An innovative Pedestrian-Centred Walkability Assessment.
Carlos Canas (firstname.lastname@example.org), University of MaltaShow Abstract
Maria Attard, University of Malta
Muki Haklay, University College London
Most objective walkability studies based on a description of the urban fabric, land use and street infrastructure, are making way for more subjective studies that include pedestrian-centred observations. The importance of pedestrian needs, experiences and perceptions are being increasingly recognised as crucial determinants to assess the walkability of a place and understand the underlying relationships between the public space and pedestrian behaviour. However, whilst subjective walkability studies add value and complement the objective oriented research, new insights into pedestrian perceptions and experiences are necessary. This requires a methodological shift in measuring, analysing and interpreting the walkable environment through systematic pedestrian-centred observations. A new methodological approach inspired by citizen science principles, allows pedestrians to contribute to walkability research by collecting information about the walkable environment. The purpose of this research is twofold. First, it develops a perceived walkability assessment based on subjective constructs, such as safety, comfort, pleasantness and vibrancy. Second, it identifies the most relevant components of the walkable environment according to pedestrians’ observations, while they experienced the place. The use of social media and messaging platforms, such as Facebook and WhatsApp, presents some innovative and cost-effective data collection and analysis methods to simultaneously compile structured subjective and unstructured objective georeferenced observations. As a result, this approach can determine the degree and spatial variation of perceived walkability while identifying the components and characteristics of the public space that hinder or facilitate pedestrian experiences. This in turn, can greatly assist policy in improving the walkability of a place.
Curb Ramp and Accessibility Element Upgrade Prioritization: A Literature Review and Analysis of Multi-State Survey Data
Hannah MacKnight, University of VirginiaShow Abstract
Peter Ohlms, Virginia Department of Transportation
T. Donna Chen, University of Virginia
Curb ramps are a universally beneficial element of the built environment, providing improved access for all users. The Americans with Disabilities Act (ADA) requires compliant ramps to be installed with new construction or when a facility is altered. The large quantity of ramps and other facilities that must be upgraded to achieve full compliance, coupled with limited budgets, often requires states to prioritize ramps for retrofit over time. Users with varying disabilities might prioritize curb ramp improvements differently. This study assessed the current state of the practice for prioritizing curb ramp upgrades and retrofits. A background review of national standards and guidance related to curb ramps was conducted. Prioritization processes for similar accessibility elements, including sidewalks and accessible pedestrian signals, were gathered through a literature review. To identify existing curb ramp prioritization processes, state representatives were contacted through an email survey. Americans with Disabilities Act Accessibility Guidelines and Proposed Accessibility Guidelines for Pedestrian Facilities in the Public Right-of-Way provide similar standards and guidelines for accessibility. Three studies found that pedestrians with vision disabilities found domed surfaces most detectable, although users with mobility disabilities experienced negative safety and negotiability impacts with detectable warning surfaces. Compliance with accessibility standards and citizen requests were most commonly used for prioritization at the state level; localities were more likely to consider proximity to pedestrian generators and transit. These findings provide a foundational resource for agencies that are developing or revising prioritization processes for curb ramp retrofits.
Defining Sidewalks: Improvements Inspired by Pedestrian Perceptions and Built Environment Characteristics
Jose Vallejo-Borda (email@example.com), BRT+ Centre of ExcellenceShow Abstract
German Barrero, Universidad de Los Andes
Hernan Ortiz-Ramirez, Universidad de Los Andes
Laura Barchelot-Aceros, Universitaria de Investigacion y Desarrollo
Daysy Pabón-Poches, Universitaria de Investigacion y Desarrollo
Claudia Silva-Fernández, Universitaria de Investigacion y Desarrollo
Evaluations of urban sidewalks commonly include the investigation of objective and subjective attributes as explanatory variables separately. Recent authors have proposed the simultaneous use of both objective and subjective attributes through a latent variable associated with pedestrian perceptions of sidewalk characteristics. However, researchers have not yet entirely identified all the objective physical attributes that impact this latent variable. Our aims for this paper are (i) to describe the latent variable related to the pedestrian perception about sidewalk characteristics and (ii) to identify the users and built environment attributes that explain this latent variable. We gathered both on-site objective and subjective attributes information in the city of Bogotá, Colombia, to identify and define the latent variable through an Ordered Probit Multiple Indicator and Multiple Cause (MIMIC) model. We initially identified the consistency of the latent variable. Then, we found users and built environment attributes, and their interactions with commercial land use, impacting the sidewalk characteristics perceptions that can be measured objectively. Based on our findings, it is possible to propose actions on design stages or infrastructure interventions that encourage the improvement of sidewalk characteristics to increase pedestrians’ positive perceptions of their sidewalks.
