原文传递
Modeling vehicle collision instincts over road midblock using deep learning
| 题名： | Modeling vehicle collision instincts over road midblock using deep learning |
| 正文语种： | eng |
| 作者： | Shubham Patil;Narayana Raju;Shriniwas S. Arkatkar;Said Easa |
| 作者单位： | Department of Civil Engineering Sardar Vallabhbhai National Institute of Technology Surat Gujarat India;Transport & Planning Department CiTG Delft University of Technology (TU Delft) Delft Netherlands;Department of Civil Engineering Sardar Vallabhbhai National Institute of Technology Surat Gujarat India;Department of Civil Engineering Ryerson University Toronto Canada |
| 关键词： | Homogeneous traffic; mixed traffic; time-to-collision; traffic safety; trajectory data |
| 摘要： | The present research aims to understand the safety over the midblock road sections and proposes a safety framework using the conventional Time to Collision (TTC) measure. In the present work, the safety framework underlines a supporting structure connecting the actions of the surrounding vehicles and assesses the collisions changes for a given subject vehicle. The Framework principally checks the likelihood of lateral overlap and the time gap between the subject vehicle and its surrounding vehicles. Later, for the trajectory data development, an automated trajectory data development tool is built with the help of image processing for generating the trajectory data from the study sections. In supporting the developed safety framework, the lateral movement of the vehicles is modeled precisely with the help of deep learning. Further, the conceptualized safety framework is tested with the developed trajectory data sets over the study sections. From the results, it is observed that, in mixed traffic, the collision points are over the entire geometry of the study section. In the case of homogeneous traffic, the collision instincts are clustered toward the median lanes. With the advancement of technology, trajectory data development can be a real-time exercise, and the safety framework can be implemented. By applying the study methodology, the critical spots over the road network can be flagged for better treatment and improve safety over the sections. |
| 出版年： | 2023 |
| 期刊名称： | Journal of Intelligent Transportation Systems |
| 卷： | 27 |
| 期： | 1/6 |
| 页码： | 257-271 |
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