Enhancing Pedestrian Volume Estimation and Developing HCM Pedestrian Methodologies for Safe and Sustainable Communities (2022)

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1 Summary Introduction In 2017, on a typical day in the U.S., approximately 38.9 million trips were undertaken primarily by walking, representing 10.5% of trips made by all modes, making walking the second-most prevalent transportation mode after driving or riding in a private motor vehicle (National Household Travel Survey). Furthermore, most trips involving private vehicles or public transit involve an element of walking, whether walking to and from parking places or walking to or from transit stops and stations. Despite widespread use of walking as a transportation mode, walking has received far less attention than the motor vehicle mode in terms of national guidance and methods to support planning, designing, and operating safe, functional, and comfortable facilities.  Pedestrian operations analysis, quantifying the operation of pedestrian facilities in terms of measures such as flow, speed, space, density, and delay, has the longest history. Research on pedestrian flow first published in the 1970s was added to the Highway Capacity Manual (HCM) in 1985, making methods for analyzing sidewalks, signalized intersection crosswalks, and signalized street corners widely available to transportation analysts. These operational methods were updated and expanded for the HCM2000 on the basis of more recent studies. Analysis methods for off-street exclusive and shared-use pedestrian paths were added to the 2010 HCM as a result of Federal Highway Administration (FHWA) research.  Pedestrian quality of service (QOS) analysis, quantifying the performance of pedestrian facilities from a pedestrian’s point of view, was first introduced on a wide scale around 2000 in Florida, beginning with “Walks for Science” (Landis et al. 2001), and leading to statewide analysis procedures that have since been incorporated into several editions of the Florida Department of Transportation’s Quality/Level of Service Handbook (Florida Departments of Transportation (DOT) 2009). Prior to these efforts, operational measures that mirrored traditional motor vehicle performance measures (e.g., density, delay) were used as proxies for pedestrian satisfaction in manuals such as the HCM. NCHRP Report 616: Multimodal Level of Service for Urban Streets (Dowling et al. 2008) built upon the Florida pedestrian methods using national data, and its methods appear in both the 2010 and 2016 editions of the HCM.  Pedestrian volume counting guidance on a national scale first appeared in conjunction with the National Bicycle and Pedestrian Documentation Program starting in 2004 (Alta Planning + Design 2010), and the Traffic Monitoring Guide (FHWA 2016) added a chapter on nonmotorized vehicle counting in 2013. The following year, NCHRP Report 797: Guidebook on Pedestrian and Bicycle Volume Data Collection (Ryus et al. 2014) summarized the state of the practice of nonmotorized counting and added new knowledge about the accuracy of various automated counting technologies.  Pedestrian safety analysis, although studied on a small scale for many years, has only been the focus of large-scale national projects for the last 10–15 years. These projects have generally focused on specific aspects of pedestrian safety such as uncontrolled crossings, risk-assessment methods, countermeasure selection, and systemic pedestrian safety analysis. The Highway Safety Manual (HSM), 2nd ed., in production at the time of writing, is expected to provide more comprehensive guidance on pedestrian safety analysis.

2 NCHRP Project 17-87’s objective was to develop guidance for urban, suburban, and rural jurisdictions to: (1) identify techniques to efficiently and accurately estimate pedestrian volume and exposure; (2) determine field-observed factors affecting pedestrian flow on pedestrian facilities; (3) determine how pedestrian safety improvements on the roadway and in signal timing designs should be reflected in the HCM pedestrian level of service (LOS); and (4) recommend corresponding enhancements to the current HCM methodology. This work led to several products:  NCHRP Research Report 992: Guide to Pedestrian Analysis (Ryus et al. 2022), which serves as a resource documenting the state of the practice for pedestrian volume counting, pedestrian safety analysis, pedestrian operations analysis, and pedestrian QOS analysis.  Presentation material for a webinar and a one-day workshop on the guide.  Draft HCM text, example problems, and spreadsheet-based computational engines covering: – A new method for evaluating pedestrian satisfaction and associated LOS at uncontrolled pedestrian crossings. – Updated pedestrian delay methods, correcting issues identified with the current methods and making the methods applicable to a greater variety of situations.  Draft text to update NCHRP Report 825: Planning and Preliminary Engineering Applications Guide to the HCM (Dowling et al. 2016) to match the proposed HCM changes and to incorporate a recommended approach to evaluating pedestrian network QOS. The remainder of this summary describes the Guide to Pedestrian Analysis and summarizes the findings of the research conducted by NCHRP Project 17-87. Guide to Pedestrian Analysis Given that much of the information related to pedestrian analysis has been developed relatively recently, both experienced and new analysts may be unaware of all the resources available for performing pedestrian analysis, as well as the potential applications for these analyses. The guide covers four main topics: pedestrian volume counting, pedestrian safety analysis, pedestrian operations analysis, and pedestrian QOS analysis. The guide provides a practitioner-friendly introduction to pedestrian analysis. Except for analysis methods developed or updated by NCHRP Project 17-87, which are not yet documented elsewhere, the guide refers readers to other documents for details about analysis methods and resources. The guide provides information about particular methods’ strengths, weaknesses, and data needs to help readers make informed decisions about which analysis methods may be of interest and can be performed with existing data and resources. Another intended use of the guide is to support decision-making to start up or expand pedestrian analysis programs by demonstrating the range of useful applications for the analysis results, particularly applications related to improving pedestrian safety. Depending on how specialized an analyst’s role is within an organization, a reader might only need to read the chapter covering their specific work area (e.g., volume counting, safety analysis) or might prefer to read the entire guide to learn more about how volume counts support all types of pedestrian analysis, and how different areas of pedestrian analysis are related to and can help support the other areas. Once the reader has read the guide once, it can be used in the future as a resource as specific analysis tasks arise.

