Time-optimal and privacy preserving route planning for carpool policy
-
Published:2022-03-04
Issue:3
Volume:25
Page:1151-1168
-
ISSN:1386-145X
-
Container-title:World Wide Web
-
language:en
-
Short-container-title:World Wide Web
Author:
Zhu Congcong,Ye Dayong,Zhu Tianqing,Zhou Wanlei
Abstract
AbstractTo alleviate the traffic congestion caused by the sharp increase in the number of private cars and save commuting costs, taxi carpooling service has become the choice of many people. Current research on taxi carpooling services has focused on shortening the detour distances. While with the development of intelligent cities, efficiently match passengers and vehicles and planning routes become urgent. And the privacy between passengers in the taxi carpooling service also needs to be considered. In this paper, we propose a time-optimal and privacy-preserving carpool route planning system via deep reinforcement learning. This system uses the traffic information around the carpooling vehicle to optimize passengers’ travel time, not only to efficiently match passengers and vehicles but also to generate detailed route planning for carpooling vehicles. We conducted experiments on an Internet of Vehicles simulator CARLA, and the results demonstrate that our method is better than other advanced methods and has better performance in complex environments.
Funder
Australian Research Council Linkage University of Technology Sydney
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Software
Reference33 articles.
1. An, L., Ren, C., Gu, Z., Wang, Y., Gao, Y.: Rebalancing the car-sharing system: A reinforcement learning method. In: 2019 IEEE Fourth International Conference on Data Science in Cyberspace (DSC), pp. 62–69. IEEE (2019) 2. Bast, H., Delling, D., Goldberg, A., Müller-Hannemann, M., Pajor, T., Sanders, P., Wagner, D., Werneck, R.F.: Route planning in transportation networks. In: Algorithm engineering, pp. 19–80. Springer (2016) 3. Dijkstra, E.W., et al.: A note on two problems in connexion with graphs. Numerische Mathematik 1(1), 269–271 (1959) 4. Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: CARLA: An open urban driving simulator. In: Proceedings of the 1st Annual Conference on Robot Learning, pp. 1–16 (2017) 5. Duan, Y., Mosharraf, T., Wu, J., Zheng, H.: Optimizing carpool scheduling algorithm through partition merging. In: 2018 IEEE International Conference on Communications (ICC), pp. 1–6. IEEE (2018)
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|