Does Weather Matter for Walking? Assessing the Impacts of Weather on Pedestrian Signal Activity at Signalized Intersections in Northern Utah
Ferdousy Runa, Pennsylvania State UniversityShow Abstract
Patrick Singleton, Utah State University
A deeper understanding of how weather variables impact pedestrian volumes is important, as active travelers are an essential part of a sustainable transportation system. Pedestrian data are limited for investigating the impacts of weather on walking levels, with most studies having data at only a couple of locations. Pedestrian actuation data (from push-buttons at traffic signals) overcomes this limitation. The Utah Department of Transportation (UDOT) archives pedestrian push button press data for use in its Automated Traffic Signal Performance Measures (ATSPM) system. In this study, we used pedestrian actuation data as a proxy for walking activity and collected weather data from the National Oceanic and Atmosphere Administration (NOAA). Using 15 months of daily time series data in Cache County, we examined the impacts of weather on pedestrian signal activity at 49 signalized intersections, using a log-linear time series regression analysis with categorical step-wise weather variables. The findings revealed that snow depth had the most frequent negative effect on walking activity. Snowfall (> 0.6 inches) also tended to have negative impacts when significant. Very hot maximum temperatures (≥ 90ºF) were associated with lower pedestrian activity at around one-third of signals. Very low minimum temperatures (< 20ºF) were also associated with lower pedestrian activity. Precipitation had a negative effect on walking levels, but at only a few signals. The study’s key findings offer implications for multimodal transportation planning (winter maintenance, shade trees, etc.) and traffic signal operations.
Estimating Real-Time Pedestrian Volume at Button-Activated Midblock Crosswalks
Xiaofeng Li (firstname.lastname@example.org), University of ArizonaShow Abstract
Yao-Jan Wu, University of Arizona
Pedestrian volume is essential for optimizing midblock pedestrian signals as well as quantifying pedestrian exposure in the safety analysis. However, all existing methods of pedestrian volume collection are either time-consuming for ground-truth data collection or costly when purchasing and maintaining sensors in a large-scale application. Therefore, this paper proposed a novel method for large-scale pedestrian estimation at midblock crosswalks using pushbutton and signal timing events. The pedestrian arrival is modeled as the Poisson process, and two submethods are developed to estimate pedestrian crossing volume at one-stage and two-stage button-activated midblock crosswalks (BAMCs). Because of missing cycles, often caused by pedestrians walking against one signal at two-stage BAMCs, the proposed submethod for one-stage BAMCs cannot be employed directly to two-stage BAMCs. The proposed peer-to-peer cycle identification algorithm is first used to identify all missing cycles. Based on event-based data and average crossing time, all missing cycles are accounted for and added by minimizing the error between estimation results of two stages. Eight days of the ground-truth pedestrian volume is manually collected from two study midblock crosswalks with pedestrian hybrid beacons. The resulting mean average errors of estimated pedestrian volume using a one-hour interval are 2.27 and 1.78 pedestrians/hour, respectively.
Exploring the Demographic and Behavioural Factors Associated with Public Support for Pedestrianisation: The Case of Edinburgh City Centre
Torran Semple, Edinburgh Napier UniversityShow Abstract
Grigorios Fountas (G.Fountas@napier.ac.uk), Edinburgh Napier University
This paper combines multiple forms of analyses and modelling approaches to investigate the demographic and behavioural factors, which have significant influence on public support for pedestrianisation. Using data from a survey that was conducted in Edinburgh, UK in early 2020, public perceptions towards pedestrianisation were investigated through statistical testing and the development of random forest and ordered probit models. The random forest approach can help identify the relative importance of explanatory variables, whereas the ordered probit models can unveil the specific effects of variables on level of support. To account for the potential effect of unobserved heterogeneity (i.e., the effect of non-observable characteristics) within respondent variables, random parameters were also considered in the ordered probit modelling framework. Initial results showed that residents are generally supportive of most issues surrounding pedestrianisation. Statistical tests reveal internal variance among the variables reflecting mode of travel and trip purpose. The random forest estimation identifies trip purpose, occupation and mode of travel as the most influential respondent variables. Ordered probit modelling (using fixed parameters and random parameters frameworks) produced two significant coefficients for mode of travel and trip purpose variables, suggesting those who are active travellers are more likely to strongly support pedestrianisation, while those who rarely visit the city centre are more likely to strongly oppose pedestrianisation. Subsequent computation of average marginal effects reveals probabilities for interior categories of the ordered dependent variable. The models are evaluated in terms of goodness-of-fit measures, before future policy recommendation are made.
