Title

Learning Semantic Features For Action Recognition Via Diffusion Maps

Keywords

Bayesian analysis; Bicycle crashes; Pedestrian crashes; Spatial analysis; Traffic Analysis Zones

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. © 2011 Elsevier Ltd. All rights reserved.

Publication Date

3-1-2012

Publication Title

Computer Vision and Image Understanding

Volume

116

Number of Pages

361-377

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.cviu.2011.08.010

Socpus ID

84856112175 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84856112175

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