EURO 2024 Copenhagen
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888. Integrated optimization of demand management and timetabling of an urban transit system: A Benders decomposition approach

Invited abstract in session WA-51: Timetabling 2, stream Public Transport Optimization.

Wednesday, 8:30-10:00
Room: M5 (building: 101)

Authors (first author is the speaker)

1. Yahan Lu
Delft University of Technology
2. Lixing Yang
Beijing Jiaotong University
3. Jiateng Yin
State Key Lab of Rail Traffic Control and Safety, Beijing Jiaotong University
4. Shadi Sharif Azadeh
Transport & Planning, TU Delft

Abstract

The intelligent upgrading of metropolitan rail transit systems has made it feasible to implement delicate demand management policies that synergize multiple strategies in practical operations. The interdependence of supply and demand motivates the following question: How to scientifically and rationally develop demand-side management policies and supply-side capacity allocations to enhance the whole urban rail transit ecosystem? To answer this question, we propose a mathematical and computational framework that optimizes train timetables, passenger flow control, and trip-shifting strategies, incorporating emerging trip reservations and service fairness. The problem is formulated as a nonlinear programming model and solved using a Benders decomposition-based solution algorithm within the branch-and-cut method. To further improve the computational efficiency, a novel decomposition method, and valid inequalities are developed, which decomposes the model into a train timetabling problem combined with partial passenger assignment and a passenger flow control problem. The computational results of real-world case studies based on the Beijing metro show that our solution method outperforms commercial solvers in terms of computational efficiency. Our proposed integrated optimization method consistently leads to a reduction of 16.3% in the system-wide waiting time of passengers while simultaneously reducing the number of operated trains.

Keywords

Status: accepted

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