Direct Demand Model Research Articles

Linear regression coupled Wasserstein generative adversarial network for direct demand modeling of ride-hailing trips in Chicago and Austin

ABSTRACT Accurate estimation of ride-hailing demand and understanding of its influencing factors are necessary for modern-day transportation planning. Although modern machine learning techniques improve the predictive accuracy of zone-to-zone direct demand models, they lack inference ability due to their complex model structure. Furthermore, zone-to-zone direct demand models suffer from limited sample size for machine learning due to fixed number of origin-destination (OD) pairs. To overcome these limitations, we propose a linear regression coupled Wasserstein generative adversarial network (LR-WGAN) for direct demand modeling of zone-to-zone ride-hailing trips that captures both linear explanatory components and non-linear patterns and improves the predictive accuracy by generating data to expand the OD sample size. The proposed LR-WGAN is found to significantly improve the predictive accuracy of OD ride-hailing demand in Chicago and Austin. Furthermore, the linear explanatory component of the model is utilized to draw inferences regarding the relationship between OD ride-hailing demand and predictor variables.

The use of crowdsourced mobile data in estimating pedestrian and bicycle traffic: A systematic review

To address the need for better non-motorized traffic data, policymakers and researchers are collaborating to develop new approaches and methods for estimating pedestrian and bicyclist traffic volumes. Crowdsourced mobile data, which has higher spatial and temporal coverage and lower collection costs than data collected through traditional approaches, may help improve pedestrian and bicyclist traffic estimation despite their limitations or biases. This systemic literature review documents how researchers have used crowdsourced mobile data to estimate pedestrian and bicyclist traffic volumes. We find that one source of commercial fitness application data (i.e., Strava) has been used much more frequently than other crowdsourced mobile data, and that most studies have used crowdsourced mobile data to estimate bicyclist volumes. Comparatively few studies have estimated pedestrian volumes. The most common approach to the use of crowdsourced counts is as independent variables in direct demand models. Variables constructed from crowdsourced mobile data not only have significant correlations with observed counts in statistical models but also have larger relative importance than other factors in machine learning models. Studies also show that including crowdsourced mobile data can significantly improve estimation performance. Future research directions include application of crowdsourced mobile data in more pedestrian traffic estimations, comparison of the performance of different crowdsourced mobile data, incorporation of multiple data sources, and expansion of the methods using crowdsourced mobile data for non-motorized traffic estimation.

Enhancing the Spatial Transferability of Direct Demand Models for Estimating Pedestrian Volumes at Intersections

Direct demand (DD) models are an important tool for estimating annual average daily pedestrian traffic (AADPT) for all intersections in a jurisdiction. These models associate socioeconomic and land-use variables with pedestrian exposure and allow the estimation of AADPT for sites where pedestrian counts are not (readily) available. However, some jurisdictions lack pedestrian volume counts from a sufficiently large number of intersections to develop their own DD model or do not have the institutional resources to carry out the model development. Under these circumstances, a cost-effective alternative is to use DD models that were developed in other jurisdictions. Previous research evaluated the spatial transferability of DD models in scenarios where no pedestrian counts are available (i.e., naïve transferability) and showed that this resulted in large estimation errors. This paper examines methods to improve the estimation accuracy of spatially transferred DD models by using AADPT that is readily accessible to jurisdictions (we call this local calibration). Five local calibration models were proposed and evaluated using observed field counts and synthesized counts from three jurisdictions. The best model to use is a function of the number of local jurisdiction sites for which pedestrian counts are available. When pedestrian volume is available for 10% of the sites, Model C presented the best results for the synthetic approach: an average improvement of 8.7% when comparing the locally calibrated and naïve estimates. Using real AADPTs and very limited samples for local calibration, Model C also presented the best performance: an average improvement of 35.0%.

