Towards activity-based exposure measures in spatial analysis of pedestrian-motor vehicle crashesThis article was handled by Associate Editor Chris Lee.

BACKGROUND Although numerous efforts have been devoted to exploring the effects of area-wide factors on the frequency of pedestrian crashes in neighborhoods over the past two decades, existing studies have largely failed to provide a full picture of the factors that contribute to the incidence of zonal pedestrian crashes, due to the unavailability of reliable exposure data and use of less sound analytical methods. METHODS Based on a crowdsourced dataset in Hong Kong, we first proposed a procedure to extract pedestrian trajectories from travel-diary survey data. We then aggregated these data to 209 neighborhoods and developed a Bayesian spatially varying coefficients model to investigate the spatially non-stationary relationships between the number of pedestrian-motor vehicle (PMV) crashes and related risk factors. To dissect the role of pedestrian exposure, the estimated coefficients of models with population, walking trips, walking time, and walking distance as the measure of pedestrian exposure were presented and compared. RESULTS Our results indicated substantial inconsistencies in the effects of several risk factors between the models of population and activity-based exposure measures. The model using walking trips as the measure of pedestrian exposure had the best goodness-of-fit. We also provided new insights that in addition to the unstructured variability, heterogeneity in the effects of explanatory variables on the frequency of PMV crashes could also arise from the spatially correlated effects. After adjusting for vehicle volume and pedestrian activity, road density, intersection density, bus stop density, and the number of parking lots were found to be positively associated with PMV crash frequency, whereas the percentage of motorways and median monthly income had negative associations with the risk of PMV crashes. CONCLUSIONS The use of population or population density as a surrogate for pedestrian exposure when modeling the frequency of zonal pedestrian crashes is expected to produce biased estimations and invalid inferences. Spatial heterogeneity should also not be negligible when modeling pedestrian crashes involving contiguous spatial units.

[1]  S. Wong,et al.  Cyclists injured in traffic crashes in Hong Kong: A call for action , 2019, PloS one.

[2]  Xiaoguang Wang,et al.  Examining spatial relationships between crashes and the built environment: A geographically weighted regression approach , 2018 .

[3]  Zhan Guo,et al.  Pedestrian Environment and Route Choice: Evidence from New York City and Hong Kong , 2013 .

[4]  Tarek Sayed,et al.  Evaluating the impact of connectivity, continuity, and topography of sidewalk network on pedestrian safety. , 2017, Accident; analysis and prevention.

[5]  Pengpeng Xu,et al.  Bayesian approach to model pedestrian crashes at signalized intersections with measurement errors in exposure. , 2018, Accident; analysis and prevention.

[6]  Jean-Claude Thill,et al.  Spatial epidemiologic analysis of relative collision risk factors among urban bicyclists and pedestrians , 2012 .

[7]  Yan Song,et al.  Comparing measures of urban land use mix , 2013, Comput. Environ. Urban Syst..

[8]  Chengcheng Xu,et al.  Incorporating twitter-based human activity information in spatial analysis of crashes in urban areas. , 2017, Accident; analysis and prevention.

[9]  Shenjun Yao,et al.  Safety in numbers for cyclists beyond national-level and city-level data: a study on the non-linearity of risk within the city of Hong Kong , 2016, Injury Prevention.

[10]  Mohamed Abdel-Aty,et al.  Development of zone system for macro-level traffic safety analysis , 2014 .

[11]  Peter Congdon,et al.  A spatially adaptive conditional autoregressive prior for area health data , 2008 .

[12]  Anne T McCartt,et al.  A review of evidence-based traffic engineering measures designed to reduce pedestrian-motor vehicle crashes. , 2003, American journal of public health.

[13]  Mohamed Abdel-Aty,et al.  Macro-level pedestrian and bicycle crash analysis: Incorporating spatial spillover effects in dual state count models. , 2016, Accident; analysis and prevention.

[14]  Mohamed Abdel-Aty,et al.  A Bayesian spatial random parameters Tobit model for analyzing crash rates on roadway segments. , 2017, Accident; analysis and prevention.

[15]  Duncan Lee,et al.  A comparison of conditional autoregressive models used in Bayesian disease mapping. , 2011, Spatial and spatio-temporal epidemiology.

[16]  Mohamed Abdel-Aty,et al.  Sensitivity analysis in the context of regional safety modeling: identifying and assessing the modifiable areal unit problem. , 2014, Accident; analysis and prevention.

