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NCHRP Report 616: Multimodal Level of Service Analysis for Urban Streets (2008)
National Cooperative Highway Research Program (NCHRP)

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Flannery, Aimee, Dowling, Richard G, Rouphail, Nagui M, Petritsch, Theodore Anton, Landis, Bruce W, Bonneson, James A, Ryus, Paul, Reinke, David B, Vandehey, Mark, Transportation Research Board. "6.2 Recommended Transit LOS Model." NCHRP Report 616: Multimodal Level of Service Analysis for Urban Streets. Washington, DC: The National Academies Press, 2008.

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Page
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Page
78
Front Matter (R1-R11)
Summary (1-2)
1.2 The Research Plan (3-3)
1.3 This Report (4-4)
Highway Capacity Manual (5-5)
Transit Capacity and Quality of Service Manual (6-8)
Florida Quality/Level of Service Handbook (9-10)
Highway Capacity Manual (11-12)
Transit TCQSM Critique (13-13)
Florida DOT Q/LOS Handbook (14-14)
The Major Level of Service Manuals (15-15)
Implications for Research Project (16-16)
Urban Street LOS (17-17)
Intersection LOS Research (18-20)
Rural Road Research (21-21)
A Handbook for Measuring Customer Satisfaction (22-22)
3.3 Bicyclist Perceptions of LOS (23-23)
Segment LOS Models Based on Field Surveys or Video Lab (24-25)
Models of Rural Road Bicycle LOS (26-26)
Intersection Crossing LOS Studies (27-27)
Sidewalk and Path LOS Studies (28-28)
Midblock Crossing LOS Studies (29-29)
3.5 Multimodal LOS Research (30-31)
4.1 Selection of QOS Survey Method (32-34)
Auto Video Clips (35-35)
Bicycle Video Clips (36-37)
Pedestrian Video Clips (38-41)
Development of Master DVDs (42-45)
Selection of Video Lab Cities (46-46)
Recruitment (47-49)
Video Lab Sessions (50-50)
4.5 Effects of Demographics on LOS (51-51)
Effects of Demographics on Auto LOS Ratings (52-52)
Effects of Demographics on Pedestrian LOS Ratings (53-53)
Field Data Collection (54-54)
Survey Form Development (55-56)
Survey Distribution (57-57)
Route Characteristics (58-59)
4.7 Representation of Survey Results By A Single LOS Grade (60-61)
Linear Regression Tests (62-63)
Limitations of Linear Regression Modeling (64-64)
Performance of Candidates (65-68)
5.2 Recommended Auto LOS Model (69-70)
5.3 Performance of Auto LOS Models (71-71)
Selection of Explanatory Variables for LOS (72-73)
Elasticity Concept (74-76)
Reliability (77-77)
6.2 Recommended Transit LOS Model (78-78)
Estimation of the Transit Wait Ride Score (79-80)
6.3 Performance of Transit LOS Model (81-81)
7.2 Recommended Bicycle LOS Model (82-82)
Bicycle Intersection LOS (83-83)
7.3 Performance of Bicycle LOS Model on Video Clips (84-85)
8.1 Model Development (86-86)
Pedestrian Other LOS Model (87-87)
Pedestrian Midblock Crossing Factor (88-90)
8.3 Performance Evaluation of Pedestrian LOS Model (91-91)
Input Variable Interactions Among Modes (92-94)
Interactions Among Modal LOS Results (95-95)
Chapter 10 - Accomplishment of Research Objectives (96-97)
References (98-101)
Appendix A - Subject Data Collection Forms (102-104)
Appendix B - Study Protocol (105-109)
Appendix C - Example Recruitment Flyer/Poster (110-110)
Abbreviations used without definitions in TRB publications (111-111)

