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• W3136178184 abstract "The frequent crash occurrences have caused massive loss of lives and properties all over the world. In order to improve traffic safety, it is vital to understand the relationships between traffic operation conditions and crash risk, and further implement safety countermeasures. Emerging studies have conducted the crash risk analyses using discrete and aggregated traffic data (e.g., loop detector data, probe vehicle data), where crash events were selected as the prediction target. However, traditional traffic sensing data obtained at segment level cannot describe the detailed operation conditions for the vehicle platoons near crash locations. Thus, more microscopic and high-resolution traffic sensing data are needed. In addition, considering the random occurrence feature of crashes, high-risk events should be paid more attentions given their higher occurrence probability and consistent causations with crashes, which could proactively reduce crash likelihood. In this study, HighD Dataset from German highways was utilized for the empirical analyses. First, high-risk events were obtained using safety surrogate measures with Modified Time to Collision (MTTC) less than 2 s. Traffic operation characteristics within 5 s prior to event occurrence were extracted based on vehicle trajectory data. Then, a total of three different logistic regression models were established, which are standard logistic regression model, random-effects logistic regression (RELR) model, and random-parameter logistic regression (RPLR) model. Among which, the RPLR model was showed to have the best fitness and prediction accuracy. The results showed that the disturbed traffic flows in both longitudinal and lateral directions have positive impacts on high-risk events occurrence. Besides, too close following distance between vehicles would lead to high-risk events. Moreover, RPLR models could provide a high prediction accuracy of 97 % for 2 s ahead of the high-risk events. Finally, potential safety improvement countermeasures and future application scenarios were also discussed." @default.
• W3136178184 created "2021-03-29" @default.
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• W3136178184 date "2021-05-01" @default.
• W3136178184 modified "2023-10-02" @default.
• W3136178184 title "Trajectory data based freeway high-risk events prediction and its influencing factors analyses" @default.
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• W3136178184 doi "https://doi.org/10.1016/j.aap.2021.106085" @default.
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