Title: Optimisation of recommended speed profile for train operation based on ant colony algorithm

Authors: Fang Cao; Li-Qian Fan; Bwo-Ren Ke; Tao Tang

Addresses: State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China; Beijing Laboratory of Urban Mass Transit, Beijing Jiaotong University, Beijing, China; Beijing Key Laboratory of Urban Mass Transit Automation and Control, Beijing Jiaotong University, Beijing, China ' State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China; Beijing Laboratory of Urban Mass Transit, Beijing Jiaotong University, Beijing, China; Beijing Key Laboratory of Urban Mass Transit Automation and Control, Beijing Jiaotong University, Beijing, China ' Department of Electrical Engineering, National Penghu University of Science and Technology, Taiwan ' State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China; Beijing Laboratory of Urban Mass Transit, Beijing Jiaotong University, Beijing, China; Beijing Key Laboratory of Urban Mass Transit Automation and Control, Beijing Jiaotong University, Beijing, China

Abstract: An automatic train operation (ATO) system generally consists of the generation of recommended speed profile and the speed tracking strategy. It determines the tracked trajectory and the energy consumption of trains during the trip. Therefore, the optimisation of recommended speed profile and the ATO tracking strategy are regarded as two important means to achieve energy-efficient train operation between the successive stations. By considering the ATO tracking strategy, an optimisation method of the recommended speed profile is proposed in this paper. Based on the approximate calculation, a discrete combination optimisation model is formulated and a modified max-min ant system (MMAS) is taken as the core algorithm. With the integration speed tracking strategy, this method achieves the recommended speed profile with optimised energy consumption and a perfect running punctuality along the actual tracked trajectory. The computation time of the algorithm is shorter and the switching time of operation during the cruising phase is reduced by integrating the drivers' experience, which also reduces the energy consumption of train running between stations. The simulation results of a case study based on Beijing Subway verify the effectiveness of the proposed method, which has a good performance on energy-efficient train operation.

Keywords: recommended speed profile; optimisation models; energy efficiency; automatic train operation; ATO; ant colony optimisation; ACO; tracking strategy; simulation; speed tracking; energy consumption; subway trains; railways; train running punctuality; trajectory tracking; driver experience; underground trains; China; Beijing; urban rail transit.

DOI: 10.1504/IJSPM.2016.078512

International Journal of Simulation and Process Modelling, 2016 Vol.11 No.3/4, pp.229 - 240

Available online: 19 Aug 2016 *

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