3 Pedestrian Satisfaction Crossing Roadways Treated with Safety Countermeasures Background A variety of pedestrian safety countermeasures, such as curb extensions, median refuge islands, rectangular rapid-flashing beacons (RRFBs), and leading pedestrian intervals (LPIs) have been developed to make roadway crossings safer and more comfortable for pedestrians. Other research ongoing at the time of this project was investigating the effects of various countermeasures on pedestrian safety, with the objective of developing updated guidance to be incorporated into the Highway Safety Manual 2nd ed. In contrast, NCHRP Project 17-87 investigated the effects of various countermeasures on pedestrian comfort, with the objective of updating the pedestrian LOS methodologies in the HCM. The HCM defines QOS as “a description of how well a transportation facility or service operates from a traveler’s perspective.” From a pedestrian perspective, factors that influence QOS include pedestrian facility presence, condition, and connectivity; perceived safety and security; comfort (e.g., separation from traffic, protection from the elements, hills and slopes); pedestrian delay, speed, and crowding; wayfinding information and pedestrian amenity provision; and facility and adjacent land use aesthetics. LOS is the stratification of a QOS metric into six defined ranges, from A (best) to F (worst) (TRB 2016). The HCM 6th ed. uses pedestrian delay as the basis for determining the LOS of uncontrolled street crossings. The effects of some countermeasures on LOS can be evaluated using the HCM based on their ability to improve motorist yielding rates to pedestrians, shorten the crossing distance, or both, which tends to reduce pedestrian delay. However, it was not known whether the presence of safety countermeasures improved QOS in other ways. Similarly, at signalized crosswalks, the presence of LPIs only affected LOS in the HCM method if the amount of WALK time provided to pedestrians increased, and it was not known whether LPIs affect QOS in other ways. Research Approach To answer the question of how safety countermeasures affect the pedestrian QOS of roadway crossings, NCHRP Project 17-87 took a three-pronged approach involving:  Intercept surveys of pedestrians crossing the street at treated and control sites while making actual trips, to ask them about their satisfaction with their crossing experience, and later comparing the pedestrians’ ratings to the actual conditions (e.g., delay, traffic volumes, motorist interactions) they experienced while making the crossing;  Video observations of the study pedestrian crossings to identify whether safety countermeasures affected motorist or pedestrian behaviors compared to similar control sites (e.g., motorist yielding rates, pedestrian risk-taking behavior); and  A naturalistic walking study in which volunteers wore a biosensing wristband for a week that measured their stress levels while making their normal walking trips over the course of the week, including using some of the study crossings. Based on input from leading pedestrian practitioners, three countermeasures were selected for study based on their frequency of use and perceived effectiveness: median refuge islands, RRFBs, and LPIs. Pedestrian satisfaction at sites with median refuge islands, RRFBs, or both was compared to satisfaction at similar control sites with crosswalk marking and signage only, and to sites with unmarked crosswalks. Pedestrian satisfaction at signalized crosswalks with LPIs was compared to satisfaction at similar signalized crosswalks without LPIs.