Catchment-Area Delineation Approach Considering Travel Purposes for Station-Level Ridership Prediction Task

Station-catchment-area delineation is a key component of direct ridership models for urban rail-transport systems as it can determine the relationship between the urban-rail-transit station-level ridership and the variables within the station catchment area. The neglect of differences in the passenger-flow distribution for different travel purposes in previous studies has led to low accuracy of the obtained walk-to-station distances. Therefore, this paper proposes a station-catchment-area delineation method which is based on web map data to obtain accurate walk-to-station distances and considers differences in the distance thresholds and the ridership attraction intensity (RAI) for six travel purposes (corresponding to commercial, medical, residential, educational, administrative, and recreational land uses). In the case study, the ridership data of Xi’an Metro, the 2015 Xi’an Residential Travel Survey data, and the corresponding Gaode Map data are employed to extract passengers’ walking-distance distribution for several travel purposes to delineate the station catchment areas and build direct ridership models. Several geographically weighted regression (GWR) models are constructed to evaluate and examine the effects of the various station-catchment-area delineation methods on the model findings. The obtained results show that the proposed station-catchment-area delineation method significantly improves the ridership prediction performance compared with the traditional circular-buffer method, with the entry and exit ridership prediction accuracy improving by 3.57% and 6.65% on average, respectively. Finally, this study will guide transportation planners on how to delineate station catchment areas when constructing direct-demand models for urban rail stations.

The adverse impact of headway variability on bus transit ridership: Evidence from Bengaluru, India

This study examines the impact of bus service headway variability on bus transit ridership using direct demand models at different levels of spatial aggregation – route level and stop-route level – using transit demand and supply data from the city of Bengaluru, India. In addition, auxiliary models are developed to understand the determinants of service frequency and headway variability and to address the endogeneity of these service characteristics in the demand models. This is perhaps the first study in the public transit literature to compare and contrast the endogeneity and non-linearity effects of service frequency and headway variability in transit demand models using both a conceptual framework and empirical evidence from a large transit system. The empirical results offer evidence that variability in headways adversely impacts transit ridership and passenger-kilometres. The strength of the adverse effect increases with increasing variability. On the other hand, the influence of service frequency decreases with increasing frequency. Furthermore, it is shown conceptually and demonstrated empirically that ignoring the endogeneity of service variability results in an underestimation of its adverse effect on transit demand. On the other hand, the empirical results suggest that ignoring the endogeneity of service frequency would result in an overestimation of its beneficial effect. An important takeaway from these results and additional policy simulations is that transit agencies can potentially gain greater ridership and revenue by reducing headway variability rather than simply allocation more buses and crew to high-frequency routes.

Comparing Direct Demand Models for Estimating Pedestrian Volumes at Intersections and Their Spatial Transferability to Other Jurisdictions

Direct demand (DD) models are used to estimate pedestrian volumes at intersections as a function of readily available variables, such as land use and socioeconomic features. The objectives of this paper are: (1) to identify and qualitatively assess existing DD models in the literature; and (2) to evaluate the spatial transferability of DD models for estimating annual average daily pedestrian traffic (AADPT) at signalized intersections. Six DD models developed from jurisdictions with varying characteristics were selected for spatial transferability assessment. The models were applied to three jurisdictions (Milton, Canada; Pima County, U.S.; and Downtown Toronto, Canada) that had notable differences in the level of pedestrian activity, land use, and socioeconomics. Observed pedestrian volumes were obtained for sites in each jurisdiction. The DD models performed considerably differently across jurisdictions. Five of the models performed reasonably well for Milton, a jurisdiction that is comparable to those considered in the calibration of the selected DD models and that shares characteristics with many suburban Canadian and U.S. jurisdictions. Overall, the applications for Pima County and Downtown Toronto, which have extremely low and high pedestrian volumes, respectively, provided poor accuracy. This paper demonstrated the potential for transferring existing DD models to other jurisdictions; but also identified the clear need for further research to improve the spatial transferability of DD models.

Enhancing station level Direct-Demand models with Multi-Scalar accessibility indicators

Direct-demand models (DDM) are increasingly being used for a diversity of transit research and practice purposes. Yet few station-level DDM studies have explored the use of composite indicators of metropolitan accessibility in predicting demand. After all, provision of access to metropolitan destinations is one of the main goals of rapid-transit systems. Furthermore, to this author’s knowledge no study has explored potential interactions with local-level accessibility indicators that are typically included in station level transit DDMs. This study explores these possibilities and uses Los Angeles multimodal rapid-transit network as a representative case study of a system that operates in a dispersed agglomeration where multiple sub-centers are linked. Multi-level generalized linear models were implemented where key predictors, including stations' metropolitan- and a local-accessibility indicators are regressed onto average weekday boardings. Furthermore, more general accessibility constructs were developed via EFA and implemented in models; and parameters non-stationarity was assessed via geographically weighted regressions. Results indicate that nodal metropolitan accessibility is a significant predictor of patronage in LA’s rapid-transit network, and that its interaction with local-accessibility amplifies boardings and improves DDM models’ explanatory power. More general constructs of accessibility at metropolitan and local-scale were derived via EFA and these resulted in a more parsimonious model with equal predictive power. Land-use and transit planners would benefit from including an accessibility lens in their DDM modeling. Practical applications of these type of models include TOD scenario planning, comparative route alignment studies, system expansion studies, and for didactic purposes given the ability of accessibility measures to capture land-use/transportation interactions.

