Traffic Congestion Alleviation over Dynamic Road Networks: Continuous Optimal Route Combination for Trip Query Streams

Li, Ke; Chen, Lisi; Shang, Shuo; Kalnis, Panos; Yao, Bin

Traffic Congestion Alleviation over Dynamic Road Networks: Continuous Optimal Route Combination for Trip Query Streams

Files

Li-Chen-Shang-Kalnis-Yao IJCAI-21.pdf (1.11 MB)

Type

Conference Paper

Authors

Li, Ke
Chen, Lisi
Shang, Shuo
Kalnis, Panos

Yao, Bin

KAUST Department

Computer Science Program
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Extreme Computing Research Center
InfoCloud Research Group

Date

2021-08

Abstract

Route planning and recommendation have attracted much attention for decades. In this paper, we study a continuous optimal route combination problem: Given a dynamic road network and a stream of trip queries, we continuously find an optimal route combination for each new query batch over the query stream such that the total travel time for all routes is minimized. Each route corresponds to a planning result for a particular trip query in the current query batch. Our problem targets a variety of applications, including traffic-flow management, real-time route planning and continuous congestion prevention. The exact algorithm bears exponential time complexity and is computationally prohibitive for application scenarios in dynamic traffic networks. To address this problem, a self-aware batch processing algorithm is developed in this paper. Extensive experiments offer insight into the accuracy and efficiency of our proposed algorithms.

Citation

Li, K., Chen, L., Shang, S., Kalnis, P., & Yao, B. (2021). Traffic Congestion Alleviation over Dynamic Road Networks: Continuous Optimal Route Combination for Trip Query Streams. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. https://doi.org/10.24963/ijcai.2021/503

Acknowledgements

Ke Li and Shuo Shang were supported by the NSFC (U2001212, 62032001, and 61932004). Bin Yao was supported by the NSFC (61922054, 61872235, 61832017, and 61729202), and the National Key Research and Development Program of China (2020YFB1710202, and 2018YFC1504504).