4 Findings at Uncontrolled Crossings The following statistically significant factors were found to affect pedestrian satisfaction at uncontrolled crossings:  Average annual daily traffic (AADT) of the street being crossed;  Whether or not the pedestrian was delayed making the crossing (a function of the crossing length, traffic volume at the time of crossing, and motorist yielding behavior); and  The presence of specific countermeasures (crosswalk markings, median refuge islands, and RRFBs). Although pedestrian satisfaction at uncontrolled crossings decreased with increasing speed limit, traffic speed does not appear in the final model. Instead, AADT appears to account for such factors as speed, number of lanes crossed, and traffic intensity, as streets with higher AADTs are frequently wider and have higher speed limits. No statistical relationship was found between satisfaction and trip purpose, trip length, frequency of using the crossing, or crossing the street in conjunction with travel to or from a transit stop. This model can be used in conjunction with a pedestrian safety analysis that estimates the crash-reducing effect of a particular safety countermeasure. In cases where high-quality crash modification factors have yet to be developed by research for a particular countermeasure, this model can help support the case for implementing safety countermeasures by estimating the improvement in pedestrian satisfaction that the countermeasure would create. The model and accompanying LOS thresholds are recommended to be included in an update of HCM Chapter 20. Draft HCM chapter text, an example problem illustrating its use, and a spreadsheet-based computational engine have been developed by NCHRP Project 17-87. Findings at Signalized Crossings No significant difference in satisfaction was found between signalized crosswalks with and without LPIs. Pedestrians were generally satisfied with their crossing experience at both types of signalized crossings. The only field-measurable factors found to be significant in predicting satisfaction were the average volume of conflicting left-turning traffic during the pedestrian phase and the specific city where the survey was taken. Neither traffic speeds nor number of lanes crossed were found to be significant. However, pedestrian compliance with traffic signal indications was higher at the LPI crosswalks. It is cautioned that because the purpose of the study was to identify whether LPIs increased pedestrian satisfaction with their crossing experience, the control sites were selected to be comparable to the sites where LPIs had been installed—typically urban locations with relatively high pedestrian volumes. Developing a more generalized model for signalized crossings will require more data collection, in particular at sites with long cycle lengths, wider and higher-speed streets, and channelized right-turn lanes. Other Findings Effects on Motorist Yielding The data collected from video observations at 40 treated and untreated crosswalks was used to develop average motorist yielding rates for the safety countermeasures studied, as well as for unmarked crosswalks. This information was combined with data from the literature on a wide range of countermeasures to develop the up-to-date table of motorist yielding rates shown in Table S-1.

5 Table S-1. Effect of Pedestrian Crossing Treatments on Motorist Yielding Rates. Crossing Treatment Yield Rate (%) Sample Size Average Range (sites) No treatment (unmarked) 24 0–100 37 Crosswalk markings only (any type) 33 0–95 58 Crosswalk markings, plus: Overhead sign 26 0–52 2 Pedestal-mounted flashing beacon 35 12–57 2 Overhead flashing beacon (push-button activation) 51 13–91 14 Overhead flashing beacon (passive activation) 73 61–76 29 In-roadway warning lights 58 53–65 11 Median refuge island 60 0–100 21 Pedestrian crossing flags 74 72–80 6 In-street pedestrian crossing signs 76 35–88 20 RRFBs 82 31–100 64 School crossing guard 86 — 1 School crossing guard and RFFB 92 — 1 Pedestrian hybrid beacon (HAWK) 91 73–99 37 Midblock crossing signals, half signals 98 94–100 13 Source: NCHRP Project 17-87 literature review and field data collection. As suggested by the large range of observed yielding rates for many pedestrian crossing safety countermeasures, motorist yield rates are influenced by a range of factors. These factors include roadway geometry, travel speeds, isolated vs. corridor- or citywide pedestrian crossing treatments, local driving culture, and law enforcement practices. In nearly all cases, safety countermeasures improved yielding rates at a given site compared to the “before” condition (e.g., crosswalk markings only). It is suggested that practitioners supplement or replace these values with local knowledge and engineering judgement when possible. Furthermore, decisions to install a particular treatment should also consider the treatment’s effect on safety and whether site-specific conditions make the treatment inappropriate for that location. The information in Table ES-1 is proposed to be included in an update of HCM Chapter 20, and is also included in NCHRP Research Report 992: Guide to Pedestrian Analysis (Ryus et al. 2022). Effects on Pedestrian Stress Skin conductance levels and heart rates measured by biosensing wristbands were used as proxies for stress. No correlation was found between participants’ stress levels and individual crossing locations, including crossings that were study sites for the intercept survey. Instead, stress was associated with conditions along a particular facility. Higher levels of stress were generally associated with walking in proximity to collector and arterial streets and in areas with industrial and mixed (e.g., offices, retail, residential) land uses. Stress levels were relatively low in lower-density residential land uses, as well as in forest, park, and university campus environments. Participants’ mean and maximum heart rates were elevated in land contexts with mixed and industrial uses, as well as along collector roads. Participants’ heart rates were lower when walking along paths and in environments with lower motor vehicle traffic (<4,000 AADT). These findings provide a quantitative confirmation of findings from previous qualitative studies involving pedestrian satisfaction surveys. Updates to HCM Pedestrian Delay Methods The research team reviewed critiques in the literature of the HCM 6th ed. pedestrian delay methods and performed sensitivity analyses of the methods. The following issues were identified:  The pedestrian delay method for uncontrolled pedestrian crossings produces illogical results at high motorist yielding rates.

