Research on Dynamic Train Scheduling for Shanghai Metro Based on Enhanced PageRank and Data Fusion
Year: 2025
Type: article
Abstract: This paper addresses the issues of idle capacity and peak congestion in Shanghai Metro caused by uneven spatiotemporal passenger flow distribution. It proposes a dynamic train scheduling model integrating an improved PageRank algorithm. The study pioneers a three-part data fusion architecture combining "section flow data, timetable data, and transfer station observations." By employing the Expectation-Maximization (EM) algorithm to back-propagate dynamic origin-destination (OD) matrices from publicly available section flow data, it effectively resolves the challenge of obtaining complete passenger flow data. At the algorithmic level, an innovative dual-mechanism PageRank model is designed: a time-sensitive factor (λ=0.7) quantifies passenger flow variation rates, while capacity-adaptive weights (β=1.2) reflect station load states, precisely capturing the dynamic characteristics of station importance. Based on this, a multi-objective spatiotemporal network optimization model is constructed, embedding station importance parameters into decision variable constraints such as headway intervals and train formation quantities. Experimental results demonstrate: Compared to fixed timetables, this model reduces average passenger waiting time during peak hours by 22.4% (measured from 158s to 123s), decreases train empty-run rates during off-peak periods by 31.7%, and achieves a 17.3% improvement in multi-objective Pareto solution sets over the genetic algorithm benchmark. Field validation at People's Square Station (sample size: 300 passengers) demonstrated a waiting time prediction error of only 3.2 seconds (MAPE=2.1%) and an overall reliability of 92.7%, providing an innovative solution for dynamic scheduling in urban rail transit.
Author: Han Kaihui
Institution: Shandong University of Technology
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4415838777
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