Utilizing Bluetooth and Adaptive Signal Control Data for Urban Arterials Safety Analysis

TL;DR

This study develops Bayesian models using Bluetooth, adaptive signal control, and weather data to analyze real-time crash risk on urban arterials, highlighting key traffic and weather factors influencing safety.

Contribution

It introduces a novel Bayesian modeling approach incorporating adaptive signal data for urban arterial crash risk analysis, which is less explored compared to freeways.

Findings

Average speed and rainy conditions significantly affect crash risk.

Models with 5-10 minute data windows perform best.

Bayesian random parameters models outperform traditional models.

Abstract

Real-time safety analysis has become a hot research topic as it can more accurately reveal the relationships between real-time traffic characteristics and crash occurrence, 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. 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 urban arterials and the access controlled freeways, the adaptive signal phasing data was introduced in addition to the traditional traffic data. 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 intervals) before the time of crash occurrence. Model comparison results indicated that the model based on 5-10 minute interval dataset performs the best. It revealed that the average speed, upstream left-turn volume, downstream green ratio, and rainy indicator were found to have significant effects on crash occurrence. Furthermore, both Bayesian random parameters logistic and Bayesian random parameters conditional logistic models were developed to compare with the Bayesian conditional logistic model, and the Bayesian random parameters conditional logistic model was found to have the best model performance in terms of the AUC and DIC values. These results are important in real-time safety applications in the context of Integrated Active Traffic Management.