Research on improvement of urban rail transit energy consumption model

2021 ◽  
Author(s):  
Chenglong Wang ◽  
Xinji Wang ◽  
Yeli Wang
Keyword(s):  
Energy Consumption ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Urban Rail ◽  
Energy Consumption Model ◽  
Consumption Model

Can urban rail transit curb automobile energy consumption?

Energy Policy ◽  
2017 ◽  
Vol 105 ◽  
pp. 120-127 ◽  
Author(s):  
Boqiang Lin ◽  
Zhili Du
Keyword(s):  
Energy Consumption ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Urban Rail

scholarly journals Stop Plan of Express and Local Train for Regional Rail Transit Line

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Qin Luo ◽  
Yufei Hou ◽  
Wei Li ◽  
Xiongfei Zhang
Keyword(s):  
Energy Consumption ◽  
Travel Time ◽  
Programming Model ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Long Distance ◽  
Passenger Travel ◽  
Urban Rail ◽  
Proposed Model ◽  
Rail Line

The urban rail transit line operating in the express and local train mode can solve the problem of disequilibrium passenger flow and space and meet the rapid arrival demand of long-distance passengers. In this paper, the Logit model is used to analyze the behavior of passengers choosing trains by considering the sensitivity of travel time and travel distance. Then, based on the composition of passenger travel time, an integer programming model for train stop scheme, aimed at minimizing the total passenger travel time, is proposed. Finally, combined with a certain regional rail line in Shenzhen, the plan is solved by genetic algorithm and evaluated through the time benefit, carrying capacity, and energy consumption efficiency. The simulation result shows that although the capacity is reduced by 6 trains, the optimized travel time per person is 10.34 min, and the energy consumption is saved by about 16%, which proves that the proposed model is efficient and feasible.

Thinking of Comprehensive Energy-Saving System Construction for Urban Rail Transit

2014 ◽  
Vol 505-506 ◽  
pp. 405-409
Author(s):  
Jun Ke Liang ◽  
Zhi Gang Liu ◽  
Yuan Chun Huang
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Optimization Design ◽  
High Efficiency ◽  
High Energy ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
System Construction ◽  
Planning Period ◽  
Urban Rail

The High Energy Consumption of the Current Urban Rail Transit Industry, High Efficiency Energy Saving Measures must be Taken. this Paper Entity from the Traction Energy Consumption, Building Structure and Operating Equipment Aspects of the Current Situation, Described the Energy Saving Strategies. Aiming at the Present Problems Existing in Energy Saving Practice, this Article Puts Forward the Comprehensive Energy Saving System which Contains Optimization Design in Planning Period, Low Resource Consumption in Construction Period, Energy Saving Work in Operation Period. above all, Implement Energy Saving Practice at Every Concrete Work of Reaching.

Design for Urban Rail Transit Energy Efficiency Management System

2013 ◽  
Vol 749 ◽  
pp. 629-633
Author(s):  
Jian Bin Ye ◽  
Zhi Yan Ding ◽  
Qi Zhu
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Management System ◽  
Rapid Development ◽  
Chinese Economy ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Service Architecture ◽  
Urban Rail ◽  
Emission Reduction Strategies

With the rapid development of Chinese economy and the speeding up urbanization, urban rail transit has entered a rapid development period, which results in more and more energy consumption. Meanwhile affected by energy source and environment factors, the state has implemented energy-saving emission reduction strategies in various fields, so energy efficient management for urban rail transit becomes more important. Based on the analysis of energy consumption problems in urban rail transit, the paper designs urban rail transit energy efficiency management system from the aspects of overall architecture, service architecture and application function, and provides technical support for the realization of the software system.

The Effect of Urban Rail Transit on Decreasing Energy Consumption and Air Pollution in Ahvaz City

2011 ◽  
Vol 255-260 ◽  
pp. 2802-2805 ◽  
Author(s):  
Seyed Abbas Tabatabaiee ◽  
Ali Rahman
Keyword(s):  
Air Pollution ◽  
Energy Consumption ◽  
Central Area ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Karun River ◽  
Railway Systems ◽  
The North ◽  
Urban Rail ◽  
North Eastern

In this paper, the effect of utilizing first urban rail transit on decreasing energy consumption as well as air pollution in Ahvaz has been investigated. The No. 1metro path in Ahvaz with 23 kilometers length will connect the north eastern part of the city to its south western part by passing through the central area and crossing Karun River. At the beginning, some points will be mentioned regarding railway systems around the world and then the specification of Ahvaz No. 1 metro will be introduced. Finally, the amount of decreasing energy consumption and air pollution after utilizing 4 urban metro lines will be investigated. The results have shown considerable effects on decreasing these items.

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

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2686 ◽  
Author(s):  
Huanhuan Lv ◽  
Yuzhao Zhang ◽  
Kang Huang ◽  
Xiaotong Yu ◽  
Jianjun Wu
Keyword(s):  
Linear Programming ◽  
Energy Consumption ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Urban Rail Transit ◽  
Mixed Integer ◽  
Rail Transit ◽  
Operational Cost ◽  
System Cost ◽  
Urban Rail

The quick growth of energy consumption in urban rail transit has drawn much attention due to the pressure of both operational cost and environmental responsibilities. In this paper, the timetable is optimized with respect to the system cost of urban rail transit, which pays more attention to energy consumption. Firstly, we propose a Mixed-Integer Non-Linear Programming (MINLP) model including the non-linear objective and constraints. The objective and constraints are linearized for an easier process of solution. Then, a Mixed-Integer Linear Programming (MILP) model is employed, which is solved using the commercial solver Gurobi. Furthermore, from the viewpoint of system cost, we present an alternative objective to optimize the total operational cost. Real Automatic Fare Collection (AFC) data from the Changping Line of Beijing urban rail transit is applied to validate the model in the case study. The results show that the designed timetable could achieve about a 35% energy reduction compared with the maximum energy consumption and a 6.6% cost saving compared with the maximum system cost.

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

2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Kang Huang ◽  
Jianjun Wu ◽  
Xin Yang ◽  
Ziyou Gao ◽  
Feng Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Urban Rail Transit ◽  
Data Driven ◽  
Support Vector ◽  
Rail Transit ◽  
Speed Profile ◽  
Urban Rail ◽  
Train Speed ◽  
Profile Optimization

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.

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