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. 2017 Dec;94(6):855-868.
doi: 10.1007/s11524-017-0200-z.

Neighborhood Influences on Vehicle-Pedestrian Crash Severity

Alireza Toran Pour  1 , Sara Moridpour  2 , Richard Tay  3 , Abbas Rajabifard  4
Affiliations

Neighborhood Influences on Vehicle-Pedestrian Crash Severity

Alireza Toran Pour et al. J Urban Health. 2017 Dec.

Abstract

Socioeconomic factors are known to be contributing factors for vehicle-pedestrian crashes. Although several studies have examined the socioeconomic factors related to the location of the crashes, limited studies have considered the socioeconomic factors of the neighborhood where the road users live in vehicle-pedestrian crash modelling. This research aims to identify the socioeconomic factors related to both the neighborhoods where the road users live and where crashes occur that have an influence on vehicle-pedestrian crash severity. Data on vehicle-pedestrian crashes that occurred at mid-blocks in Melbourne, Australia, was analyzed. Neighborhood factors associated with road users' residents and location of crash were investigated using boosted regression tree (BRT). Furthermore, partial dependence plots were applied to illustrate the interactions between these factors. We found that socioeconomic factors accounted for 60% of the 20 top contributing factors to vehicle-pedestrian crashes. This research reveals that socioeconomic factors of the neighborhoods where the road users live and where the crashes occur are important in determining the severity of the crashes, with the former having a greater influence. Hence, road safety countermeasures, especially those focussing on the road users, should be targeted at these high-risk neighborhoods.

Keywords: Boosted regression tree; Neighborhood socioeconomic influences; Neighborhood where crash occur; Neighborhoods where road users live; Vehicle-pedestrian crashes.

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Figures

Fig. 1
Fig. 1
Melbourne metropolitan area and its suburbs
Fig. 2
Fig. 2
Methodology that is applied in this research
Fig. 3
Fig. 3
20 top relative important variables in BDT model for vehicle-pedestrian crashes
Fig. 4
Fig. 4
Roadway and traffic-related factors. a Distance from public transport stops. b Light condition. c Road gradient. d Number of lanes
Fig. 5
Fig. 5
Road users’ characteristics (population over 75 years of age)
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Fig. 6
Pedestrian residency neighborhood factors. a Bus commutes. b Suburbs median age. c Born in UK
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Fig. 7
Drivers residency neighbourhood factors. a Tertiary education. b Indigenous people. c Bus commutes. d Born in UK
Fig. 8
Fig. 8
Crash neighborhood factors. a Population density. b Other type of education. c Tertiary education
See this image and copyright information in PMC

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