Will good public transport substitute free-floating car sharing? A case study from Copenhagen

Free-floating car sharing (FFCS) is an emerging mode of transport that enhances the mobility of individuals that do otherwise not have access to cars. This article investigates how the usage of FFCS is affected by the implementation of new high-class public transport. The analysis presents changes in spatiotemporal substitution patterns for FFCS and public transport usage after the opening of a high-class metro line in Copenhagen. A direct demand model is developed with the aim of modelling the FFCS demand and to associate this with land-use variables and public transport accessibility variables at level of zones. The results reveal that while generally increasing over time, the car sharing usage decreased in areas well-served by the new metro line after opening. Thus, it is suggested that FFCS usage and public transport are mainly substitutes and compete for the same costumer base. However, locally, there are signs that the two modes are complementary as well.

The environment around bus rapid transit stops in Indore and Ahmedabad, India

ABSTRACT An understanding of the environment around transit stops provides useful information concerning key urban design features that can promote sustainable urban development and mass transit systems. Although several researchers have analysed the associations between the built environment and transit ridership for rail-based systems, few have examined this relationship for Bus Rapid Transit systems. This research expands in India the data collection and the analysis of direct demand models I previously tested in Latin America by examining associations between built environment attributes and BRT ridership in Indore and Ahmedabad. I found positive associations for high-quality pedestrian environments, the level of non-motorized transport friendliness and the presence of affordable housing. However, I found negative associations for vacant land, low urban density, and large blocks. My findings suggest the importance of a high-quality pedestrian infrastructure and the necessity of the presence of affordable housing to make BRT systems more sustainable.

A direct demand model for bus transit ridership in Bengaluru, India

• A disaggregate direct demand model of bus transit ridership in Bengaluru, India. • Log-sum variable as an empirical strategy to relate spatially aggregated demand data to disaggregate stop-level catchment area characteristics. • Route-level service frequency is endogenous to and has a non-linear impact on stop-level ridership. • Inter-route relationships (competition and complementarity among routes) within the bus network and with the Metro network significantly influence bus ridership. • The proposed framework and empirical strategies are applicable to analyze transit ridership in other cities. This study formulates a disaggregate direct demand model of bus transit ridership while addressing the following substantive and methodological issues: (a) endogeneity and non-linearity of the influence of service frequency on ridership, (b) inter-route relationships such as competition and complementarity among routes within the bus transit network and with other transit networks (such as the metro/rail network), (c) relating spatially aggregated demand to disaggregate, stop-level catchment characteristics – although demand data are available only at an aggregation of stop-clusters, and (d) overlapping of catchment areas among closely spaced stops. The proposed model is applied to analyze bus transit ridership (boardings) during weekdays for morning peak period in Bengaluru, India. This study is among the first to develop a comprehensive direct demand model for forecasting bus transit ridership in an Indian city. Yet, the proposed conceptual and methodological framework and the findings from the study are general enough to be of use for transit planning in other cities of India and other countries. Transit agencies with spatially aggregate, fare-stage cluster-level ridership data can employ the proposed approach to examine the influence of disaggregate stop-level catchment characteristics on ridership. Additionally, transit agencies may utilise the proposed model to quantify bus ridership impacts of service network modifications, route alignments, and network connectivity/accessibility, while considering interactions with other transit networks. The empirical results suggest that while increasing service frequency increases ridership along low-frequency routes, the returns from increasing service frequency diminish as current frequency levels increase. Further, it is shown that route-level passenger kilometres, a variable commonly available with transit agencies, serves effectively as an instrument for addressing endogeneity between route-level service frequency and stop-route-level ridership.

