Exploring the Spatiotemporal Characteristics and Causes of Rear-End Collisions on Urban Roadways

Abstract

Rear-end collisions are caused by drivers misjudging urgent risks while following vehicles ahead in most cases. However, compared with other accident types, rear-end collisions have higher preventability. This study aims to reveal the prone segments and hours of rear-end collisions. First, we extracted 1236 cases from traffic accident records in Harbin from 2015 to 2019. These accidents are classified as property damage accidents, injury accidents and fatal accidents according to the collision severity. Second, density analysis in GIS was used to demonstrate the spatial distribution of rear-end collisions. The collision spots considering the density and severity were visually displayed. We counted the hourly and seasonal distribution characteristics according to the statistical data. Finally, LightGBM and random forest classifier models were used to evaluate the substantial factors affecting accident severity. The results have potential practical value in rear-end collision warning and prevention.

The Effect of the COVID-19 Pandemic on the Distribution of Traffic Accident Hotspots in New York City

“…Traffic accident hotspots refer to locations with high-density clusters of traffic accidents. In recent years, based on substantial volumes of traffic accident data and with the help of GIS platforms, in-depth studies on urban traffic accident hotspots have been conducted [9][10][11][12][13]. GIS-based traffic accident hotspot identification methods include the kernel density estimation method, the local Moran's I, the Getis-Ord Gi* method, and so on [14][15][16].…”

Does Weisburd's law of crime concentration apply to traffic crashes? Implications for policing and traffic law enforcement

“…Extensive transportation research has already found that traffic accidents and crashes occur more frequently in certain places and times and has also explored the behavioral routines and environmental causes and correlates of traffic crashes. Some of these studies focus on larger place and time units, such as higher-density city centers (Alkhadour et al, 2021;Dezman et al, 2016;Rahman et al, 2020;Zhang et al, 2022) or during congested traffic hours (Li et al, 2020;Plug et al, 2011;Zhang et al, 2022). However, a large body of transportation research examines specific behavioral and environmental factors contributing to crashes.…”