the calculated segment BLOS methods are shown in Table 4. From this example it is clear that the HCM method calculates BLOS that is much worse that could reasonably be expected. While the Stretched HCM method results in a BLOS of A, the numerical score still exceeds any of the individual component parts; again, this is does not appear reasonable. The LinExposure, Exp 0.5, and Exp 0.25 BLOS methods all yield considerably reasonable results. This example provides little support for using any method more complicated than a linear time-weighted average of the individual component BLOS scores. As another thought experiment, consider that to get to this eight-mile path-like roadway bike ride, one must ride on 1 mile of unpleasant very congested, higher speed facilities without bike lanes or paved shoulders. Assume an LOS value of 6.00 for this roadway. Further assume the intersections have a BLOS value of Ignoring the conflicts per mile term of the ArtBikeLOS equations for now, in tabular format the trip would be represented in Table 5. The values associated with each of the calculated segment BLOS methods are shown in Table 6. In this second example, the linear time-weighted average does not seem to adequately represent the impact of the degradation of having to spend nearly 20% of the trip time on a very bad roadway. Either the 0.5 exponent time-weighted method or 0.25 exponent time-weighted method might be considered to reasonably represent the over BLOS for the ride. Members of the bike and pedestrian subcommittee were of the opinion that the 0.5 exponent function more accurately represented conditions along the facility. Figure 2 below provides a graphic example of how the methodologies compared. Chart similar to those shown above for the two-thought experiments were developed for all 19 of the roadways evaluated during the NCHRP 3-70 Phase III effort. Space

8 Petritsch, et al constraints prevent the authors from showing all 19 charts and tables for the NCHRP 3-17 Phase III study, however several are provided for the readers review. In many of the examples the 0.5 and 0.25 exponential models provide very similar results. Modification of the Unsingalized Conflicts Term The unsignalized conflicts term sensitivity appears reasonable. However, given that arterial roadways typically have driveways, it would be reasonable to assume a base condition other than zero conflicts per mile and apply the modification factor to variations from that base. The 19 sections used for the sensitivity analysis had an average conflict density of 21.3 unsignalized conflicts per mile. Consequently, it is recommended 20 conflict per mile be the base from which modifications are calculated. This would result in a conflicts per mile modification factor calculated as follows: This equation could result in an overall improvement of 0.7 if no unsignalized conflicts are present. This term should be added to the link BLOS as it will be applied over the same distance and hence time. Additional Examples Zarzamora Avenue, San Antonio, TX Table 7 provides a summary of the link and intersection BLOS values for Zarzamora Avenue, San Antonio, TX. Figure 3 and Table 8 represent the comparison of BLOS scores as before. In this example there is a significant difference between the squared and cubic functions. Discussions amongst the research team and with review panelists suggest the 0.5 exponent function better represents the actual roadways with poor BLOS. San Pedro Avenue, San Antonio, TX Table 9 provides a summary of the link and intersection BLOS values for San Pedro Avenue, San Antonio, TX. Figure 4 and Table 10 represent the comparison of BLOS scores as before. The resultant HCM BLOS is lower than any of the individual links; the Stretched HCM method result exceeds only one link BLOS. The linear and exponential models provide similar results (the vertical scale of the chart has been expanded to better show the difference in the values there is). This is because most of the exposure time (the sum of the link exposure is 94.7% of the total exposure) spans a numerical range of only Tables 9 and Figure 5 show the influence of the various unsignalized conflict modification factors on the resultant LOS. Figures 10 and 11 illustrate the difference in the sensitivity to bike lanes between the HCM methodology and the proposed time-exposure function. On the sample roadway, San Pedro Avenue in San Antonio, TX, adding a bike lane made a 0.54 value difference in the HCM Segment BLOS. Using the proposed methodology the difference was RECOMMENDATIONS

9 Petritsch, et al The HCM Segment bike LOS methodology should be replaced with a methodology that is more sensitive to the roadway conditions and better represents the conditions of the roadway. Based upon the consensus of a practitioners, a 0.5 exponent based timeexposure function should be considered as an alternative to the HCM method. Additionally, the driveway modification factor should be modified to reflect a base number of driveways and be applied directly to the link BLOS. ACKNOWLEDGMENTS The authors wish to thank the members of the TRB Highway Capacity and Quality of Service Committee Pedestrians and Bicycles Subcommittee who donated their valuable time to provide feedback on this project. In particular we would like to thank Robert Bryson, Janice Daniels, Martin Guttenplan, Peyton McLeod, and a special thank you to Jamie Parks for his help with the eliminating the exponent of a negative problem. REFERENCES (1) Transportation Research Board, Highway Capacity Manual 2010, Transportation Research Board of the National Academy of the Sciences (TRB), Washington, D.C., (2) Dowling, R., et al, NCHRP Report 616 Multimodal Level of Service Analysis for Urban Streets, TRB, Washington, D.C., (3) FDOT, Quality/Level of Service Handbook, FDOT, Tallahassee, FL, (4) Landis, B., V. Vattikuti, and M. Brannick. Real-Time Human Perceptions: Toward a Bicycle Level of Service, Transportation Research Record 1578, Transportation Research Board, National Research Council, Washington, DC, (5)Landis, B., et al. Bicycle Level of Service for Arterials. Presented at the Transportation Research Board Annual Meeting, Washington, DC, 2007.