Estimating Pedestrian Crossing Volumes and Crashes at Midblock Locations

Identifying high-risk locations is typically the necessary first task that traffic authorities consider for implementation of safety countermeasures. Before implementation of any treatment, it is necessary to understand the level of pedestrian activity at a site. Although extensive data on vehicular volumes are available, pedestrian crossing volume is seldom available, at least for midblock locations. Collecting pedestrian volume is very expensive and labor intensive. The first objective of this study is to explore options for estimating pedestrian volumes at midblock locations. The second objective is to develop a safety performance function (SPF) for estimating pedestrian crashes. To accomplish the study objectives, pedestrian safety data including crossing volumes at 64 midblock locations in Houston and San Antonio, Texas, were collected. A direct demand model that effectively estimates pedestrian volumes at midblock locations is developed. The variables that were found to be significant in influencing the pedestrian crossing volumes are: posted speed limit, number of bus stops, sidewalk width, and type of area. The SPF developed for estimating the midblock pedestrian crashes showed that the variables that most influence pedestrian safety are traffic volume and pedestrian crossing volume. The study results provide important methodological tools that can be used for proactive safety planning activities, including networkwide screening and identification of hazardous midblock locations.

Enhancing Station-Level Direct-Demand Models with Multi-Scalar Accessibility Indicators

Enhancing Station-Level Direct-Demand Models with Multi-Scalar Accessibility Indicators

Estimating Bicycle Demand in the Austin, Texas Area: Role of a Bikeability Index

This study contributes to research and practice by demonstrating the use of a composite measure, a bikeability index, to facilitate the use of and improve the performance of direct demand models for bicycle traffic, especially when only limited observation is available. The city of Austin was selected as a case study to develop the model using bicycle volume from 44 intersections. Existing knowledge and data were leveraged to develop the bikeability index that encompasses multiple built environment features (bicycle route length, comfort, connectivity, destination density, and transit coverage) to quantify the bike-friendliness of the network. In addition to the index, the demand model contained five demographic and land use variables. Some of the variables provided unique insights into bike travel behavior within the city, such as the significant and positive influence of the presence of bike signals and bike-accessible bridges. Along with the improved scalability and transferability of the modeling approach, the results and discussion are expected to facilitate and/or guide informed strategies and educational programs to increase nonmotorized activity in Austin as well as other regions.

How Do Different Treatments of Catchment Area Affect the Station Level Demand Modeling of Urban Rail Transit?

Direct demand modeling is a useful tool to estimate the demand of urban rail transit stations and to determine factors that significantly influence such demand. The construction of a direct demand model involves determination of the catchment area. Although there have been many methods to determine the catchment area, the choice of those methods is very arbitrary. Different methods will lead to different results and their effects on the results are still not clear. This paper intends to investigate this issue by focusing on three aspects related to the catchment area: size of the catchment area, processing methods of the overlapping areas, and whether to apply the distance decay function on the catchment area. Five catchment areas are defined by drawing buffers around each station with radius distance ranging from 300 to 1500 meters with the interval of 300 meters. Three methods to process the overlapping areas are tested, which are the naïve method, Thiessen polygon, and equal division. The effect of distance decay is considered by applying lower weight to the outer catchment area. Data from five cities in the United States are analyzed. Built environment characteristics within the catchment area are extracted as explanatory variables. Annual average weekday ridership of each station is used as the response variable. To further analyze the effect of regression models on the results, three commonly used models, including the linear regression, log-linear regression, and negative binomial regression models, are applied to examine which type of catchment area yields the highest goodness-of-fit. We find that the ideal buffer sizes vary among cities, and different buffer sizes do not have a great impact on the model’s goodness-of-fit and prediction accuracy. When the catchment areas are heavily overlapping, dividing the overlapping area by the number of times of overlapping can improve model results. The application of distance decay function could barely improve the model results. The goodness-of-fit of the three models is comparable, though the log-linear regression model has the highest prediction accuracy. This study could provide useful references for researchers and planners on how to select catchment areas when constructing direct demand models for urban rail transit stations.