[17]  Helai Huang,et al.  Transportation Safety Planning Approach for Pedestrians: An Integrated Framework of Modeling Walking Duration and Pedestrian Fatalities , 2019, Transportation Research Record: Journal of the Transportation Research Board.

[18]  Rune Elvik,et al.  Factors influencing safety in a sample of marked pedestrian crossings selected for safety inspections in the city of Oslo. , 2013, Accident; analysis and prevention.

[19]  Luis Antonio Lindau,et al.  A new zone system to analyze the spatial relationships between the built environment and traffic safety , 2020 .

[20]  Bradley P. Carlin,et al.  Bayesian measures of model complexity and fit , 2002 .

[21]  Satish V. Ukkusuri,et al.  The role of built environment on pedestrian crash frequency , 2012 .

[22]  W. Y. Szeto,et al.  Accessibility to transit, by transit, and property prices: Spatially varying relationships , 2020 .

[23]  P. Speckman,et al.  Posterior distribution of hierarchical models using CAR(1) distributions , 1999 .

[24]  Lindsay S. Arnold,et al.  Association between Roadway Intersection Characteristics and Pedestrian Crash Risk in Alameda County, California , 2010 .

[25]  Chandra R. Bhat,et al.  Analytic methods in accident research: Methodological frontier and future directions , 2014 .

[26]  Max Bushell,et al.  Pedestrian crash trends and potential countermeasures from around the world. , 2012, Accident; analysis and prevention.

[27]  Anastasia Loukaitou-Sideris,et al.  Death on the Crosswalk , 2007 .

[28]  Alfred Lam,et al.  TRAVEL CHARACTERISTICS SURVEY—METHOD OF EXPANDING HOUSEHOLD INTERVIEW SURVEY DATA , 2005 .

[29]  Q. Zeng,et al.  Spatial joint analysis for zonal daytime and nighttime crash frequencies using a Bayesian bivariate conditional autoregressive model , 2018, Journal of Transportation Safety & Security.

[30]  Mohamed Abdel-Aty,et al.  In-depth approach for identifying crash causation patterns and its implications for pedestrian crash prevention. , 2020, Journal of safety research.

[31]  G. Tiwari,et al.  Correlates of fatality risk of vulnerable road users in Delhi. , 2018, Accident; analysis and prevention.

[32]  Qiang Guo,et al.  Jointly modeling area-level crash rates by severity: a Bayesian multivariate random-parameters spatio-temporal Tobit regression , 2019, Transportmetrica A: Transport Science.

[33]  Mohamed Abdel-Aty,et al.  Multi-level hot zone identification for pedestrian safety. , 2015, Accident; analysis and prevention.

[34]  Sze Chun Wong,et al.  Severity of passenger injuries on public buses: A comparative analysis of collision injuries and non-collision injuries. , 2020, Journal of safety research.

[35]  David C. Wheeler,et al.  An assessment of coefficient accuracy in linear regression models with spatially varying coefficients , 2007, J. Geogr. Syst..

[36]  Linchuan Yang,et al.  Global and local associations between urban greenery and travel propensity of older adults in Hong Kong , 2020 .

[37]  Kara M Kockelman,et al.  A Poisson-lognormal conditional-autoregressive model for multivariate spatial analysis of pedestrian crash counts across neighborhoods. , 2013, Accident; analysis and prevention.

[38]  Chris Brunsdon,et al.  Geographically Weighted Regression: The Analysis of Spatially Varying Relationships , 2002 .

[39]  Mohamed Abdel-Aty,et al.  Investigating different approaches to develop informative priors in hierarchical Bayesian safety performance functions. , 2013, Accident; analysis and prevention.

[40]  Rune Elvik,et al.  Exploring the safety in numbers effect for vulnerable road users on a macroscopic scale. , 2017, Accident; analysis and prevention.

[41]  A. Gelman.  Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper) , 2004 .

[42]  Pengpeng Xu,et al.  Occupant-level injury severity analyses for taxis in Hong Kong: A Bayesian space-time logistic model. , 2017, Accident; analysis and prevention.

[43]  Mohamed Abdel-Aty,et al.  Comparative analysis of zonal systems for macro-level crash modeling. , 2017, Journal of safety research.

[44]  Lu Bai,et al.  Exposure to pedestrian crash based on household survey data: Effect of trip purpose. , 2019, Accident; analysis and prevention.

[45]  Shenjun Yao,et al.  Pedestrian exposure measures: A time-space framework , 2014 .

