Line planning under crowding: A cut-and-column generation approach
Yahan Lu, Rolf N. van Lieshout, Layla Martin, Lixing Yang
TL;DR
This paper addresses line planning under crowding by formulating a mixed-integer second-order cone program (MI-SOCP) and solving it with a cut-and-column generation framework complemented by a diving heuristic. It introduces a quadratic crowding penalty within a change-and-go network to capture crowding effects and demonstrates scalability to large-scale networks, including the Beijing metro, while achieving notable reductions in crowding and only modest travel-time penalties. The key contributions are the MI-SOCP reformulation, three accelerated cut-and-column methods (FCTP, FPTC, APAC), and an evaluative framework using Wardrop user equilibrium to assess routing realism; results indicate significant crowding reductions with limited deterioration in average travel time, and only slight deviations between system-optimal and user-equilibrium routing. The practical impact lies in offering a computational approach capable of yielding high-quality, deployable line plans for dense urban networks, guiding operators toward more efficient use of existing infrastructure under budget constraints.
Abstract
Problem definition: To mitigate excessive crowding in public transit networks, network expansion is often not feasible due to financial and time constraints. Instead, operators are required to make use of existing infrastructure more efficiently. In this regard, this paper considers the problem of determining lines and frequencies in a public transit system, factoring in the impact of crowding. Methodology: We introduce a novel formulation to address the line planning problem under crowding and propose a mixed-integer second-order cone programming (MI-SOCP) reformulation. Three variants of the cut-and-column generation algorithm with tailored acceleration techniques find near-system-optimal solutions by dynamically generating passenger routes and adding linear cutting planes to deal with the non-linearity introduced by the crowding terms. We find integral solutions using a diving heuristic. In practice, passengers may deviate from system-optimal routes. We, thus, evaluate line plans by computing a user-equilibrium routing based on Wardrop's first principle. Results and implications: We experimentally evaluate the performance of the proposed approaches on both an artificial network and the Beijing metro network. The results demonstrate that our algorithm effectively scales to large-scale instances involving hundreds of stations and candidate lines, and nearly 57,000 origin-destination pairs. We find that considering crowding while developing line plans can significantly reduce crowding, at only a minor expense to the travel time passengers experience. This holds both for system-optimal passenger routing and user-optimal passenger routing, which only differ slightly.