Towards a better account and understanding of bus/rapid-transit interactions: The case of Los Angeles

Bus transfers (access) at rapid-transit stations represent more than one-third of total boardings in large, dispersed, polycentric cities in the U.S. Yet, many station-level access-mode statistics and person-trip surveys often ommitt bus access mode or confound it with walk access. The abscence of this key statistic in official survey reports results in an incomplete understanding of multimodal interactions and may mislead planners and decision-makers in crafting effective policies geared to increase rapid-transit ridership and performance. Study of eight large transit systems and further analysis of the Los Angeles case study, with its detailed 2012 bus-rail on-board survey, were conducted to improve our understanding and planning of bus/rapid-transit interactions in polycentric cities. In order to evaluate scope, share, and impact of bus feeder routes on station boardings, the author indexed bus access events in the original survey database to estimate the number of bus transfers (access) at station-level. The socio-economic profile of riders that accessed rapid transit via bus was also rendered and trip-origin spatial distributions by access mode was also identified and tabulated. In addition, the influence of different bus feeder services (e.g. local, rapid, express) on rapid-transit station boardings was assesed in multivariate generalized linear models. Descriptive and inferential statistics results indicate that in Los Angeles, >30% of patrons are estimated to access rapid-transit via a variety of bus services; bus network connectivity is highly significant in explaining rapid transit boardings with large effects comparable to key Transit Oriented Development (TOD) factors; and notable variance in the magnitude of effects exists depending on bus service type. Station-level boarding and bus-transfers predictive equations derived from multivariate regressions such as the ones illustrated in this paper, may be used for sketch planning purposes in direct-demand models. A more nuanced, periodic, and detailed compilation of statistics related to bus transfers at station-level (as well as that of other emerging and potentially disrupting access modes) would provide a better understanding and planning of large multi-modal transit systems in the U.S. It would also bring to light an important, but under-reported, set of users.

Varying influences of the built environment on daily and hourly pedestrian crossing volumes at signalized intersections estimated from traffic signal controller event data

Direct-demand models of pedestrian volumes (identifying relationships with built environment characteristics) require pedestrian data, typically from short-duration manual counts at a limited number of locations. We overcome these limitations using a novel source of pedestrian data: estimated pedestrian crossing volumes based on push-button event data recorded in traffic signal controller logs. These continuous data allow us to study more sites (1494 signalized intersections throughout Utah, US) over a much longer time period (one year) than in previous models, including the ability to detect variations across days-of-week and times-of-day. Specifically, we develop direct demand (log-linear regression) models that represent relationships between built environment variables (calculated at ¼- and ½-mile network buffers) and annual average daily and hourly pedestrian metrics. We control spatial autocorrelation through the use of spatial error models. All results confirm theorized relationships: There is more pedestrian activity at intersections with greater population and employment densities, a larger proportion of commercial and residential land uses, more connected street networks, more nearby services and amenities, and in lower-income neighborhoods with larger households. Notably, we also find relevant day-of-week and time-of-day differences. For example, schools attract pedestrian activity, but only on weekdays during daytime hours, and the coefficient for places of worship is higher in the weekend model. K-fold cross-validation results show the predictive power of our models. Results demonstrate the value of these novel pedestrian signal data for planning purposes and offer support for built environment interventions and land use policies to encourage walkable communities.

Estimation of Average Annual Daily Bicycle Counts using Crowdsourced Strava Data

Traffic volumes are fundamental for evaluating transportation systems, regardless of travel mode. A lack of counts for non-motorized modes poses a challenge for practitioners developing and managing multimodal transportation facilities, whether they want to evaluate transportation safety or the potential need for infrastructure changes, or to answer other questions about how and where people bicycle and walk. In recent years, researchers and practitioners alike have been using crowdsourced data to supplement the non-motorized counts. As such, several methods and tools have been developed. The objective of this paper is to take advantage of new data sources that provide a limited and biased sample of trips and combine them with traditional counts to develop a practical tool for estimating annual average daily bicycle (AADB) counts. This study developed a direct-demand model for estimating AADB in Texas. Data from 100 stations, installed in 12 cities across the state, was used together with the crowdsourced Strava, roadway inventory, and American Community Survey data to develop the count model for estimating AADB. The results indicate that crowdsourced Strava data is an acceptable predictor of bicycle counts, and when used with the roadway functional class and number of high-income households in a block group, can provide quite an accurate AADB estimate (29% prediction error).

