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 …

Date

2022

Authors

Fandel Lin, Hsun-Ping Hsieh

Journal

ACM Transactions on Intelligent Systems and Technology (TIST)

Volume

13

Issue

3

Pages

1-25

Publisher

ACM