A bi-objective timetable optimization model incorporating energy allocation and passenger assignment in an energy-regenerative metro system

S. Yang (Corresponding author), Feixiong Liao (Corresponding author), Jianjun Wu (Corresponding author), Harry J.P. Timmermans, H. Sun, Ziyou Gao

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

Uittreksel

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.

Originele taal-2Engels
Pagina's (van-tot)85-113
Aantal pagina's29
TijdschriftTransportation Research. Part B: Methodological
Volume133
DOI's
StatusGepubliceerd - mrt 2020

Vingerafdruk

optimization model
Travel time
energy
travel
Smart cards
Energy balance
Sorting
energy technology
Energy utilization
Electricity
Genetic algorithms
energy consumption
electricity
cause
demand
knowledge
time

Citeer dit

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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.",
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A bi-objective timetable optimization model incorporating energy allocation and passenger assignment in an energy-regenerative metro system. / Yang, S. (Corresponding author); Liao, Feixiong (Corresponding author); Wu, Jianjun (Corresponding author); Timmermans, Harry J.P.; Sun, H.; Gao, Ziyou.

In: Transportation Research. Part B: Methodological, Vol. 133, 03.2020, blz. 85-113.

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

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