A goal-prioritized algorithm for additional route deployment on existing mass transportation system

Fandel Lin, Hsun Ping Hsieh

研究成果: Conference contribution

摘要

Multi-criteria path planning is an important combinatorial optimization problem with broad real-world applications. Finding the Pareto-optimal set of paths ideal for all requiring features is time-consuming and unclear to obtain the subset of optimal paths efficiently for multiple origin states in the planning space. Meanwhile, due to the rise of deep learning, hybrid systems of computational intelligence thrive in recent years. When facing non-monotonic data or heuristics derived from pre-trained neural networks, most of the existing methods for the one-to-all path problem fail to find an ideal solution. We employ Gaussian mixture model to propose a target-prioritized searching algorithm called Multi-Source Bidirectional Gaussian-Prioritized Spanning Tree (BiasSpan) in solving this non-monotonic multi-criteria route planning problem given constraints including range, must-visit vertices, and the number of recommended vertices. Experimental results on mass transportation system in Tainan and Chicago cities show that BiasSpan outperforms comparative methods from 7% to 24% and runs in a reasonable time compared to state-of-art route-planning algorithms.

原文English
主出版物標題Proceedings - 20th IEEE International Conference on Data Mining, ICDM 2020
編輯Claudia Plant, Haixun Wang, Alfredo Cuzzocrea, Carlo Zaniolo, Xindong Wu
發行者Institute of Electrical and Electronics Engineers Inc.
頁面1130-1135
頁數6
ISBN(電子)9781728183169
DOIs
出版狀態Published - 2020 十一月
事件20th IEEE International Conference on Data Mining, ICDM 2020 - Virtual, Sorrento, Italy
持續時間: 2020 十一月 172020 十一月 20

出版系列

名字Proceedings - IEEE International Conference on Data Mining, ICDM
2020-November
ISSN(列印)1550-4786

Conference

Conference20th IEEE International Conference on Data Mining, ICDM 2020
國家Italy
城市Virtual, Sorrento
期間20-11-1720-11-20

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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