您当前的位置：
首页 >
文章列表页 >
Review on crew planning optimization for urban rail transit
更新时间：2025-05-26

    • Review on crew planning optimization for urban rail transit

    • “New progress has been made in the optimization of urban rail transit crew planning, and modeling methods and solving algorithms have been systematically summarized to provide guidance for future research directions.”
    • Journal of Jilin University(Engineering and Technology Edition)   Vol. 55, Issue 2, Pages: 401-418(2025)
    • 作者机构：

      1.西南交通大学 交通运输与物流学院， 成都 611756

      2.西南交通大学 综合交通大数据应用技术国家工程实验室， 成都 611756

      3.西南交通大学 综合交通运输智能化国家地方联合工程实验室， 成都 611756

    • DOI：10.13229/j.cnki.jdxbgxb.20231138    

      CLC： U292.8

    • Received：22 October 2023，

      Published：01 February 2025

    移动端阅览

  • HU Zuo-an,ZHOU Shu,ZHANG Yu-ang.Review on crew planning optimization for urban rail transit[J].Journal of Jilin University(Engineering and Technology Edition),2025,55(02):401-418. DOI： 10.13229/j.cnki.jdxbgxb.20231138.

  •  
  •  

0

Views

25

下载量

0

CSCD

Alert me when the article has been cited
提交
Tools
Download
Export Citation
Share
Add to favorites
Add to my album

Related Articles

Optimization of cross-line train operation across the urban rail transit loop line
Recognition of travel patterns for urban rail transit passengers based on spatiotemporal sequence similarity
Urban rail transit emergency risk level identification method
Optimization algorithm of urban rail transit operation scheduling based on linear programming
Modelling on catchment area and attraction intensity of urban rail transit stations

Related Author

MAO Bao-hua
TONG Rui-yong
WANG Hai-peng
ZHANG Hao-xiang
LU Xia
TIAN Pei-ning
ZHANG Na
ZHU Ya-di

Related Institution

Orbital Courtyard， Beijing Urban Construction Design and Development Group Co.， Ltd.
Intelligent Transportation Research Centre， Research Institute of Highway Ministry of Transport
Integrated Transportation Research Centre of China， Beijing Jiaotong University
Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport， Beijing Jiaotong University
School of Transportation Engineering， Chang'an University
0