Learning from Black pedestrian experiences in Portland, Oregon
Lincoln Edwards (email@example.com), University of ArizonaShow Abstract
Arlie Adkins, University of Arizona
Tara Goddard, Texas A&M University
Kimberly Kahn, Portland State University
Jaboa Lake, Portland State University
This paper presents the analysis of a series of focus groups conducted with Black pedestrians in Portland, Oregon in 2017. The aim of the research was to better understand the experiences of Black pedestrians and the unique challenges they face walking in a place that is objectively, based on traditional criteria, quite walkable. Our hope is that this paper will elevate the voices of individuals, and by extension, groups that are often overlooked in transportation decision 8 making. The research team used thematic coding to identify several key themes from the focus groups. These were: racialized and racist experiences while walking; race/gender i ntersectionality; stress related to police and policing; and neighborhood change, gentrification, and belonging. Each of these themes is described and explored, largely through participant voices. We conclude with a discussion of the findings and their relevance to transportation decision-making.
Pedestrian Traffic Signal Data Accurately Estimates Pedestrian Crossing Volumes
Patrick Singleton, Utah State UniversityShow Abstract
Ferdousy Runa, Pennsylvania State University
Existing methods of pedestrian travel monitoring are generally inefficient for collecting pedestrian data in many locations over long time periods. In this study, we demonstrate the validity of using a novel and relatively ubiquitous big data source—pedestrian data from high-resolution traffic signal controller logs—as a way to estimate pedestrian crossing volumes. Every time a person presses a pedestrian push button or a pedestrian call is registered at a signal, this information can be logged and archived. To validate these pedestrian signal data against observed pedestrian counts, we recorded over 10,000 hours of video at 90 signalized intersections in Utah, and counted around 175,000 people walking. For each hour and crossing, we compared these observed counts to measures of pedestrian activity calculated from traffic signal data, using a set of five simple piecewise linear and quadratic regression models. Overall, our results show that traffic signal big data can be successfully used to estimate pedestrian crossing volumes with good accuracy: model-predicted volumes were strongly correlated (0.84) with observed volumes and had a low mean absolute error (3.0). We also demonstrate how our models can be used to estimate annual average daily pedestrian volumes at signalized intersections and identify high pedestrian volume locations. Transportation agencies can use pedestrian signal data to help improve pedestrian travel monitoring, multimodal transportation planning, traffic safety analyses, and heath impact assessments.
Street Network Configuration and Active Transport to Primary Schools
Ali Soltani (firstname.lastname@example.org), University of South AustraliaShow Abstract
Masoud Javadpoor, Shiraz University
Fatemeh Shams, Tarbiat Modares University
Milad Medizadeh, University of Science and Technology of Lille
While previous research showed influences of built-environment attributes on children’s travel mode choice in school trips there is less knowledge about how and to what extent neighborhoods morphology including space syntax and ped-shed indices associate with active commuting of students in school trips. This study investigated the commuting behavior of students of 18 primary schools in three urban fabric zones (inner, middle, and outer suburbs) of Shiraz, Iran. Using a stratified random sampling approach, the students’ parents in the 1st to 6th grade were selected (n=1503), and they were asked to fill out a questionnaire sheet in order to report the record of school travel diary and some information about their children (individual variables) and their families (households’ variables). For quantifying street network configuration (physical variables), five well-known indices basing on space syntax theory were measured: Connectivity; Integration; Control; Intelligibility and Choice. Among the local morphology factors in the radius of 400 and 800 meters, Integration (R3) and Intelligibility have a positive effect on students' walking. This means that as the readability, continuity, and coherence of the space increase, so does the likelihood of walking. On the other hand, Control and Choice (R3) had a negative effect on students' walking. The results confirm that that improving local street network structure with the aim of increasing readability, cohesion, and spatial continuity, can increase their willingness to walk to/from school.