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78 Exhibit 84. In-Vehicle Time Value of Stop Amenities. train would be 2.58 pence/minute, corresponding to a value of time 36% higher than in uncrowded conditions. This value- Amenity In-Vehicle Time Value (min) Shelter with roof and end panel 1.3 of-time factor, 1.36, corresponds to factor a1 in the perceived Shelter with roof 1.1 travel time rate equation. In application, the transit LOS Lighting at bus stop 0.7 model would provide a lookup table based on a similar calcu- Molded seats 0.8 Flip seats 0.5 lation using bus passenger value-of-time to directly provide a1. Bench seats 0.2 Section 6.10 provides an example of such a lookup table. SOURCE: Calculated from Steer Davies Gleave, Bus passenger preferences. No corresponding British values exist for bus crowding For London Transport buses. (1996) in Balcombe, R. (editor), [105]. penalties and no American work was found that could be used to compare U.S. rail crowding perceptions to U.K. per- should reduce, if not eliminate, the perceived travel time ceptions. Additional research would be needed to establish penalty. (In other words, perceived excess wait time would be U.S. crowding penalty values. In the absence of other data, the the same as actual excess wait time when real-time bus-arrival recommended transit model uses a combination of U.K. bus information is provided.) Although this seems to be a reason- value-of-time data and rail crowding penalties. able hypothesis, to date, the project team has not seen any literature documenting such an effect. Load Variability Effects Crowding Unreliable operations tend to result in higher levels of crowding on buses that are running late, because these buses Perceived Travel Time Effects pick up not only their own passengers, but passengers In the same way that passengers perceive or value wait time who have arrived early for the following bus. For existing- more than in-vehicle time, passengers also perceive or value condition analyses, this crowding can be measured directly. time spent in crowded conditions more than time spent in For future-condition analyses, for frequent service (i.e., head- uncrowded conditions. Exhibit 85 presents values of train ways approximately 10 minutes or less), this additional crowding used in Great Britain. crowding can be estimated as the mean load multiplied by (1 + To demonstrate how the Exhibit 85 information could be cvh) (Derived from the TCQSM, Part 4, Appendix E, Equa- applied, consider a situation where a train is operating with a tions 4-22 and 4-23). At long headways, a late bus will pick up passenger load that is 120% of its seating capacity (i.e., a load its normal load, because passengers will have timed their ar- factor of 1.20). In the absence of crowding, rail commuters rival at the bus stop to the expected departure time (Furth and value in-vehicle time at 7.2 pence/minute. At a load factor of Miller, previously cited). There is also an intermediate range 1.20, seated commuters experience a penalty of 1.6 of headways with a mix of randomly and non-randomly ar- pence/minute due to the more crowded conditions, while riving passengers [107]. This late-bus load can be used in the standing commuters experience a penalty of 7.5 pence/minute perceived travel time calculation described in the previous (i.e., standees perceive their time spent standing as being more section, thus incorporating the effect of unreliable service's than twice as onerous as being seated in an uncrowded car- crowding into the LOS measure. riage). The weighted average penalty for all passengers in the The literature review uncovered no information directly relating overcrowding and/or reliability to transit demand. Exhibit 85. British Crowding Penalties In the United States, the San Francisco County Transporta- for Rail Passengers. tion Authority appears to be the only agency to have docu- mented a test of reliability and crowding factors for use in a Crowding Penalty (pence/min) Load Factor (p/seat) Seated Passengers Standing Passengers travel demand model. The tested values were based on a 0.80 0.0 -- stated-preference telephone survey, but were not incorpo- 0.90 0.4 -- rated in the final model because the predicted number 1.00 0.8 6.5 of boardings did not reasonably match the observed number 1.10 1.2 7.0 1.20 1.6 7.5 of boardings [108]. 1.30 2.0 8.0 1.40 2.4 8.5 1.50 -- 9.0 6.2 Recommended Transit 1.60 -- 9.5 LOS Model NOTE: The baseline value of in-vehicle time for rail passengers is 7.2 pence/minute, in 2000 prices. Intermediate values are obtained through The recommended transit LOS model predicts the average linear interpolation. The baseline value of in-vehicle time for bus passengers is 4.2 pence/minute; no corresponding crowding penalties are available. quality of service rating that transit riders would give the bus SOURCE: Derived from Balcombe [106]. service on an urban street. The model is as follows: