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

Ke Li, Lisi chen, Shuo Shang, Panos Kalnis, Bin Yao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

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.
Original languageEnglish (US)
Title of host publicationProceedings of the Thirtieth International Joint Conference on Artificial Intelligence
PublisherInternational Joint Conferences on Artificial Intelligence Organization
Pages3656-3662
Number of pages7
ISBN (Print)9780999241196
DOIs
StatePublished - Aug 2021

Bibliographical note

KAUST Repository Item: Exported on 2022-03-29
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).

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