[46]  Pengpeng Xu,et al.  Revisiting crash spatial heterogeneity: A Bayesian spatially varying coefficients approach. , 2017, Accident; analysis and prevention.

[47]  Linchuan Yang,et al.  A geographically weighted regression model augmented by Geodetector analysis and principal component analysis for the spatial distribution of PM2.5 , 2020 .

[48]  Rune Elvik,et al.  Safety-in-numbers: Estimates based on a sample of pedestrian crossings in Norway. , 2016, Accident; analysis and prevention.

[49]  Charles DiMaggio,et al.  Small-Area Spatiotemporal Analysis of Pedestrian and Bicyclist Injuries in New York City , 2015, Epidemiology.

[50]  Pengpeng Xu,et al.  The modifiable areal unit problem in traffic safety: Basic issue, potential solutions and future research , 2016 .

[51]  Peter Congdon.  Bayesian models for spatial incidence: a case study of suicide using the BUGS program , 1997 .

[52]  Liping Fu,et al.  Bayesian methodology to estimate and update safety performance functions under limited data conditions: a sensitivity analysis. , 2014, Accident; analysis and prevention.

[53]  Jean-Claude Thill,et al.  Analysis of traffic hazard intensity: A spatial epidemiology case study of urban pedestrians , 2011, Comput. Environ. Urban Syst..

[54]  Sylvia Richardson,et al.  Spatial Linear Models with Autocorrelated Error Structure , 1992 .

[55]  Peter Lyndon Jacobsen,et al.  Safety in Numbers for walkers and bicyclists: exploring the mechanisms , 2015, Injury Prevention.

[56]  Hong Yang,et al.  Analysis of Traffic Crashes Involving Pedestrians Using Big Data: Investigation of Contributing Factors and Identification of Hotspots , 2017, Risk analysis : an official publication of the Society for Risk Analysis.

[57]  Nicholas K Tulach,et al.  Do lower income areas have more pedestrian casualties? , 2013, Accident; analysis and prevention.

[58]  Xuesong Wang,et al.  Macro-level safety analysis of pedestrian crashes in Shanghai, China. , 2016, Accident; analysis and prevention.

[59]  Federico E Vaca,et al.  The Relationship of Pedestrian Injuries to Socioeconomic Characteristics in a Large Southern California County , 2010, Traffic injury prevention.

[60]  Norman E. Breslow,et al.  Estimation of Disease Rates in Small Areas: A new Mixed Model for Spatial Dependence , 2000 .

[61]  Peng Chen,et al.  Effects of the Built Environment on Automobile-Involved Pedestrian Crash Frequency and Risk , 2016 .

[62]  Patrick Morency,et al.  The link between built environment, pedestrian activity and pedestrian-vehicle collision occurrence at signalized intersections. , 2011, Accident; analysis and prevention.

[63]  B. Carlin,et al.  Identifiability and convergence issues for Markov chain Monte Carlo fitting of spatial models. , 2000, Statistics in medicine.

[64]  Sze Chun Wong,et al.  Rethinking safety in numbers: are intersections with more crossing pedestrians really safer? , 2017, Injury Prevention.

[65]  Becky P Y Loo,et al.  Validating crash locations for quantitative spatial analysis: a GIS-based approach. , 2006, Accident; analysis and prevention.

[66]  Philip Stoker,et al.  Pedestrian Safety and the Built Environment , 2015 .

[67]  J. Besag,et al.  Bayesian image restoration, with two applications in spatial statistics , 1991 .

[68]  Qiang Guo,et al.  The effect of road network patterns on pedestrian safety: A zone-based Bayesian spatial modeling approach. , 2017, Accident; analysis and prevention.

[69]  Mohamed Abdel-Aty,et al.  Macroscopic spatial analysis of pedestrian and bicycle crashes. , 2012, Accident; analysis and prevention.

[70]  R. Elvik,et al.  Safety-in-numbers: An updated meta-analysis of estimates. , 2019, Accident; analysis and prevention.

[71]  Andrew Gelman,et al.  General methods for monitoring convergence of iterative simulations , 1998 .

[72]  Fred L. Mannering,et al.  The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives , 2010 .

[74]  S. Sain,et al.  Environmental characteristics associated with pedestrian-motor vehicle collisions in Denver, Colorado. , 2009, American journal of public health.

[75]  Rajiv Bhatia,et al.  "Safety in Numbers" re-examined: can we make valid or practical inferences from available evidence? , 2011, Accident; analysis and prevention.

