TY - GEN
T1 - Traffic Congestion Alleviation over Dynamic Road Networks: Continuous Optimal Route Combination for Trip Query Streams
AU - Li, Ke
AU - chen, Lisi
AU - Shang, Shuo
AU - Kalnis, Panos
AU - Yao, Bin
N1 - 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).
PY - 2021/8
Y1 - 2021/8
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/675993
UR - https://www.ijcai.org/proceedings/2021/503
UR - http://www.scopus.com/inward/record.url?scp=85125463046&partnerID=8YFLogxK
U2 - 10.24963/ijcai.2021/503
DO - 10.24963/ijcai.2021/503
M3 - Conference contribution
SN - 9780999241196
SP - 3656
EP - 3662
BT - Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
PB - International Joint Conferences on Artificial Intelligence Organization
ER -
