Evaluating Crash Risk in Urban Areas Based on Vehicle and Pedestrian Modeling

Abstract

This article presents a method for investigating the spatial distribution of vehicle and pedestrian traffic crashes relative to the volume of vehicle and pedestrian movement in urban areas. This method consists of two phases. First, vehicle and pedestrian traffic volumes on the street network are modeled using a space syntax configurational analysis of the network, land use data, and observed traffic data. Second, crash prediction models are fitted to the traffic crash data, using negative binomial regression models and based on traffic volume estimates and street segment lengths. The method was applied in two areas in Tel Aviv, Israel, which differ in their morphological and traffic characteristics. The case‐studies illustrated the method's capability in identifying hazardous locations on the network and examining relative crash risks. The analysis shows that an increase in vehicle or pedestrian traffic volume tends to be associated with a decrease in relative crash risk. Moreover, the spatial patterns of relative crash risks are associated with the design characteristics of urban space: areas characterized by dense street networks encourage more walking, and are generally safer for pedestrians, while those with longer street segments encourage more driving, are less safe for pedestrians, but safer for vehicles.

Speeds of Young E-Cyclists on Urban Streets and Related Risk Factors: An Observational Study in Israel

“…Based on the study database [34] and the familiarity with common street settings in Israeli cities, for the speed survey, five types of urban streets were defined, such as: (1) undivided roads in city centers, (2) undivided roads in residential areas, (3) pedestrian zone streets, (4) divided roads in city centers, and (5) divided roads in residential areas. Furthermore, in selecting urban streets for speed observations, the preference was given to collector streets versus local ones since collector streets are characterized by higher vehicle traffic volumes and pedestrian activities and, typically, mixed land uses, and are frequently associated with road safety problems in urban areas of the country [36,37]. The presence of pedestrians and higher traffic volumes on the street increases the potential for conflicts between e-cyclists and other road users and thus such streets are more relevant for observational studies seeking to improve urban road safety.…”

Predictive spatial network analysis for high-resolution transport modeling, applied to cyclist flows, mode choice, and targeting investment

“…It has a history of use in the production of models to fit pedestrian and vehicle flows (Cooper, 2015;Haworth, 2014;Hillier & Iida, 2005;Jayasinghe, 2017;Lowry, 2014;Omer et al, 2017;Patterson, 2016;Serra & Hillier, 2017;Turner, 2007) but is not used in mainstream motor vehicle transport modeling for which the four-step model (Ort uzar & Willumsen, 2011) is ubiquitous. Due to their simplified nature, SpNA models have also been used in epidemiology to quantify built environment factors for individuals (Cooper, Fone, & Chiaradia, 2014;Fone et al, 2012;Sarkar et al, 2015;Sarkar, Gallacher, & Webster, 2013;Sarkar, Webster, & Gallacher, 2014).…”

Crash Patterns in the COVID-19 Pandemic: The Tale of Four Florida Counties

“…The illustration of the study area is presented in Figure 1. We selected these counties due to their distinct population characteristics which can help examine the effects of demographics on the spatial patterns of the crash densities [53]. Table 1 summarizes the statistical characteristics for each county, including demographic characteristics, transportation-related factors, college/university enrollment, and curfew policy details.…”