ITSC 2025 Paper Abstract

Paper WE-LA-T2.3

Davis, Cameron (University of Canterbury), Keyvan-Ekbatani, Mehdi (University of Canterbury)

Dynamic Electric Dial-A-Ride Problem Considering Traffic and Charging Congestion

Scheduled for presentation during the Regular Session "S02c-Optimization for Shared, Electric, and Sustainable Mobility Systems" (WE-LA-T2), Wednesday, November 19, 2025, 16:40−17:00, Southport 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 19, 2025

Keywords Shared and Electric Mobility Services in Public Transport Networks, Integration of Electric Vehicles into Smart City Mobility Networks, Transportation Optimization Techniques and Multi-modal Urban Mobility

Abstract

This study presents a dynamic electric dial-a-ride problem that considers traffic and queuing congestion within the optimization process. By considering the impact of private electric vehicle charging demand on queueing, fleet vehicles can be routed to charging stations with lower queues to maintain higher service quality. Accumulation-based Network Macroscopic Fundamental Diagrams (NMFDs) in statically partitioned regions are used to estimate vehicle speeds at each time step. Wait time due to stochastic charging demand from private EVs is predicted through a quasi-dynamic queuing model that considers both an M/M/S queue model and the current state of charging stations. Gurobi was used to solve the dial-a-ride problem. It was shown that the developed quasi-dynamic queuing model outperformed the M/M/S queue model.