Real-Time Crash Risk Analysis of Urban Arterials Incorporating Bluetooth, Weather, and Adaptive Signal Control Data

TL;DR

This study develops Bayesian models incorporating Bluetooth, weather, and adaptive signal data to analyze real-time crash risk on urban arterials, revealing key factors like speed and rain, and demonstrating the model's effectiveness for traffic safety management.

Contribution

It introduces a Bayesian conditional logistic model for real-time crash risk analysis on urban arterials, integrating Bluetooth, weather, and adaptive signal control data, which is novel compared to prior freeway-focused studies.

Findings

Average speed, upstream volume, and rainy weather significantly affect crash risk.

The 5-10 minute data interval model performs best for crash prediction.

Bayesian conditional logistic model outperforms other Bayesian models.

Abstract

Real-time safety analysis has become a hot research topic as it can reveal the relationship between real-time traffic characteristics and crash occurrence more accurately, and these results could be applied to improve active traffic management systems and enhance safety performance. Most of the previous studies have been applied to freeways and seldom to arterials. Therefore, this study attempts to examine the relationship between crash occurrence and real-time traffic and weather characteristics based on four urban arterials in Central Florida. Considering the substantial difference between the interrupted traffic flow on urban arterials and the free flow on freeways, the adaptive signal phasing was also introduced in this study. Bayesian conditional logistic models were developed by incorporating the Bluetooth, adaptive signal control, and weather data, which were extracted for a period of 20 minutes (four 5-minute interval) before the time of crash occurrence. Model comparison results indicate that the model based on 5-10 minute interval dataset is the most appropriate model. It reveals that the average speed, upstream volume, and rainy weather indicator were found to have significant effects on crash occurrence. Furthermore, both Bayesian logistic and Bayesian random effects logistic models were developed to compare with the Bayesian conditional logistic model, and the Bayesian conditional logistic model was found to be much better than the other two models. These results are important in real-time safety applications in the context of Integrated Active Traffic Management.