Research Article
Where Is the Next Path? A Deep Learning Approach to Path Prediction Without Prior Road Networks
@INPROCEEDINGS{10.1007/978-3-030-67540-0_13, author={Guiling Wang and Mengmeng Zhang and Jing Gao and Yanbo Han}, title={Where Is the Next Path? A Deep Learning Approach to Path Prediction Without Prior Road Networks}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 16th EAI International Conference, CollaborateCom 2020, Shanghai, China, October 16--18, 2020, Proceedings, Part II}, proceedings_a={COLLABORATECOM PART 2}, year={2021}, month={1}, keywords={Trajectory prediction LSTM Crowdsourcing trajectories AIS data}, doi={10.1007/978-3-030-67540-0_13} }- Guiling Wang
Mengmeng Zhang
Jing Gao
Yanbo Han
Year: 2021
Where Is the Next Path? A Deep Learning Approach to Path Prediction Without Prior Road Networks
COLLABORATECOM PART 2
Springer
DOI: 10.1007/978-3-030-67540-0_13
Abstract
Trajectory prediction plays an important role in many urban and marine transportation applications, such as path planning, logistics and traffic management. The existing prediction methods of moving objects mainly focus on trajectory mining in Euclidean space. However, moving objects generally move under road network constraints in the real world. It provides an opportunity to take use of road network constraints or de-facto regular paths for trajectory prediction. As yet, there is little research work on trajectory prediction under road network constraints. And these existing work assumes prior road network information is given in advance. However in some application scenarios, it is very difficult to get road network information, for example the maritime traffic scenario on the wide open ocean. To this end, we propose an approach to trajectory prediction that can make good use of road network constrains without depending on prior road network information. More specifically, our approach extracts road segment polygons from large scale crowdsourcing trajectory data (e.g. AIS positions of ships, GPS positions of vehicles etc.) and translates trajectories into road segment sequences. Useful features such as movement direction and vehicle type are extracted. After that, a LSTM neural network is used to infer the next road segment of a moving object. Experiments on real-world AIS datasets confirm that our approach outperforms the state-of-the-art methods.
