Predicting real-time traffic restoration time based on the estimated traffic state - Md Rakibul Islam; Zaheen E Muktadi Syed; Mohamed Abdel-Aty; Samiul Hasan (Transportation Research Interdisciplinary Perspectives)
Ensuring efficient traffic management after crash occurrence is crucial for minimizing fatalities, avoiding secondary crashes, reducing congestion, guiding traffic to alternative routes. Hence, it is important to know the time required to return the traffic state to a normal operating condition after such incidents in real time. In this paper, we present a new approach to predict the traffic restoration time after a crash occurrence based on the estimated traffic state using real-time data. The contribution of this study is threefold: first, the study developed models to predict the traffic state after a crash; second, the study predicted the traffic restoration time based on the estimated post-crash traffic state; third, the study applied three-step validation techniques to evaluate the performance of the developed approach and compare it with crash clearance time. To accomplish these tasks, we considered a 220 miles section of Interstate-75 of Florida, USA. Traffic, crash, weather, and emergency facility data from 2017 to 2019 were collected recording 24,448 events (4,939 crashes and 19,509 non-crash events) and 65 real-time features. A total of eight traffic state prediction models with high accuracy were developed using the XGBoost machine learning technique. The estimated traffic state was used to calculate the post-crash congestion level. Then pre-crash and post-crash congestion levels were compared to determine the time when traffic returned to normal operating conditions. The estimated traffic restoration time was validated by investigating the post-crash speed volume relationship, comparing it with the actual crash clearance time data, and finding the cosine similarity index. The proposed framework enables traffic management agencies to correctly predict the impact of a crash based on predicted traffic. To the best of the knowledge of the authors the developed approach to estimate traffic restoration time is a novel idea and has the potential to contribute to real-time traffic management after a crash.