Random Parameter Model Used to Explain Effects of Built-Environment Characteristics on Pedestrian Crash Frequency

Pedestrian safety has been a major concern for megacities such as New York City. Although pedestrian fatalities show a downward trend, these fatalities constitute a high percentage of overall traffic fatalities in the city. Data from New York City were used to study the factors that influence the frequency of pedestrian crashes. Specifically, a random parameter, negative binomial model was developed for predicting pedestrian crash frequencies at the census tract level. This approach allows the incorporation of unobserved heterogeneity across the spatial zones in the modeling process. The influences of a comprehensive set of variables describing the sociodemographic and built-environment characteristics on pedestrian crashes are reported. Several parameters in the model were found to be random, which indicates their heterogeneous influence on the numbers of pedestrian crashes. Overall, these findings can help frame better policies to improve pedestrian safety.

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

[2]  Kelly J Clifton,et al.  An examination of the environmental attributes associated with pedestrian-vehicular crashes near public schools. , 2007, Accident; analysis and prevention.

[3]  Paul J Gruenewald,et al.  An ecological study of the locations of schools and child pedestrian injury collisions. , 2004, Accident; analysis and prevention.

[4]  D F Preusser,et al.  IDENTIFICATION OF ALCOHOL-PEDESTRIAN CRASH PROBLEMS AMONG SELECTED RACIAL/ETHNIC GROUPS , 1997 .

[5]  Robert B. Voas,et al.  Ethnicity and alcohol-related fatalities : 1990 to 1994 , 2000 .

[6]  Fred L Mannering,et al.  A note on modeling vehicle accident frequencies with random-parameters count models. , 2009, Accident; analysis and prevention.

[7]  M J Cynecki,et al.  A REVIEW OF PEDESTRIAN SAFETY RESEARCH IN THE UNITED STATES AND ABROAD , 2004 .

[8]  K. Train.  Halton Sequences for Mixed Logit , 2000 .

[9]  Mohamed Abdel-Aty,et al.  Comprehensive analysis of vehicle-pedestrian crashes at intersections in Florida. , 2005, Accident; analysis and prevention.

[10]  Charles V. Zegeer,et al.  Safety Effects of Marked Versus Unmarked Crosswalks at Uncontrolled Locations: Analysis of Pedestrian Crashes in 30 Cities , 2001 .

[11]  David R. Ragland,et al.  Traffic Safety in Communities of Color , 2003 .

[12]  Viola Cavallo,et al.  Age-related differences in street-crossing decisions: the effects of vehicle speed and time constraints on gap selection in an estimation task. , 2007, Accident; analysis and prevention.

[13]  C. Bhat.  Simulation estimation of mixed discrete choice models using randomized and scrambled Halton sequences , 2003 .

[14]  Nadine Schuurman,et al.  Pedestrian injury and the built environment: an environmental scan of hotspots , 2009, BMC public health.

[15]  M Stevenson.  Building safer environments: injury, safety, and our surroundings , 2006, Injury Prevention.

[16]  Anne Vernez Moudon,et al.  Risk of Pedestrian Collision Occurrence , 2008 .

[17]  A. Herring,et al.  Exploring Associations between Physical Activity and Perceived and Objective Measures of the Built Environment , 2007, Journal of Urban Health.

[18]  N. Levine,et al.  Spatial analysis of Honolulu motor vehicle crashes: II. Zonal generators. , 1995, Accident; analysis and prevention.

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

[20]  Simon Washington,et al.  Statistical and Econometric Methods for Transportation Data Analysis (2nd Edition) , 2010 .

[21]  S. Walgren.  USING GEOGRAPHIC INFORMATION SYSTEM (GIS) TO ANALYZE PEDESTRIAN ACCIDENTS , 1998 .

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

[23]  Per E Gårder,et al.  The impact of speed and other variables on pedestrian safety in Maine. , 2004, Accident; analysis and prevention.

[24]  P. Dischinger,et al.  Social, behavioral and driving characteristics of injured pedestrians: a comparison with other unintentional trauma patients. , 2007, Accident; analysis and prevention.

[25]  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.

[26]  J. Guralnik,et al.  Characteristics of older pedestrians who have difficulty crossing the street. , 1997, American journal of public health.

[27]  I Made Brunner,et al.  Influence of Land Use, Population, Employment, and Economic Activity on Accidents , 2006 .

[28]  C. Bay,et al.  Pedestrian fatalities by race/ethnicity in Arizona, 1990-1996. , 2002, American journal of preventive medicine.

[29]  Ross H Day,et al.  Crossing roads safely: an experimental study of age differences in gap selection by pedestrians. , 2005, Accident; analysis and prevention.

[30]  Eric Yamashita,et al.  Motor Vehicle Crashes and Land Use: Empirical Analysis from Hawaii , 2002 .

[31]  John L. Renne,et al.  Socioeconomics of Urban Travel: Evidence from the 2001 NHTS , 2003 .

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

[33]  L H Nitz,et al.  Spatial analysis of Honolulu motor vehicle crashes: I. Spatial patterns. , 1995, Accident; analysis and prevention.

[34]  S. Washington,et al.  Statistical and Econometric Methods for Transportation Data Analysis , 2010 .