Title

Macroscopic spatial analysis of pedestrian and bicycle crashes

Authors

Authors

C. Siddiqui; M. Abdel-Aty;K. Choi

Comments

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Abbreviated Journal Title

Accid. Anal. Prev.

Keywords

Pedestrian crashes; Bicycle crashes; Spatial analysis; Traffic Analysis; Zones; Bayesian analysis; INJURY COLLISIONS; HETEROGENEITY; Ergonomics; Public, Environmental & Occupational Health; Social; Sciences, Interdisciplinary; Transportation

Abstract

This study investigates the effect of spatial correlation using a Bayesian spatial framework to model pedestrian and bicycle crashes in Traffic Analysis Zones (TAZs). Aggregate models for pedestrian and bicycle crashes were estimated as a function of variables related to roadway characteristics, and various demographic and socio-economic factors. It was found that significant differences were present between the predictor sets for pedestrian and bicycle crashes. The Bayesian Poisson-lognormal model accounting for spatial correlation for pedestrian crashes in the TAZs of the study counties retained nine variables significantly different from zero at 95% Bayesian credible interval. These variables were - total roadway length with 35 mph posted speed limit, total number of intersections per TAZ, median household income, total number of dwelling units, log of population per square mile of a TAZ, percentage of households with non-retired workers but zero auto, percentage of households with non-retired workers and one auto, long term parking cost, and log of total number of employment in a TAZ. A separate distinct set of predictors were found for the bicycle crash model. In all cases the Bayesian models with spatial correlation performed better than the models that did not account for spatial correlation among TAZs. This finding implies that spatial correlation should be considered while modeling pedestrian and bicycle crashes at the aggregate or macro-level. (C) 2011 Elsevier Ltd. All rights reserved.

Journal Title

Accident Analysis and Prevention

Volume

45

Publication Date

1-1-2012

Document Type

Article

Language

English

First Page

382

Last Page

391

WOS Identifier

WOS:000301081700044

ISSN

0001-4575

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