ITSC 2025 Paper Abstract

Paper FR-LM-T35.4

Yang, Jiaxiang (Shenzhen Technology University), He, Yuxin (Shenzhen Technology University), Wang, Hao (Shenzhen University), Chen, Jingjing (Shenzhen Technology University), Luo, Qin (Shenzhen Technology University), Lei, Tian (Shenzhen Technology University)

A Theme-Based CityWalk Route Planning Algorithm: Considering Repeated Route Avoidance with Online Platform Data

Scheduled for presentation during the Regular Session "S35a-Optimization, Control, and Learning for Efficient and Resilient ITS" (FR-LM-T35), Friday, November 21, 2025, 11:30−11:50, Surfers Paradise 2

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Keywords Transportation Optimization Techniques and Multi-modal Urban Mobility, Multimodal Transportation Networks for Efficient Urban Mobility, Demand-Responsive Transit Systems for Smart Cities

Abstract

Theme-based CityWalk has emerged as an immersive urban exploration paradigm demanding personalized routes with minimal repetition in real-world road networks. Traditional methods suffer from static Points of Interest (POI) evaluation and idealized network modeling, causing route redundancy and poor thematic alignment. As a variant of the Traveling Salesman Problem (TSP), we propose a three-stage iterative framework to solve it. By leveraging user interaction data from online platforms, we dynamically score POIs to reflect individual preferences. Our three-stage approach involves selecting key POIs through spatial clustering, generating an initial route using Ant Colony Optimization (ACO), and developing an algorithm named Two-phase Adaptive Repetition Avoidance (TP-ARA) to adaptively avoid repeated routes. Experiments conducted on the urban road network of Shenzhen demonstrate that the proposed optimization algorithm reduces the route repetition by at least 92.3% and improves overall performance by at least 11.3%, proving its effectiveness in enhancing personalized tourism experiences. This work bridges theoretical route planning problems and real-world urban network constraints, providing municipalities with a computationally tractable framework for enhancing cultural tourism experiences.