Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
11-2022
Abstract
In many route planning applications, finding constrained shortest paths (CSP) is an important and fundamental problem. CSP aims to find the shortest path between two nodes on a graph while satisfying a path constraint. Solving CSPs requires a large search space and is prohibitively slow on large graphs, even with the state-of-the-art parallel solution on GPUs. The reason lies in the lack of effective navigational information and pruning strategies in the search procedure. In this paper, we propose SPEC, a Shortest Path Enhanced approach for solving the exact CSP problem. Our design rationales of SPEC rely on the observation that the shortest path (SP) provides valuable information in the search procedure of CSP. Hence, we propose a label priority that distinguishes promising candidate paths based on SP. We further devise efficient pruning and teleporting strategies utilizing SP lengths and costs, which eliminates unfeasible paths at an early stage. Furthermore, we observe that the expansion number at each search iteration affects the overall performance significantly. Thus, we devise an adaptive controller based on reinforcement learning. We also show that SPEC works seamlessly with the parallel implementation. Extensive experimental results on 8 read-world graphs reveal that single thread SPEC achieves an order of magnitude speedup over the state-of-the-art GPU-based method. The parallel implementation boosts SPEC 3 to 5 times further.
Keywords
Constrained shortest path, shortest path
Discipline
Numerical Analysis and Scientific Computing | Operations Research, Systems Engineering and Industrial Engineering
Research Areas
Data Science and Engineering
Publication
2022 IEEE International Conference on Data Mining (ICDM): Orlando, November 22 - December 1: Proceedings
First Page
588
Last Page
597
ISBN
9781665450997
Identifier
10.1109/ICDM54844.2022.00069
Publisher
IEEE Computer Society
City or Country
Washington, DC
Citation
XIA, Wenwen; LI, Yuchen; GUO, Wentian; and LI, Shenghong.
Efficient navigation for constrained shortest path with adaptive expansion control. (2022). 2022 IEEE International Conference on Data Mining (ICDM): Orlando, November 22 - December 1: Proceedings. 588-597.
Available at: https://ink.library.smu.edu.sg/sis_research/7782
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/ICDM54844.2022.00069
Included in
Numerical Analysis and Scientific Computing Commons, Operations Research, Systems Engineering and Industrial Engineering Commons