Research Article
Unpaired Learning of Roadway-Level Traffic Paths from Trajectories
@INPROCEEDINGS{10.1007/978-3-030-92635-9_11, author={Weixing Jia and Guiling Wang and Xuankai Yang and Fengquan Zhang}, title={Unpaired Learning of Roadway-Level Traffic Paths from Trajectories}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 17th EAI International Conference, CollaborateCom 2021, Virtual Event, October 16-18, 2021, Proceedings, Part I}, proceedings_a={COLLABORATECOM}, year={2022}, month={1}, keywords={Deep learning Unpaired learning Trajectory mining Roadway-level path extraction}, doi={10.1007/978-3-030-92635-9_11} }- Weixing Jia
Guiling Wang
Xuankai Yang
Fengquan Zhang
Year: 2022
Unpaired Learning of Roadway-Level Traffic Paths from Trajectories
COLLABORATECOM
Springer
DOI: 10.1007/978-3-030-92635-9_11
Abstract
Traffic path data can be used as the basis for traffic monitoring and other technologies, which is essential for developing traffic-related technologies. Traditional methods of traffic path data extraction can no longer meet the needs because they cannot solve the problem of lacking standard benchmark data that may exist in the traffic field. Deep learning-based path extraction methods using large-scale data are a class of promising approaches. However, most of the deep learning-based path extraction methods are supervised and rely on paired training data. This paper proposes an unpaired learning method for fine-grained roadway-level paths from trajectory data based on CycleGAN. The method constructs spatio-temporal features based on HSV color space from trajectories which can enhance the model’s ability to recognize the roadway details. It transforms the features using convolutional layers, which can preserve the spatio-temporal information of the features, thus making the extraction results more accurate. We conduct experiments using urban and maritime traffic trajectory data and compare the proposed method with the state-of-the-art methods. The results of our model have more roadway level details, higher precision and F1 score than the other existing unsupervised traffic path learning methods.