TOPOGRAPHICAL FACTORS IN TRAVEL MODE CHOICE: EVIDENCE FROM YOKOHAMA, JAPAN
Gen Hayauchi, Yokohama National UniversityShow Abstract
FUMIHIKO NAKAMURA, Yokohama National University
RYO ARIYOSHI, Yokohama Kokuritsu Daigaku Toshi Innovation Gakufu Kenkyuin
SHINJI TANAKA, Yokohama Kokuritsu Daigaku Toshi Innovation Gakufu Kenkyuin
SHINO MIURA, Tokyo Daigaku Daigakuin Shinryoiki Sosei Kagaku Kenkyuka
This study reveals the impacts of topography in the walk-mode choice using indices directly comparable with other factors and reveals the relationship between travel costs and topography in mode choice mechanism. Trip data and personal attributes from residents living in a hilly residential district in the Tokyo metropolitan area were collected by the authors. Through analyses on the mode of travel by focusing on railway feeder trips to the return journey home, the decreasing trend in choosing walking along uphill routes even in the same distance range was revealed, which suggests the negative impact of topography in walking. The mode choice mechanism was analyzed using multinomial logit models, and three models were employed: without topography, including topography as elevation change, and as the slope and all of them were significant. In both models analyzing topography, the negative impacts of topography were revealed. As a result of the model with the slope index, though the impact of topography was less than that of travel time and fare, there was a great correlation to that of the individual’s walking ability, which means topographical barriers have a sub-equal impact as a physical barrier in walking. Additionally, the accepted costs were revealed as follows: 241.0 JPY to avoid a 1% increase in slope and 19.9 JPY to avoid a 1-meter increase uphill elevation. Compared to the fare, it was revealed that public transit modes will work as means to avoid topographical barriers
Utilizing Pedestrian Push-Button Data for Direct Demand Models of Pedestrian Traffic Volumes at Intersections
Patrick Singleton (email@example.com), Utah State UniversityShow Abstract
Keunhyun Park, Utah State University
Doo Hong Lee, Utah State University
Traditional data collection methods for developing pedestrian volume models have limitations: short durations, few locations, or samples of the population. In this project, we overcome these limitations with a novel source of pedestrian data: estimated pedestrian crossing volumes based on push-button data recorded in traffic signal controller logs. These continuous data allow us to study more sites over a much longer time period than in previous pedestrian volume models, including variations across days-of-week and times-of-day. Specifically, we use one year of pedestrian signal data from 1,494 signalized intersections throughout Utah to develop direct demand (log-linear regression) models that represent relationships between built environment variables (measured at ¼- and ½-mile network buffers) and annual average daily and hourly pedestrian metrics. We control spatial autocorrelation in pedestrian data 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. 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 the 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.
We shape our buildings, but do they then shape us? A longitudinal analysis of pedestrian flows and development-activity in Melbourne
Andres Sevtsuk (firstname.lastname@example.org), Massachusetts Institute of Technology (MIT)Show Abstract
Rounaq Basu, Massachusetts Institute of Technology (MIT)
Bahij Chancey, Massachusetts Institute of Technology (MIT)
As demand for walkable cities increases due to their environmental, economic and quality of life benefits, there is a rising need for pedestrian traffic models in planning practice and academia. This paper offers an activity- and network-based pedestrian flow model at a property-level resolution in the City of Melbourne, which we calibrate on hourly observed pedestrian counts from automated sensors. Data on Melbourne’s urban form, land-uses, amenities and pedestrian walkways as well as climatic conditions are used to train a multi-level model and six machine learning techniques on observed pedestrian counts. Updating the built-environment data annually, we (1) test the accuracy of each technique for predicting foot-traffic on the city’s streets in subsequent years; (2) assess the contribution of built environment data on the model’s accuracy; (3) analyze the extent to which each origin-destination flow contributes to foot-traffic at three peak weekday periods; and (4) assess the frequency at which such a model should be updated to maintain accuracy. We find that annual changes in the built environment have a significant impact on the spatial distribution of Melbourne’s pedestrian flows and find the vector regression technique to offer the most accurate approach for predicting foot-traffic patterns in subsequent years. Overall distribution of walking activity during different peak periods is explained by trip rates between origin-destination pairs that vary by time of day. Our findings suggest such a model should be recalibrated at least every three years to retain sufficient predictive accuracy.
