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.