Traveling Transporter Problem: Arranging a New Circular Route in a Public Transportation System Based on Heterogeneous Non-Monotonic Urban Data

Fandel Lin, Hsun Ping Hsieh

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Hybrid computational intelligent systems that synergize learning-based inference models and route planning strategies have thrived in recent years. In this article, we focus on the non-monotonicity originated from heterogeneous urban data, as well as heuristics based on neural networks, and thereafter formulate the traveling transporter problem (TTP). TTP is a multi-criteria optimization problem and may be applied to the circular route deployment in public transportation. In particular, TTP aims to find an optimized route that maximizes passenger flow according to a neural-network-based inference model and minimizes the length of the route given several constraints, including must-visit stations and the requirement for additional ones. As a variation of the traveling salesman problem (TSP), we propose a framework that first recommends new stations' location while considering the herding effect between stations, and thereafter combines state-of-the-art TSP solvers and a metaheuristic named Trembling Hand, which is inspired by self-efficacy for solving TTP. Precisely, the proposed Trembling Hand enhances the spatial exploration considering the structural patterns, previous actions, and aging factors. Evaluation conducted on two real-world mass transit systems, Tainan and Chicago, shows that the proposed framework can outperform other state-of-the-art methods by securing the Pareto-optimal toward the objectives of TTP among comparative methods under various constrained settings.

Original languageEnglish
Article number49
JournalACM Transactions on Intelligent Systems and Technology
Volume13
Issue number3
DOIs
Publication statusPublished - 2022 Jun

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Artificial Intelligence

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