Bi-objective nonlinear programming with minimum energy consumption and passenger waiting time for metro systems, based on the real-world smart-card data

Metro is considered as an efficient transport mode to alleviate traffic congestion in big cities because of its large transport capacity. Generally, a good metro system means not only a passenger-oriented timetable but also an eco-friendly speed profile. This study develops a bi-objective nonlinear...

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Veröffentlicht in:Transportmetrica. (Abingdon, Oxfordshire, UK) Jg. 6; H. 4; S. 302 - 319
Hauptverfasser: Yang, Songpo, Wu, Jianjun, Sun, Huijun, Yang, Xin, Gao, Ziyou, Chen, Anthony
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Abingdon Taylor & Francis 02.10.2018
Taylor & Francis Ltd
Schlagworte:
Energy conservation
Energy consumption
Mathematical models
Nonlinear programming
passenger waiting time
Passengers
Quadratic programming
speed profile
Subways
Timetable
Traffic capacity
Traffic congestion
ISSN:2168-0566, 2168-0582
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Zusammenfassung:Metro is considered as an efficient transport mode to alleviate traffic congestion in big cities because of its large transport capacity. Generally, a good metro system means not only a passenger-oriented timetable but also an eco-friendly speed profile. This study develops a bi-objective nonlinear programming model to determine the optimal timetable and speed profile, with minimum energy consumption and passenger waiting time. In the nonlinear formulation, the average passenger waiting time is calculated based on the dynamic passenger flow by using the real-world smart-card data, and the energy consumption is obtained based on the tractive and regenerative energy on each section. The high-dimensional nonlinear problem is converted to a classical quadratic programming by using the Taylor approximation for obtaining the optimal solution easily. Finally, we conduct a numerical example based on the real-world data from the Beijing Metro Yizhuang Line of China. The results show that the developed model can save energy consumption by 6.0% and reduce passenger waiting time by 10.9% in comparison with the current planned timetable.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:2168-0566
2168-0582
DOI:10.1080/21680566.2017.1320775