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Leveraging connected vehicle data for near-crash detection and analysis in urban environments

Xinyu Li et al.

Journal of Transport Geography2026https://doi.org/10.1016/j.jtrangeo.2026.104645preprint
AJG 2ABDC A
Weight
0.37

Abstract

Urban traffic safety is a pressing concern in modern transportation systems, especially in rapidly expanding metropolitan areas where traffic congestion and diverse driving behavior increase the risk of traffic incidents. As situations in which vehicles come close to colliding while in motion (i.e., drivers took rapid evasive action to avoid an actual crash), near-crash events offer more sensitive insight into underlying roadway safety risks than traditional crash data. This study develops a framework integrating spatial-temporal buffering, heading algorithms, and a binary logistic regression model to identify and analyze near-crash events across San Antonio, Texas, revealing key environmental and traffic factors. Results show that over half of near-crash events involve vehicles traveling over 57.98 mph, with distances less than 20 m between them. Additionally, road segments carrying higher proportions of large or less maneuverable vehicles exhibit elevated near-crash likelihood, and this pattern is amplified on high-level roads, where speed and flow complexity elevate conflict risk. Near-crash risks are highest during weekday peak hours, particularly in downtown freeways and high-income neighborhoods. Findings suggest that strategies such as speed management, optimized routes, real-time monitoring, and adaptive traffic control based on data-driven insights can help reduce urban near-crash risks and enhance metropolitan traffic safety.

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https://doi.org/https://doi.org/10.1016/j.jtrangeo.2026.104645

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@article{xinyu2026,
  title        = {{Leveraging connected vehicle data for near-crash detection and analysis in urban environments}},
  author       = {Xinyu Li et al.},
  journal      = {Journal of Transport Geography},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.jtrangeo.2026.104645},
}

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Leveraging connected vehicle data for near-crash detection and analysis in urban environments

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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