Joint optimization of energy conservation and transfer passenger service quality in rail transit system

ABSTRACTThe complex electricity transmission process and transfer passenger demand in the rail transit system make it challenging to formulate optimal timetables and train speed profiles to meet the needs of the system. This study optimizes train timetable to meet energy conservation and passenger service quality of cross-mode rail transit system during different operation periods. Firstly, based on the spatiotemporal travel data of transfer passengers, the train-time matrix is proposed to construct a bi-objective optimization model (BOOM) that incorporates the total energy consumption optimization model (TECOM) and total waiting time optimization model of transfer passenger (TWTOM). It can better combine the passenger transport scheme and the train energy-saving operation strategy, thus improving energy-saving performance and passenger transport efficiency and simplifying the difficulty of mathematical modelling of such BOOM. Then, a method for determining the appropriate weight coefficient ratio and the improved non-dominated sorting genetic algorithm II (NSGA-II) are applied to obtain the efficient Pareto front solutions. Taking the numerical experiments as test cases, the improved NSGA-II can get a efficient solution faster than the traditional NSGA-II. Finally, two numerical experiments using real-world data are conducted to verify the practicability of the optimization model and the effectiveness of improved NSGA-II. The optimization results can reduce energy consumption by up to 11.02% and passenger waiting time by 137,320 s, respectively. This study helps to improve the energy efficiency and passenger service quality of cross-mode rail transit systems, and also provides a reference for dispatchers to optimize train timetable.KEYWORDS: Transportation systemtimetable optimizationenergy conservationtransfer passengerimproved NSGA-II AcknowledgementsThis work was supported by Science and technology research project of Hubei Provincial Department of Education (No. Q20222606). The authors also acknowledge the Open Fund of Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle, Hubei Superior and Distinctive Discipline Group of “New Energy Vehicle and Smart Transportation”. Finally, the authors are grateful for the useful contributions made by their project partners.Disclosure statementNo potential conflict of interest was reported by the authors.Author contributions statementChao Wen: Methodology, Validation, Software, Data curation, Writing-Original draft preparation. Wenxin Li: Conceptualization, Methodology, Supervision, Writing- Reviewing and Editing, Funding acquisition. Qiang Ma: Conceptualization, Methodology, Funding acquisition. Guanming Wang and Baoquan Tao: Writing- Reviewing and Editing.Data availability statementThe data that support the findings of this study are available from the authors on reasonable request.Statements and DeclarationsThis manuscript has not been published or presented elsewhere in part or in entirety and is not under consideration by another journal. All study participants provided informed consent, and the study design was approved by the appropriate ethics review board. There are no conflicts of interest to declare.Additional informationFundingThe work was supported by the Science and technology research project of Hubei Provincial Department of Education [Q20222606].