Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/61663
Title: A stochastic model for the integrated optimization on metro timetable and speed profile with uncertain train mass
Authors: Yang, X
Chen, A 
Ning, B
Tang, T
Keywords: Integrated optimization
Metro systems
Speed profile
Timetable
Uncertain train mass
Issue Date: 2016
Publisher: Pergamon Press
Source: Transportation research. Part B, Methodological, 2016, v. 91, p. 424-445 How to cite?
Journal: Transportation research. Part B, Methodological 
Abstract: The integrated timetable and speed profile optimization model has recently attracted more attention because of its good achievements on energy conservation in metro systems. However, most previous studies often ignore the spatial and temporal uncertainties of train mass, and the variabilities of tractive force, braking force and basic running resistance on energy consumption in order to simplify the model formulation and solution algorithm. In this paper, we develop an integrated metro timetable and speed profile optimization model to minimize the total tractive energy consumption, where these real-world operating conditions are explicitly considered in the model formulation and solution algorithm. Firstly, we formulate a two-phase stochastic programming model to determine the timetable and speed profile. Given the speed profile, the first phase determines the timetable by scheduling the arrival and departure times for each station, and the second phase determines the speed profile for each inter-station with the scheduled arrival and departure times. Secondly, we design a simulation-based genetic algorithm procedure incorporated with the optimal train control algorithm to find the optimal solution. Finally, we present a simple example and a real-world example based on the operation data from the Beijing Metro Yizhuang Line in Beijing, China. The results of the real-world example show that, during peak hours, off-peak hours and night hours, the total tractive energy consumptions can be reduced by: (1) 10.66%, 9.94% and 9.13% in comparison with the current timetable and speed profile; and (2) 3.35%, 3.12% and 3.04% in comparison with the deterministic model.
URI: http://hdl.handle.net/10397/61663
ISSN: 0191-2615
EISSN: 1879-2367
DOI: 10.1016/j.trb.2016.06.006
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