A bi-objective timetable optimization model incorporating energy allocation and passenger assignment in an energy-regenerative metro system. (March 2020)

• Record Type: Journal Article
• Authors: Yang, Songpo; Liao, Feixiong; Wu, Jianjun; Timmermans, Harry J.P.; Sun, Huijun; Gao, Ziyou
• Is Part Of: Transportation research. Volume 133(2020)
• Page Start: 85
• Page End: 113
• Publication Date: 2020-03
• Subjects: Passenger assignment -- Energy allocation -- Irregular timetable -- Block operation -- Local search; Transportation -- Research -- Periodicals; Transportation -- Mathematical models -- Periodicals
• DOI: 10.1016/j.trb.2020.01.001
• Languages: English
• ISSNs: 0191-2615

Abstract

Highlights: Mechanismsof passenger assignment and energy allocation aredeveloped in an energy regenerative metro system. Aparallelogram-based method is developed to generate random irregulartimetables. Abi-objective optimization model is formulated foroptimizingregenerative energy use and passenger travel time. An NSGA-IIbasedalgorithmisadoptedtosolve the bi-objective optimization model. Operators based on domain knowledge are developed toexplore and exploit the solution space. Abstract: Complex passenger demand and electricity transmission processes in metro systems cause difficulties in formulating optimal timetables and train speed profiles, often leading to inefficiency in energy consumption and passenger service. Based on energy-regenerative technologies and smart-card data, this study formulates an optimization model incorporating energy allocation and passenger assignment to balance energy use and passenger travel time. The Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is applied and the core components are redesigned to obtain an efficient Pareto frontier of irregular timetables for maximizing the use of regenerative energy and minimizing total travel time. Particularly, a parallelogram-based method is developed to generate random feasible timetables; crossover and local-search-driven mutation operators are proposed relying on the graphic representations of the domain knowledge. The suggested approach is illustrated using real-world data of a bi-directional metro line in Beijing. The results show that the approach significantly improves regenerative energy use and reduces total travel time compared to the fixed regular timetable.