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

Abstract

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.

Integrated Optimization on Energy Saving and Quality of Service of Urban Rail Transit System

“…e Dutch Railway was selected as a practical case to verify the e ectiveness of the proposed optimization model, and the results show that train energy consumption and delay recovery time can be e ectively reduced. Huang et al [22] proposed a data-driven optimization model to describe the relationship between energy consumption and speed pro le. ey then integrated two typical machine learning algorithms, random forest regression (RFR), and support vector machine regression (SVR) into a heuristic algorithm to solve the model.…”

“…A er investigating the existing literature, most of them [20-22, 26, 28, 29] have contributed to energy-e cient driving using the timetable optimization method to obtain energy-saving timetables that are conducive to reducing energy consumption. e decision variables selected in the existing literature on train energy-saving driving optimization are usually running time [20][21][22] or dwelling time [26,28,29]. e decision variable chosen in this paper is the dwelling time.…”

“…In summary, most of the literature in Table 1 focuses on improving energy e ciency through speed optimization [17,20,22] or timetable optimization [8,15,30]. Some studies also consider the impact of transport service quality [13,16,21].…”

An Energy-Efficient Timetable Optimization Approach in a Bi-DirectionUrban Rail Transit Line: A Mixed-Integer Linear Programming Model

“…Therefore, a data-driven optimization model emerges. Huang et al (2019) [7] proposed a Data-Driven Optimization Model (DDOM) to describe the relationship between energy consumption and the discrete speed profile, then integrated two typical machine learning algorithms, Random Forest Regression (RFR) and Support Vector machine Regression (SVR), into a heuristic algorithm to solve the model. Similarly, much research has been associated with the train timetable optimization problem in rail transportation systems (Shakibayifar et al, 2017 [18], Wang et al, 2017 [19], Gainanov et al, 2017 [20], Hassannayebi et al, 2018 [21]).…”

“…In particular, an energy-efficient timetable pays more attention to the running process in the section, which greatly affects the energy consumption and running time. There is a long history of research (Howlett et al [5], Yang et al [6], Canca et al [4], Huang et al [7]) focusing on energy-efficient train operation. It is always solving the optimal train control problem based on the Pontryagin Maximum Principle (PMP).…”