Traffic conflict assessment using macroscopic traffic flow variables: A novel framework for real-time applications. (June 2023)

• Record Type: Journal Article
• Authors: Gore, Ninad; Chauhan, Ritvik; Easa, Said; Arkatkar, Shriniwas
• Is Part Of: Accident analysis and prevention. Volume 185(2023)
• Publication Date: 2023-06
• Subjects: Traffic Conflict -- Time Spent in Conflict -- Two-dimensional vehicle-to-vehicle interactions -- Machine learning models -- Trajectory data; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2023.107020
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: Two-dimensional vehicle-to-vehicle interactions are considered for traffic conflict assessment. Time spent in Conflict (TSC) is adopted to evaluate traffic conflicts. A grouped random parameter Tobit model is applied to investigate the relationship between TSC and traffic flow characteristics. Machine learning models are employed for real-time monitoring of traffic conflicts. Abstract: The present study develops a comprehensive traffic conflict assessment framework using macroscopic traffic state variables. To this end, vehicular trajectories extracted for a midblock section of a ten-lane divided Western Urban Expressway in India are used. A macroscopic indicator termed "time spent in conflict ( TSC )" is adopted to evaluate traffic conflicts. The proportion of Stopping distance (PSD) is adopted as a suitable traffic conflict indicator. Vehicle-to-vehicle interactions in a traffic stream are two-dimensional, highlighting that the vehicles interact simultaneously in lateral and longitudinal dimensions. Therefore, a two-dimensional framework based on the influence zone of the subject vehicle is proposed and employed to evaluate TSCs . The TSCs are modeled as a function of macroscopic traffic flow variables, namely, traffic density, speed, the standard deviation in speed, and traffic composition, under a two-step modeling framework. In the first step, the TSCs are modeled using a grouped random parameter Tobit (GRP-Tobit) model. In the second step, data-driven machine learning models are employed to model TSCs . The results revealed that intermediately congested traffic flow conditions are critical for traffic safety. Furthermore, macroscopic traffic variables positively influence the value of TSC, highlighting that the TSC increases with an increase in the value of any independent variable. Among different machine learning models, the random forest (RF) model was observed as the best-fitted model to predict TSC based on macroscopic traffic variables. The developed machine learning model facilitates traffic safety monitoring in real-time.