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

Fandel Lin, Hsun Ping Hsieh

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

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 20th IEEE International Conference on Data Mining, ICDM 2020
EditorsClaudia Plant, Haixun Wang, Alfredo Cuzzocrea, Carlo Zaniolo, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1130-1135
Number of pages6
ISBN (Electronic)9781728183169
DOIs
Publication statusPublished - 2020 Nov
Event20th IEEE International Conference on Data Mining, ICDM 2020 - Virtual, Sorrento, Italy
Duration: 2020 Nov 172020 Nov 20

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
Volume2020-November
ISSN (Print)1550-4786

Conference

Conference20th IEEE International Conference on Data Mining, ICDM 2020
CountryItaly
CityVirtual, Sorrento
Period20-11-1720-11-20

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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