首页|Data on Intelligent Transport Systems Discussed by Researchers at Southwest Jiaotong University (An Energy-efficient Timetable Optimization Method for Express/local Train With On-board Passenger Number Considered)
Data on Intelligent Transport Systems Discussed by Researchers at Southwest Jiaotong University (An Energy-efficient Timetable Optimization Method for Express/local Train With On-board Passenger Number Considered)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Research findings on Transportation-Intelligent Transport Systems are discussed in a new report. According to news reporting originating in Chengdu, People's Republic of China, by NewsRx journalists, research stated, "With the expansion of the metropolitan area, the application of express/local mode is gradually increasing. In contrast to the normal mode, the express/local mode has advantages in reducing energy consumption and saving total travel time by having express trains skipping some stops." Financial support for this research came from Natural Science Foundation of Sichuan Province. The news reporters obtained a quote from the research from Southwest Jiaotong University, "This paper aims to minimize the total energy consumption of express and local train throughout the day by optimizing the train operation strategy in the same power supply section and increasing the overlap time between train traction acceleration and train regenerative braking to obtain the optimal energy-efficient timetable. As the consumed energy of a train is highly dependent on the rolling stock weight and the onboard passengers ‘ weight. An integer programming model is proposed with on-board passengers considered accurately, in which the dwell times, departure headway, and total turnaround time of express and local trains are determined. An improved grey wolf algorithm is designed by improving convergence factor and incorporating differential evolution to solve the proposed problem. The real data on Guangzhou Metro Line 18 is adopted for numerical studies."
ChengduPeople's Republic of ChinaAsiaIntelligent Transport SystemsTransportationSouthwest Jiaotong University
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