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This paper introduces distributed optimization approaches, with the aim of improving the computational efficency of an integrated optimization problem for large-scale railway net-works. We first propose three decomposition methods to decompose the whole problem into a number of subproblems, namely a geography-based (GEO), a train-based (TRA), and a time-interval-based (TIN) decomposition respectively. As a result of the decomposition, couplings exist among the subproblems, and the presence of these couplings leads to a non-separable structure of the whole problem. To handle this issue, we further introduce three distributed optimization approaches. An Alternating Direction Method of Multipliers (ADMM) algorithm is developed to solve each subproblem through coordination with the other subproblems in an iterative manner. A priority-rule-based (PR) algorithm is proposed to sequentially and iteratively solve the subproblems in a priority order with respect to the solutions of the other subproblems solved with a higher priority. A Cooperative Distributed Robust Safe But Knowledgeable (CDRSBK) algorithm is presented, where four types of couplings are de?ned and each subproblem is iteratively solved together with its actively coupled subproblems. Experiments are conducted based on the Dutch railway network to comparatively examine the performance of the three proposed algorithms with the three decomposition methods, in terms of feasibility, computational efficiency, solution quality, and estimated optimality gap. Overall, the combinations GEO-ADMM, TRA-ADMM, and TRA-CDRSBK yield better performance. Based on our findings, a feasible solution can be found quickly by using TRA-ADMM, and then a better solution can be potentially obtained by GEO-ADMM or TRA-CDRSBK at the cost of more CPU time., Linköping Electronic Conference Proceedings, 69, ISSN:1650-3686, ISSN:1650-3740, RailNorrköping 2019. 8th International Conference on Railway Operations Modelling and Analysis (ICROMA), Norrköping, Sweden, June 17th – 20th, 2019, ISBN:978-91-7929-992-7

This study aims to investigate delays, delay increases, and delay recovery characteristics, by using statistical methods to clarify delay propagation patterns according to historical records of the Wuhan-Guangzhou high-speed railway (HSR) in China in 2014 and 2015. Specifically, we examined arrival and departure delay duration distributions and used heatmaps to demonstrate the spatiotemporal frequency distribution of delays, delay increases, and delay recovery, and the heatmaps clearly show hot spots (coordinates with high frequencies) in a timetable. Then, we separated delays as discrete intervals according to their severity, and analyzed the delay increasing frequency and the delay increasing severity within each interval, so as to clarify the relationships of delay increasing probability and delay increasing severity with delay extents. Next, we investigated the observed delay recoveries and prescheduled buffer times at (in) station (section), which demonstrate the recovery ability of each station and section. Finally, to understand the key influencing factor of delay propagation, we analyzed the relationship between capacity utilization and delays, delay increases, and delay recoveries, by examining their Pearson correlation coefficients. These indicate that delay frequencies and delay increasing frequencies with Pearson correlation coefficients as high as 0.9 are highly dependent on capacity utilization. The uncovered delay propagation patterns can enrich dispatchers’ experience, and improve their decision-making ability during real-time dispatching in HSR., Linköping Electronic Conference Proceedings, 69 (29), ISSN:1650-3686, ISSN:1650-3740, RailNorrköping 2019. 8th International Conference on Railway Operations Modelling and Analysis (ICROMA), Norrköping, Sweden, June 17th – 20th, 2019, ISBN:978-91-7929-992-7