A two-stage stochastic optimization model for integrated tram timetable and speed control with uncertain dwell times. (1st December 2022)

• Record Type: Journal Article
• Authors: Li, Jiajie; Bai, Yun; Chen, Yao; Yang, Lingling; Wang, Qian
• Is Part Of: Energy. Volume 260(2022)
• Publication Date: 2022-12-01
• Subjects: Tram scheduling -- Dwell time disturbance -- Stochastic optimization -- Energy consumption -- Speed profile; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2022.125059
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Modern trams usually own passive transit signal priority (TSP) to avoid interruption from traffic signals along the route. The key to TSP depends on the stick to the recommended travel time between intersections strictly. However, the effectiveness of the TSP can be weakened by dwell time fluctuation due to uncertain passenger demand at the stations. This paper proposes a two-stage stochastic optimization model for timetable and tram control to improve the TSP reliability considering uncertain dwell times. The first stage of the model focuses on designing timetable alternatives, and the second stage evaluates the timetables through expected travel time and energy consumption under different dwell time disturbance scenarios. The Brute force algorithm is developed to attain the optimal tram control, while the non-dominated sorting genetic algorithm II (NSGA-II) and GUROBI solver are both adopted to optimize the timetables. A case study of Nanjing Tram Line 1 in China is performed to demonstrate the effectiveness of the proposed approach. The results show that compared to the existing method, the proposed method reduces energy consumption by 16.0% and the number of stops at intersections decreases by 73.7% with the same travel time. Highlights: A two-stage stochastic optimization model for tram timetable and speed control with dwell time uncertainty. Expected travel time and energy consumption are established as objectives. The energy-efficient tram control is formulated to generate speed profiles. The brute force algorithm and non-dominated sorting genetic algorithm II are designed compared with GUROBI solver.