A Personalized Trip Planner for Vulnerable Road Users
Khadijeh Shirzad, North Carolina A&T State UniversityShow Abstract
Justice Darko (email@example.com), North Carolina A&T State University
Larkin Folsom, North Carolina A&T State University
Nigel Pugh, North Carolina A&T State University
Hyoshin Park, North Carolina A&T State University
Justin Owens, Virginia Polytechnic Institute and State University (Virginia Tech)
Andrew Miller, Virginia Polytechnic Institute and State University (Virginia Tech)
This research presents an adaptive and personalized routing model that enables individuals withdisabilities to define their route preferences as a part of a mobility assistant program. Theproactive approach based on anticipated user need accommodates vulnerable road users’personalized optimum dynamic routing rather than a reactive approach passively awaiting input.Most of currently available trip planners target the general public’s use of simpler route planningprioritized based on static road characteristics. These static normative approach is onlysatisfactory when conditions of intermediate nodes in the network are consistent, a constant rateof change occurs per each change of the link condition, and the same fixed routes are valid everyday regardless of the user preference. In this research, an adaptive dynamic wayfinding techniquenavigates vulnerable road users with personalized preferences and time-varying parameters. Areinforcement learning algorithm is used to compute the optimal policy.
Development and Validation of a Seven-County Regional Pedestrian Volume Model
Robert Schneider (firstname.lastname@example.org), University of Wisconsin, MilwaukeeShow Abstract
Schmitz Andrew, University of Wisconsin, Milwaukee
Xiao Qin, University of Wisconsin, Milwaukee
This study describes the development and validation of pedestrian intersection crossing volume models for the seven-county Milwaukee metropolitan region. The set of three models, among the first developed at a multi-county scale, can be used to estimate the total number of pedestrian crossings per year at four-leg intersections along state highways and other major thoroughfares. We used negative binomial regression to relate annual pedestrian volumes at 260 intersections to roadway and surrounding neighborhood socioeconomic and land use variables. The three models include seven variables that have significant positive associations with annual pedestrian volume: population density within 400m of the intersection, employment density within 400m, number of bus stops within 100m, number of retail businesses within 100m, number of restaurant and bar businesses within 100m, presence of a school within 400m, and proportion of households without a motor vehicle within 400m. Results suggest that square root or cube root transformations of continuous explanatory variables could potentially improve model fit. The models have fair accuracy, with each of the three model formulations predicting 60% or more of validation intersection counts to within half or double the observed value. Future research could address overprediction by creating new variables to better-represent the number of lanes on each intersection leg and low socioeconomic status of adjacent neighborhoods.
Static Obstructions and the Impact on Functional Sidewalk Width
Nicholas Coppola (email@example.com), University of Colorado, DenverShow Abstract
Wesley Marshall, University of Colorado, Denver
Sidewalks are fundamental to cities, but data on sidewalks has long been deficient. Advances in remote sensing, however, are beginning to increase the prevalence and accuracy of sidewalk data sets allowing researchers to estimate sidewalk width. These sidewalk data sets rarely, if ever, account for static obstructions in the sidewalk such as signs or trees. This paper asks: how much of a difference will accounting for static obstructions make when measuring the functional width of sidewalks? We used a Geographic Information System (GIS) to extract and compare the functional width of planimetric sidewalks against sidewalks when accounting for static obstructions in Cambridge, Massachusetts. For additional context, we compared the functional width results against the Americans with Disabilities Act (ADA) standards as well as national and federal sidewalk guidelines. The results suggest a significant decrease in the average functional width of sidewalks when accounting for static obstructions. More specifically, the functional width of the average sidewalk drops from 4.5-ft (1.4-m) to 3.5-ft (1.1-m). The percentage of sidewalk segments meeting the 3-ft ADA standard drops from 78% to 51% when accounting for static obstructions. For the proposed 4-ft (1.2-m) ADA standard, it plunges from 59% of sidewalk segments meeting the width threshold to 31%. The results of this paper demonstrate that not accounting for static obstructions could lead to a gross overestimation of seemingly adequate sidewalks and an unrealistic assessment of sidewalk infrastructure and pedestrian accessibility.
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