Discrete Train Speed Profile Optimization for Urban Rail Transit: A Data-Driven Model and Integrated Algorithms Based on Machine Learning

• Published in: Journal of advanced transportation Vol. 2019; no. 2019; pp. 1 - 17
• Main Authors: Liu, Feng, Ziyou, Gao, Yang, Xin, Wu, Jianjun, Huang, Kang, Zhu, Yu-Ting
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2019; Hindawi; John Wiley & Sons, Inc; Wiley
• Subjects: Algorithms; Analysis; Big Data; Data mining; Empirical equations; Energy consumption; Energy efficiency; Environmental protection; Heuristic methods; Integrated approach; Learning algorithms; Light rail transit; Machine learning; Mathematical models; Neural networks; Optimization; Public transportation; Regression analysis; Simulation; Support vector machines; Urban rail; China; Beijing China
• ISSN: 0197-6729; 2042-3195; 2042-3195
• DOI: 10.1155/2019/7258986

Energy-efficient train speed profile optimization problem in urban rail transit systems has attracted much attention in recent years because of the requirement of reducing operation cost and protecting the environment. Traditional methods on this problem mainly focused on formulating kinematical equations to derive the speed profile and calculate the energy consumption, which caused the possible errors due to some assumptions used in the empirical equations. To fill this gap, according to the actual speed and energy data collected from the real-world urban rail system, this paper proposes a data-driven model and integrated heuristic algorithm based on machine learning to determine the optimal speed profile with minimum energy consumption. Firstly, a data-driven optimization model (DDOM) is proposed to describe the relationship between energy consumption and discrete speed profile processed from actual data. Then, two typical machine learning algorithms, random forest regression (RFR) algorithm and support vector machine regression (SVR) algorithm, are used to identify the importance degree of velocity in the different positions of profile and calculate the traction energy consumption. Results show that the calculation average error is less than 0.1 kwh, and the energy consumption can be reduced by about 2.84% in a case study of Beijing Changping Line.