Traffic Crash Evolution Characteristic Analysis and Spatiotemporal Hotspot Identification of Urban Road Intersections

Abstract

Road traffic safety is a key concern of transport management as it has severely restricted Chinese economic and social development. With the objective to prevent and reduce road traffic crashes, this study proposes a comprehensive spatiotemporal analysis method that integrates the time-space cube analysis, spatial autocorrelation analysis, and emerging hot spot analysis for exploring the traffic crash evolution characteristics and identifying crash hot spots. These analyses are all conducted by the corresponding toolbox of ArcGIS 10.5. Then, a small sized-city of China (i.e., Wujiang) is selected as the case study, and the historical traffic crash data occurring at the road intersections of Wujiang for the year 2016 are analyzed by the proposed method. The analysis process identifies the high incidence locations of traffic crashes, then presents the spatial change trend and statistical significance of the crash locations. Finally, different types of crash hotspots, as well as their evolution patterns over time, are determined. The results illustrate that the traffic crash hotspots of road intersections are primarily distributed in the Northeast area of Wujiang’s major urban area, while the crash cold spots are concentrated in the Southwest of Wujiang, which points out the direction for crash prevention. In addition, the finding has a potential engineering application value, and it is of great significance to the sustainable development of Wujiang.

Factors Contributing to the Relationship between Driving Mileage and Crash Frequency of Older Drivers

“…The more roads that contain more vehicles, the greater the likelihood of collision. Previous studies [21][22][23][24][25][26][27][28][29][30][31][32][33]39] reported similar results. According to Kang [47], bus stops can substantially increase pedestrian volume, as well.…”

“…As a result, we adopted a negative binomial model to control for the problems of overdispersion (presence of greater variability) or under dispersion (presence of less variability) based on the observed variance. This model was the preferred approach used in other crash frequency studies [24,25,[30][31][32][33][34][35][36][37][38]. The Poisson model proved inadequate, but the negative binomial model proved to be appropriate.…”

“…To check for the existence of spatial autocorrelation (SA), the Moran's I test has been widely used [30] and is used here to evaluate SA. In building the neighborhood matrix W, two locations (TAZs) that share an edge or a corner are considered neighbors, based on the queen adjacency of the first order.…”

Multi-View Interactive Visual Exploration of Individual Association for Public Transportation Passengers

“…W. Yu et al analyzed the changes of urban community structure based on taxi GPS trajectory data [9]. Z. Cheng et al, based on urban road intersection data, analyze the evolution characteristics of traffic accidents and identify spatiotemporal hotspots [10].…”

Analysis of Accident Severity for Curved Roadways Based on Bayesian Networks

“…The study discovered that the road junctions are the critical accident-prone areas [10]. Cheng, Zu and Lu [11] conducted a comprehensive spatiotemporal model for investigating the traffic crash evolution characteristics and labelling crash hot spots in Wujiang, Suzhou, China. Seven factors (crash occurrence at night, the existence of three or fewer traffic lanes in a freeway section, etc.)…”