[76]  P J Gruenewald,et al.  Demographic and environmental correlates of pedestrian injury collisions: a spatial analysis. , 2000, Accident; analysis and prevention.

[77]  Rajiv Bhatia,et al.  An area-level model of vehicle-pedestrian injury collisions with implications for land use and transportation planning. , 2009, Accident; analysis and prevention.

[78]  Mohamed Abdel-Aty,et al.  Macro-level vulnerable road users crash analysis: A Bayesian joint modeling approach of frequency and proportion. , 2017, Accident; analysis and prevention.

[79]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[80]  Daniel J. Graham,et al.  Quantifying the effect of area deprivation on child pedestrian casualties by using longitudinal mixed models to adjust for confounding, interference and spatial dependence , 2013 .

[81]  Pengpeng Xu,et al.  Modeling crash spatial heterogeneity: random parameter versus geographically weighting. , 2015, Accident; analysis and prevention.

[82]  Wen Cheng,et al.  Experimental evaluation of hotspot identification methods. , 2005, Accident; analysis and prevention.

[83]  Michael Duncan,et al.  Walking, bicycling, and urban landscapes: evidence from the San Francisco Bay Area. , 2003, American journal of public health.

[84]  C. Cherry,et al.  A geographically weighted regression to estimate the comprehensive cost of traffic crashes at a zonal level. , 2019, Accident; analysis and prevention.

[85]  Mike Rees,et al.  5. Statistics for Spatial Data , 1993 .

[86]  A. Metcalfe,et al.  The influence of urban land-use on non-motorised transport casualties. , 2006, Accident; analysis and prevention.

[87]  Huiying Wen,et al.  Bayesian spatial-temporal model for the main and interaction effects of roadway and weather characteristics on freeway crash incidence. , 2019, Accident; analysis and prevention.

[88]  Chuan Ding,et al.  Non-linear effects of the built environment on automobile-involved pedestrian crash frequency: A machine learning approach. , 2018, Accident; analysis and prevention.

[89]  Piyushimita Thakuriah,et al.  Evaluating pedestrian crashes in areas with high low-income or minority populations. , 2010, Accident; analysis and prevention.

[90]  Robert B. Noland,et al.  Analysis of Pedestrian and Bicycle Casualties with Regional Panel Data , 2004 .

[91]  Joshua Stipancic,et al.  Pedestrian safety at signalized intersections: Modelling spatial effects of exposure, geometry and signalization on a large urban network. , 2019, Accident; analysis and prevention.

[92]  Rune Elvik,et al.  Safety-in-numbers: A systematic review and meta-analysis of evidence , 2017 .

[93]  D. Graham,et al.  Spatial Variation in Road Pedestrian Casualties: The Role of Urban Scale, Density and Land-use Mix , 2003 .

[94]  Shenjun Yao,et al.  Measures of activity-based pedestrian exposure to the risk of vehicle-pedestrian collisions: space-time path vs. potential path tree methods. , 2015, Accident; analysis and prevention.

[95]  S. Srinivasan,et al.  Planning-Level Model for Assessing Pedestrian Safety , 2014 .

[96]  Yi Zhang,et al.  The Relationship between Community Design and Crashes Involving Older Drivers and Pedestrians , 2013 .

[97]  T. Brijs,et al.  Assessing the impacts of enriched information on crash prediction performance. , 2019, Accident; analysis and prevention.

[98]  M. Abdel-Aty,et al.  Explore effects of bicycle facilities and exposure on bicycle safety at intersections , 2020, International Journal of Sustainable Transportation.

[99]  Peter Congdon,et al.  Applied Bayesian Modelling , 2003 .

[100]  Satish V. Ukkusuri,et al.  Random Parameter Model Used to Explain Effects of Built-Environment Characteristics on Pedestrian Crash Frequency , 2011 .

[101]  George Yannis,et al.  A review of spatial approaches in road safety. , 2020, Accident; analysis and prevention.

[102]  Stephen J Mooney,et al.  Use of Google Street View to Assess Environmental Contributions to Pedestrian Injury. , 2016, American journal of public health.

[103]  L. Steg,et al.  Promoting physical activity and reducing climate change: opportunities to replace short car trips with active transportation. , 2009, Preventive medicine.

[104]  Mohamed Abdel-Aty,et al.  Macroscopic hotspots identification: A Bayesian spatio-temporal interaction approach. , 2016, Accident; analysis and prevention.