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Paper FR-LM-T34.1

Yu, Changjian (Tongji University), Zhang, Zihan (Shanghai Motor Vehicle Inspection Certification and Tech Innovat), So, Jaehyun (The Korea Transport Institute), Liu, Chang (Budapest University of Technology and Economics), Braghin, Francesco (Politecnico di Milano), Hu, Jia (Tongji University)

An Integrated Framework for Optimized Cross-Line Transit Scheduling

Scheduled for presentation during the Regular Session "S34a-Data-Driven Optimization and Governance in Intelligent Urban Mobility" (FR-LM-T34), Friday, November 21, 2025, 10:30−10:50, Surfers Paradise 1

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 Demand-Responsive Transit Systems for Smart Cities, Real-time Passenger Information and Service Optimization in Public Transportation, Autonomous Public Transport Systems and Mobility-as-a-Service (MaaS)

Abstract

This paper introduces a scheduling framework for reinforcement bus transit that enables targeted cross-line deployment without disrupting existing routes. Unlike conventional strategies that rely on full-line reallocation, the proposed method focuses on reinforcing high-demand segments across multiple lines. The approach consists of three key steps: identifying critical segments based on passenger volume using a graph-based network model; optimizing departure intervals via a mixed-integer programming formulation that accounts for waiting time, crowding, and operational cost; and implementing fixed scheduling for selected segments. Simulation experiments demonstrate that this method effectively reduces passenger delays, improves vehicle load balance, and lowers service deployment intensity. Under imbalanced demand conditions, the proposed framework achieves measurable reductions in both operating frequency and bus overcrowding, offering a scalable and resource-efficient alternative to traditional reinforcement practices.

 

 

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