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.
Highlights
In recent years, urban rail transit has developed rapidly around the world due to its high capacity, safety, superior energy performance, and reliable service with sufficient punctuality [1], which is becoming increasingly important for large cities development [2]
The traction energy consumption of a single train is related to the speed profile between stations
When energy-efficient profiles are applied in every section, there will be a positive effect on reducing energy consumption of the urban rail transit system
Summary
Urban rail transit has developed rapidly around the world due to its high capacity, safety, superior energy performance, and reliable service with sufficient punctuality [1], which is becoming increasingly important for large cities development [2]. Public Transit Usage Report of moovit), urban rail transit system has attracted much attention in recent years especially in some large cities and accounts for a high proportion of public transportation. Taking Beijing rail transit as an example, in 2011, the total electric consumption of Beijing urban rail transit was 750 million kwh, and 470 million kwh was used for traction energy consumption, with the proportion as high as 55% which has attracted tremendous attention in recent years (Yin et al [4]). To reduce the energy consumption in urban rail transit, a lot of models have been developed in recent years which mainly considered the train controlling between two stations based on the kinematic equations. There are three types in general, i.e., mathematical optimization models, simulation methods, and multiple linear regression, and neural network model based on the data.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
