A Gaussian-Prioritized Approach for Deploying Additional Route on Existing Mass Transportation with Neural-Network-Based Passenger Flow Inference

Fandel Lin, Jie Yu Fang, Hsun Ping Hsieh

研究成果: Conference contribution

2 引文 斯高帕斯(Scopus)

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 pretrained neural networks, most of the existing methods for the oneto-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 multicriteria 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2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781728169293
DOIs
出版狀態Published - 2020 7月
事件2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Virtual, Glasgow, United Kingdom
持續時間: 2020 7月 192020 7月 24

出版系列

名字2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings

Conference

Conference2020 IEEE Congress on Evolutionary Computation, CEC 2020
國家/地區United Kingdom
城市Virtual, Glasgow
期間20-07-1920-07-24

All Science Journal Classification (ASJC) codes

  • 控制和優化
  • 決策科學(雜項)
  • 人工智慧
  • 電腦視覺和模式識別
  • 硬體和架構

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