Decomposition and distributed optimization of real-time traffic management for large-scale railway networks. (November 2020)

• Record Type: Journal Article
• Authors: Luan, Xiaojie; De Schutter, Bart; Meng, Lingyun; Corman, Francesco
• Is Part Of: Transportation research. Volume 141(2020)
• Page Start: 72
• Page End: 97
• Publication Date: 2020-11
• Subjects: Real-time traffic management -- Decomposition -- Distributed optimization -- Large-scale railway network; Transportation -- Research -- Periodicals; Transportation -- Mathematical models -- Periodicals
• DOI: 10.1016/j.trb.2020.09.004
• Languages: English
• ISSNs: 0191-2615

Abstract

Highlights: We address the real-time railway traffic management for large-scale networks microscopically. We propose three decomposition methods, based on geography, trains, and time intervals, to partition a large-scale problem into some small-scale subproblems. We present three distributed optimization approaches, namely ADMM, PR, and CDRSBK, for achieving the coordination of subproblems. The proposed CDRSBK algorithm in the case of the train-based decomposition can find an optimal or near-optimal solution within 10 s of computation time. Abstract: This paper introduces decomposition and distributed optimization approaches for the real-time railway traffic management problem considering microscopic infrastructure characteristics, aiming at an improved computational efficiency when tackling large-scale railway networks. Based on the nature of the railway traffic management problem, we consider three decomposition methods, namely a geography-based (GEO) decomposition, a train-based (TRA) decomposition, and a time-interval-based (TIN) decomposition, in order to partition the large railway traffic management optimization problem into several subproblems. In particular, an integer linear programming (ILP) model is developed to generate the optimal GEO solution, with the objectives of minimizing the number of interconnections among regions and of balancing the size of regions. The decomposition creates couplings among the subproblems, in terms of either capacity usage or transit time consistency; therefore the whole problem gets a non-separable structure. To handle the couplings, we introduce three distributed optimization approaches, namely an Alternating Direction Method of Multipliers (ADMM) algorithm, a priority-rule-based (PR) algorithm, and a Cooperative Distributed Robust Safe But Knowledgeable (CDRSBK) algorithm, which operate iteratively. We test all combinations of the three decomposition methods and the three distributed optimization algorithms on a large-scale railway network in the South-East of the Netherlands, in terms of feasibility, computational efficiency, and optimality. Overall the CDRSBK algorithm with the TRA decomposition performs best, where high-quality (optimal or near-optimal) solutions can be found within 10 s of computation time.