Abstract
Urban road intersections are one of the key components of road networks. Due to complex and diverse traffic conditions, traffic conflicts occur frequently. Accurate traffic conflict detection allows improvement of the traffic conditions and decreases the probability of traffic accidents. Many time-based conflict indicators have been widely studied, but the sizes of the vehicles are ignored. This is a very important factor for conflict detection at urban intersections. Therefore, in this paper we propose a novel time difference conflict indicator by incorporating vehicle sizes instead of viewing vehicles as particles. Specially, we designed an automatic conflict recognition framework between vehicles at the urban intersections. The vehicle sizes are automatically extracted with the sparse recurrent convolutional neural network, and the vehicle trajectories are obtained with a fast-tracking algorithm based on the intersection-to-union ratio. Given tracking vehicles, we improved the time difference to the conflict metric by incorporating vehicle size information. We have conducted extensive experiments and demonstrated that the proposed framework can effectively recognize vehicle conflict accurately.
Highlights
Traffic environments at urban road intersections are complex and diverse due to frequent traffic conflicts between vehicles [1]
The safety of intersections in urban road networks is important because they are key nodes in the urban road network, where all types of traffic participants must meet and disperse
Many countries have used the Traffic Conflict Technique (TCT) instead of traffic accident data for safety analysis because of its reliability, validity, and low cost compared to traditional methods [6]
Summary
Traffic environments at urban road intersections are complex and diverse due to frequent traffic conflicts between vehicles [1]. Traditional safety analysis approaches are mostly based on accident data. They are effective, they suffer from a small number of samples due to the random nature of the accidents [3]. They suffer from a small number of samples due to the random nature of the accidents [3] Another direction is to exploit the technical theory of traffic conflict to conduct safety studies at intersections [4]. Many countries have used the TCT instead of traffic accident data for safety analysis because of its reliability, validity, and low cost compared to traditional methods [6]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