6  The pedestrian delay method for signalized crosswalks only calculates average delay for pedestrians who arrive randomly at a street corner and then make their crossing in one stage (i.e., not waiting in the median, if one exists). However, many other types of crossing scenarios exist. Furthermore, when making a multiple-stage or multiple-leg crossing, pedestrians do not arrive at the median or far street corner randomly; their arrival time is dictated by the signal timing and their walking speed.  The HCM’s “roadway crossing difficulty” factor, used to adjust the LOS of an urban street to reflect the ease of crossing the street, is insensitive to the distance between signalized crossings. Uncontrolled Crossings The research team identified the cause of the problems with the uncontrolled crossing delay procedure and developed a revised model to address the problems. The average delays estimated by the model were compared to field data collected by the project and were found to produce reasonable results. However, the size of the prediction error for crossings over right-turn lanes, left-turn lanes, or both was greater than for crossings without turn lanes, and additional research is recommended to further improve the model. The revised model is recommended to be included in an update of HCM Chapter 20. Signalized Crossings The research team prepared proposed revisions to the HCM pedestrian delay method for signalized crossings that allow the estimation of delay for one-leg, two-stage (i.e., potentially waiting in the roadway median) and two-leg, two-stage (i.e., diagonal) crossings, based on peer-reviewed research by Wang and Tian (2010) and Zhao and Liu (2017), respectively. The team also prepared an example problem for inclusion in the HCM illustrating the methods and developed a spreadsheet-based computational engine. The research team also proposed extensions to the delay methods to address the following situations:  Crosswalk closures (i.e., comparing the time to cross three intersection legs instead of one),  Exclusive pedestrian phases (e.g., Barnes dance),  Computation of pedestrian delay for any stage of a pedestrian crossing, and  Pedestrian-friendly actuated timing, where the signal provides a pedestrian phase as soon as possible after the pedestrian button is pushed. These extensions have a good theoretical basis, but additional research is needed to validate them through simulation or field data collection. Roadway Crossing Difficulty The research team prepared a proposed revision to the HCM’s pedestrian LOS method for urban streets to address the issue that diversion delay to the nearest signalized crossing will nearly always exceed the delay associated with waiting for a suitable gap to cross the street midblock. The revision draws from pedestrian perception research by Chu and Baltes (2001) that found that crossing difficulty is sensitive to segment length. The revised method is sensitive to the distance between signalized crossings, produces logical results, and retains the original method’s intent of lowering the street’s LOS when the pedestrian environment is otherwise good, but the street is hard to cross, and improving segment LOS when the pedestrian environment is poor, but the street is easy to cross.

7 Pedestrian Network QOS Background The research team investigated a method for evaluating the QOS for a pedestrian network covering a large area, ranging in size from a neighborhood or campus to an entire city. The FHWA’s Guidebook on Measuring Multimodal Network Connectivity (Twaddell et al. 2018) defines the following components of network connectivity:  Network Quality. How does the network support users or pedestrians of varying levels of experience, ages, abilities, and comfort with walking?  Network Completeness. How much of the network is available to pedestrians?  Network Density. How dense are the available links and nodes of the pedestrian network?  Route Directness. How far out of their way do users have to travel to find a facility they can or want to use?  Access to Destinations. What destinations can be reached using the network? After reviewing the literature on pedestrian network QOS, the research team determined that no one performance metric by itself could address all five components of network connectivity. However, a common measure of link- and crossing-level QOS could be used as the basis for metrics addressing four of the five components. The research team tested two performance measures from the literature capable of evaluating link- and crossing-level QOS at a planning level (i.e., using data readily available from roadway databases): the Pedestrian Level of Service (PLOS) measure presented in the Florida DOT’s 2009 Quality/Level of Service Handbook (Florida DOT 2009) and the Pedestrian Level of Traffic Stress (PLTS) measure presented in the Oregon DOT’s Analysis Procedures Manual (Oregon DOT 2019). Using a roadway inventory database covering all collector and arterial roadway segments in the state of Florida, the research team mapped and compared PLOS and PLTS. It was observed that PLOS is a highly sensitive to traffic volume and tends to produce LOS E or F results even in downtown areas with wide sidewalks and relatively low traffic speeds. In contrast, while PLTS also assessed many arterials and collectors as having stressful walking conditions (PLTS 3 or 4), it also identified lower-stress segments that were buffered from traffic, had lower traffic speeds, or both. It was concluded that PLTS did a better job of measuring pedestrian conditions and that the data needed to calculate it would likely be available from roadway inventories or other readily available sources. Recommended Approach A recommended approach to evaluating pedestrian network QOS is to start by using geographic information systems software (GIS) to determine the PLTS of each link and intersection within the study area. Next, the GIS software would be used to identify the extent of each sub-network within the study area connected by segments and crossings of a specified PLTS or better (use PLTS 2 to measure networks usable by many users, or PLTS 3 to measure basic connectivity). These subnetworks form “connectivity islands” that can be visualized as shown in Figure ES-1. The exact color shades used in the map have no meaning, other than to distinguish different isolated networks from each other in the same way colors are used to distinguish different states from each other on a map of the United States. Streets shown in grey exceed the defined level of traffic stress and thus form barriers (in the case of arterials and collectors) or indicate neighborhoods lacking basic pedestrian facilities (in the case of local streets). With such a map, it is easy to identify where (a) short connections between internal networks and (b) street crossing improvements would open up new connections and transform small islands into larger connected pedestrian networks.

