Construction and Simulation of Rear-End Conflicts Recognition Model Based on Improved TTC Algorithm

Ge, Huimin; Xia, Ran; Sun, Hui; Yang, Yousen; Huang, Mingyue · 2019 · DOAJ

DOI: 10.1109/ACCESS.2019.2937898

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Summary

This study addresses the limitations of traditional surrogate safety measures in identifying rear-end conflicts within freeway work zones. The primary motivation is that the standard Time to Collision (TTC) metric fails to detect potential risks when the following vehicle’s speed is lower than or equal to the leading vehicle’s speed, a common scenario in work zones where leading vehicles decelerate due to speed limits. To resolve this, the authors propose a new metric, Time to Collision in the Work Zone (WTTC), which accounts for the influence of speed limit signs and the resulting deceleration of leading vehicles. The methodology involves constructing a mathematical model for WTTC based on kinematic equations that consider two collision outcomes: one where the leading vehicle collides before reaching the speed limit, and another where it collides after decelerating to the limit. The study utilizes field data collected from a one-way closed work zone on the Hefei Ring Expressway, employing high-definition cameras and portable laser survey systems to record vehicle positions, speeds, headways, and crash occurrences. To determine the optimal threshold for WTTC, the authors calculated the Pearson correlation coefficient between Road Risk (derived from actual crash counts) and Conflict Risk (derived from surrogate measures). The model was further validated using micro-traffic simulation software (VISSIM) to test performance under varying speed limit conditions. The results indicate that the WTTC model significantly outperforms traditional TTC in identifying rear-end conflicts. The optimal threshold for WTTC was determined to be 2 seconds, yielding a correlation coefficient of 0.45 between road risk and conflict risk, compared to 2.3 seconds for TTC. Validation against observed crash data showed that the WTTC model achieved a recognition accuracy of 51.73%, which is 24.28% higher than the accuracy of TTC. Furthermore, when simulating a lower speed limit of 60 km/h, the WTTC model’s accuracy increased to 77.84%, while TTC remained at 41.28%. Error tests, including Root Mean Square Error (RMSE), confirmed that WTTC identification counts were closer to actual crash counts than those produced by TTC. The significance of this research lies in providing a more accurate tool for evaluating collision risks in freeway work zones, particularly in car-following scenarios where traditional metrics fail. By capturing potential conflicts that TTC overlooks, the WTTC model offers a more comprehensive assessment of traffic safety. This improved identification capability can assist traffic management departments in better understanding risk levels in work zones, thereby facilitating strategies to enhance road safety and traffic capacity during maintenance operations.

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• time to collision looming