Developing an urban bikeability index for different types of cyclists as a tool to prioritise bicycle infrastructure investments

Abstract This study proposes an urban Bikeability Index (BI) to assess and prioritise bicycle infrastructure investments, and in turn, improve accessibility for cyclists. The contribution of this paper is twofold. First, we construct a BI that addresses particularities of the roads in cities of the Global South and considers the preferences, perceptions and socioeconomic characteristics by type of cyclist. Second, a tool is developed for decision-makers to prioritise investments in bicycle infrastructure, considering the estimated BI and expected cyclist flows. The methodology is structured into two stages. The first stage constructs a new BI following three steps. First, we select the most important factors and components related to biking behaviour. Second, we estimate weights for selected factors and components by using a discrete choice model to analyse users’ perceptions based on results from ranking surveys. Third, we estimate the BI by type of cyclist as a weighted additive function, which are equivalent to the probabilities calculated using the discrete choice model. The second stage proposes a direct demand model to prioritise bicycle infrastructure investments based on the estimation of BIs, and expected flows of cyclists. We applied the methodology to Barranquilla, Colombia and ranked different bicycle infrastructure investments for increasing bicycle demand in the city. The results suggest that even though primary roads are currently associated with low BI values due to high rates of road accidents, they tend to be preferred by cyclists. The presence of adequate bicycle infrastructure is the most crucial factor for people who frequently cycle for sport, while traffic safety and security are the most critical factors for those who cycle to work.

A Bayesian spatial Poisson-lognormal model to examine pedestrian crash severity at signalized intersections

Reducing nonmotorized crashes requires a profound understanding of the causes and consequences of the crashes at the facility level. Generally, existing literature on bicyclists and pedestrian crash models suffers from two distinct problems: lack of exposure/volume data and inadequacy in capturing potential correlations across various crash aspects. To develop a robust framework for pedestrian crash analysis, this research employed a multivariate model across multiple pedestrian crash severities incorporating a crucial piece of information: pedestrian exposure. A multivariate spatial (conditional autoregressive) Poisson-lognormal model in a Bayesian framework was developed to examine the significant factors influencing the fatal, incapacitating injury (or suspected serious injury), and non-incapacitating injury pedestrian crashes at 409 signalized intersections in the Austin area. Various explanatory variables were used to examine the pedestrian crashes, including traffic characteristics, road geometry, built environment features, and pedestrian exposure volume at intersections, which was estimated through a direct demand model as part of the study. Model results revealed valuable insights. The superior performance of the multivariate model over the univariate model emphasized the need to jointly model multiple pedestrian crash severities. The results showed the significant positive influence of speed limit on fatal pedestrian crashes and revealed that both incapacitating and non-incapacitating injury crashes increase with increasing motorized traffic volume. Bus stop presence was found to have a negative influence on incapacitating injury crashes and a positive influence on non-incapacitating injury crashes. Moreover, the pedestrian volume at intersections positively influences non-incapacitating injury crashes. The difference in influence across crash types warrants careful and focused policy design of intersections to reduce pedestrian crashes of all severity types.

Estimating Pedestrian Volumes for Signalized and Stop-Controlled Intersections

Pedestrian fatal crashes in the U.S. have increased over the years. From 2007 to 2016, pedestrian fatalities increased 27% nationally, while all other traffic fatalities decreased 14%. On average, a pedestrian was killed every 1.5 h in traffic crashes in 2016. The Federal Highway Administration (FHWA) has been working with public agencies toward developing more data-driven approaches to identify and mitigate pedestrian safety issues. However, pedestrian exposure to risk is not readily available. The absence of pedestrian exposure data makes it challenging to identify and prioritize high-crash risk locations. Using Dallas, Texas, as a case study, researchers wanted to use exposure in relation to volumes—both vehicular and pedestrian volume—to determine pedestrian risk. Although the vehicular volume is extensively available, the pedestrian volume is seldom available. The objective of this study is to explore options for collecting or estimating pedestrian volume data, particularly at intersections with high pedestrian activity. Researchers successfully developed a direct-demand model that estimates pedestrian volumes at signalized and stop-controlled intersections. The final model showed that pedestrian volume: increases 4 times within downtown; increases 12% per school within 1 mi of intersection; increases 4.8 times per 1% increase in commercial/multi-family residential land uses within 300 ft of intersection; increases 4.7 times with presence of higher education, hospitals, or malls; and decreases 36% per 5 mph increase in the intersections’ maximum posted speed limit. This research can help advance pedestrian safety analyses by providing a method of estimating pedestrian volumes for intersections by control type, particularly when volumes are infeasible to measure.