10 Petritsch, et al Tables and Figures

11 Petritsch, et al Table 1 Bike LOS Numerical Score vs. Letter Grades Numerical LOS Score Letter Grade 1.5 A >1.5 and 2.5 B >2.5 and 3.5 C >3.5 and 4.5 D >4.5 and 5.5 E >5.5 F

12 Petritsch, et al Table 2 Computed Bicycle LOS Segment & Link Intersect Bicycle Bicycle Downstream LOS LOS Score LOS Signal (#) (#) (#) F D D C C Average 4.98 E

13 Petritsch, et al Table 3 Land Park Road Lane Example BLOS Score Time of Exposure (Seconds) Link Intersection Link Intersection Section A 0.00 A Section A 0.00 A Section A N/A N/A 300 N/A

14 Petritsch, et al Table 4 Lane Path Lane Example Segment BLOS Method Numerical Value Letter Grade HCM 2.91 C Stretched HCM 1.49 A Linear 0.20 A Exp A Exp A

15 Petritsch, et al Table 5 Road Path Road Example BLOS Score Time of Exposure (Seconds) Link Intersection Link Intersection Section F 3.00 C Section A 3.00 C Section F N/A N/A 300 N/A

16 Petritsch, et al Table 6 Road Path Road Example Segment BLOS Method Numerical Value Letter Grade HCM 3.38 C Stretched HCM 2.40 B Linear 1.24 A Exp C Exp C

17 Petritsch, et al Table 7 Zarzamora Avenue, San Antonio, TX BLOS Score Time of Exposure (Seconds) Link Intersection Link Intersection Section D 2.27 B 54 1 Section D 2.27 B Section D 2.87 C 17 4 Section D 3.07 D Section A 1.64 A

18 Petritsch, et al Table 8 Zarzamora Avenue, San Antonio Example Segment BLOS Method Numerical Value Letter Grade HCM 3.43 C Stretched HCM 2.68 C Linear 2.99 C Exp C Exp C

19 Petritsch, et al Table 9 San Pedro Avenue, San Antonio, TX BLOS Score Time of Exposure (Seconds) Link Intersection Link Intersection Section D 3.36 C 22 4 Section D 3.25 C 43 0 Section E 4.01 D 43 3 Section E 4.18 D 77 5 Section E 4.21 D 81 2

20 Petritsch, et al Table 10 San Pedro Avenue, San Antonio, TX Example Segment BLOS Method Numerical Value Letter Grade HCM 4.23 D Stretched HCM 4.37 D Linear 4.78 E Exp E Exp E

21 Petritsch, et al Table 11 Link BLOS with Various Unsignalized Conflict Modifiers Modifier DW/Mile None HCM Propose Link Link Link Link Link

22 Petritsch, et al Figure 1 Lane-Park Road-Lane Example E:\ HCM Bike LOS for Arterial Roadways\Addressing Deficiencies in the HCM Bike Level of Service

23 Petritsch, et al Figure 2 Road-Park Road-Road Example E:\ HCM Bike LOS for Arterial Roadways\Addressing Deficiencies in the HCM Bike Level of Service

24 Petritsch, et al Figure 3 Zarzamora Avenue, San Antonio, TX E:\ HCM Bike LOS for Arterial Roadways\Addressing Deficiencies in the HCM Bike Level of Service

25 Petritsch, et al Figure 4 San Pedro Avenue, San Antonio, TX E:\ HCM Bike LOS for Arterial Roadways\Addressing Deficiencies in the HCM Bike Level of Service

26 Petritsch, et al Figure 5 Illustration of Potential Unsignalized Conflict Modifierr Options San Pedro Avenue, San Antonio, TX E:\ HCM Bike LOS for Arterial Roadways\Addressing Deficiencies in the HCM Bike Level of Service

27 Petritsch, et al Figure 6 HCM Method Sensitivity to Bike Lanes E:\ HCM Bike LOS for Arterial Roadways\Addressing Deficiencies in the HCM Bike Level of Service

28 Petritsch, et al Figure 7 Proposed Methodology Sensitivity to Bike Lanes 379 E:\ HCM Bike LOS for Arterial Roadways\Addressing Deficiencies in the HCM Bike Level of Service