8 Figure S-1. Conceptual network connectivity map. Once the subnetworks have been identified, the GIS software can then be used to quantify the following components of network connectivity:  Network quality—miles or percent of the network providing the specified PLTS or better.  Network density—average miles per sub-network (connectivity island), or number of subnetworks.  Route directness—shortest path (if one exists) along the PLTS network between a given origin and destination, compared to the straight-line (air) distance.  Access to destinations—percent of specified destinations reachable along the PLTS network from a given origin. The final component of network quality, network completeness, can be assessed separately as the percentage of the planned network that has been completed. References Alta Planning + Design. 2010. National Bicycle and Pedestrian Documentation Project: Instructions. http://bikepeddocumentation.org/application/files/3314/6671/8088/NBPD_Instructions_2010.pdf (As of December 3, 2019). Chu, X., and M. Baltes. 2001. Pedestrian Midblock Crossing Difficulty. Report No. NCTR-392-09. National Center for Transit Research, University of South Florida, Tampa, FL. Dowling, R., D. Reinke, A. Flannery, P. Ryus, M. Vandehey, T. Petritsch, B. Landis, N. Rouphail, and J. Bonneson. 2008. NCHRP Report 616: Multimodal Level of Service for Urban Streets. Transportation Research Board of the National Academies, Washington, D.C. Dowling, R., D. Reinke, A. Flannery, P. Ryus, M. Vandehey, T. Petritsch, B. Landis, and J. Bonneson. 2008. NCHRP Final Report 616: Multimodal Level of Service Analysis for Urban Streets. National Cooperative Highway Research Program, Transportation Research Board, Washington, D.C. Federal Highway Administration. 2016. Traffic Monitoring Guide. Report FHWA-PL-17-003. U.S. Department of Transportation, Washington, D.C. Florida Department of Transportation (FDOT). 2009. Quality/Level of Service Handbook. Tallahassee. Landis, B.W., V.R. Vattikuti, R.M. Ottenberg, D.S. McLeod, and M. Guttenplan. 2001. “Modeling the Roadside Walking Environment: Pedestrian Level of Service.” Transportation Research Record 1773, pp. 82–88. National Household Travel Survey. 2017. Popular Person Trips (PT) Statistics. https://nhts.ornl.gov/person-trips (As of December 3, 2019). Oregon Department of Transportation (ODOT). 2019. Analysis Procedures Manual. Version 2. Transportation Planning Analysis Unit, Salem. Ryus, P., E. Ferguson, K.M. Laustsen, R.J. Schneider, F.R. Proulx, T. Hull, and L. Miranda-Moreno. 2014. NCHRP Report 797: Guidebook on Pedestrian and Bicycle Volume Data Collection. Transportation Research Board of the National Academies, Washington, D.C. Transportation Research Board. 2016. Highway Capacity Manual (HCM), 6th ed. Washington, D.C. Wang, X., and Z. Tian. 2010. “Pedestrian Delay at Signalized Intersections with a Two-Stage Crossing Design.” Transportation Research Record No. 2173. Transportation Research Board, Washington, D.C., pp. 133–138. Zhao, J., and Y. Liu. 2017. “Modeling Pedestrian Delays at Signalized Intersections as a Function of Crossing Directions and Moving Paths.” Transportation Research Record No. 2615, pp. 95–104.