Yongqiu Zhu

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Zhu

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Yongqiu

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0000-0002-7454-8664

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Publications 1 - 10 of 19

Dynamic and robust timetable rescheduling for uncertain railway disruptions
Zhu, Yongqiu; Goverde, Rob M.P. (2019)
Dynamic and robust timetable rescheduling for uncertain railway disruptions
Zhu, Yongqiu; Goverde, Rob M.P. (2020)
Journal of Rail Transport Planning & Management
Unexpected disruptions occur frequently in the railways, during which many train services cannot run as scheduled. This paper deals with timetable rescheduling during such disruptions, particularly in the case where all tracks between two stations are blocked for hours. In practice, a disruption may become shorter or longer than predicted. To take the uncertainty of the disruption duration into account, this paper formulates the timetable rescheduling as a rolling horizon two-stage stochastic programming problem in deterministic equivalent form. The random disruption duration is assumed to have a finite number of possible realizations, called scenarios, with given probabilities. Every time a prediction about the range of the disruption end time is updated, new scenarios are defined, and a two-stage stochastic model computes the optimal rescheduling solution to all these scenarios. The stochastic method was tested on a part of the Dutch railways, and compared to a deterministic rolling-horizon method. The results showed that compared to the deterministic method, the stochastic method is more likely to generate better rescheduling solutions for uncertain disruptions by less train cancellations and/or delays, while the solution robustness can be affected by the predicted range regarding the disruption end time.
Handling uncertainty in train timetable rescheduling
Zhan, Shuguang; Xie, Jiemin; Wong, S.C.; et al. (2024)
Transportation Research Part E: Logistics and Transportation Review
External and internal factors can cause disturbances or disruptions in daily train operations, leading to deviations from official timetables and passenger delays. As a result, efficient train timetable rescheduling (TTR) methods are necessary to restore disrupted train services. Although TTR has been a popular research topic in recent years, the uncertain characteristics of railways have not been sufficiently addressed. This review first identifies the primary uncertainties of TTR and examines their impacts on both TTR and passenger routing during disturbances or disruptions. It finds that only a few uncertainties have been investigated, and the existing solution methods do not adequately meet practical requirements, such as considering the dynamic nature of disturbances or disruptions, which is crucial for real-world applications. Therefore, the review highlights problems associated with TTR uncertainties that need urgent attention and suggests promising methodologies that could effectively address these issues as future research directions. This review aims to help practitioners develop improved automatic train-dispatching systems with better train-rescheduling performance under disturbances or disruptions compared to current systems.
Robust cooperative train trajectory optimization with stochastic delays under virtual coupling
Wang, Pengling; Zhu, Yongqiu; Zhu, Wei (2023)
IET Intelligent Transport Systems
Virtual coupling technology was recently proposed in railways, which separates trains by a relative braking distance (or even shorter distance) and moves trains synchronously to increase capacity at bottlenecks. This study proposes a real-time cooperative train trajectory planning algorithm for coordinating train movements under virtual coupling by considering stochastic initial delays. The algorithm uses mixed-integer programming models to estimate the delay propagation among trains, detect feasible coupled-running locations, and optimize the trajectories of the two trains such that they coordinate their speeds to achieve energy-efficient, punctual movements, as well as a safe coupled-running process. A robust optimization method is proposed to capture the stochastic delays as an uncertainty set, which is reformulated to its dual problem. Case studies of planning train trajectories for the classical virtual-coupling scenario suggest that (1) the coupled-running distance is greatly affected by the coordination of train timetables, delays, and safe separation constraints at switches; (2) the coordination of train movements for a coupled-running process imposes extra energy costs; and (3) the proposed method can detect feasible coupled-running locations and produce cooperative speed profiles in short computational times.
Train traffic control in merging stations: A data-driven approach
Huang, Ping; Li, Zhongcan; Zhu, Yongqiu; et al. (2023)
Transportation Research Part C: Emerging Technologies
Railway operations are subject to deviations from the planned schedule, i.e., delays. In those situations, high-quality traffic control actions are needed to reduce the delays. Existing studies mainly used prescriptive techniques (e.g., mathematical programming, heuristics) to identify the best control action. These methods have limitations in the firm reliance on deterministic parameters prescriptively or normatively determined beforehand, and little understandability by the practitioners. These drawbacks hinder their acceptance in practice. This study exploits instead past realization data to provide decision support for traffic control. The realized data describe the traffic control actions taken by human controllers, and their effects; those latter are more complex than a linear sum of predetermined parameters. We use decision graphs to identify which traffic control action leads to the best solution, in terms of reduction of delays, based on the past performance of the same action in similar conditions. We are also able to explain the reasons and the factors that lead to each suggested action. We focus on the relevant case of merging stations, where multiple lines merge as one line, deciding the relative order between two consecutive trains. The method determines the stochastic effects of the two possible decisions at merge points, which allows for choosing the best one. Compared within the framework of realized data, the action suggested is the best out of a series of benchmarks, including simple rules and optimization, improving (reducing delays) approximately 11.7% on the common benchmarks. The variables with the highest impact on the utility are the length of the planned dwell time and the planned presence of an overtaking. The variables influencing the utility most are the actual delays of trains, the train type, and the order actually implemented.
Integrated timetable rescheduling and passenger reassignment during railway disruptions
Zhu, Yongqiu; Goverde, Rob M.P. (2020)
Transportation Research Part B: Methodological
During railway disruptions, most passengers may not be able to find preferred alternative train services due to the current way of handling disruptions that does not take passenger responses into account. To offer better alternatives to passengers, this paper proposes a novel passenger-oriented timetable rescheduling model, which integrates timetable rescheduling and passenger reassignment into a Mixed Integer Linear Programming model with the objective of minimizing generalized travel times: in-vehicle times, waiting times at origin/transfer stations and the number of transfers. The model applies the dispatching measures of re-timing, re-ordering, cancelling, flexible stopping and flexible short-turning trains, handles rolling stock circulations at both short-turning and terminal stations of trains, and takes station capacity into account. To solve the model efficiently, an Adapted Fix-and-Optimize (AFaO) algorithm is developed. Numerical experiments were carried out to a part of the Dutch railways. The results show that the proposed passenger-oriented timetable rescheduling model is able to shorten generalized travel times significantly compared to an operator-oriented timetable rescheduling model that does not consider passenger responses. By allowing only 10 min more train delay than an optimal operator-oriented rescheduling solution, the passenger-oriented model is able to shorten the generalized travel times over all passengers by thousands of minutes in all considered disruption scenarios. With a passenger-oriented rescheduled timetable, more passengers continue their train travels after a disruption started, compared to a rescheduled timetable from the operator-oriented model. The AFaO algorithm obtains high-quality solutions to the passenger-oriented model in up to 300 s.
Dynamic railway timetable rescheduling for multiple connected disruptions
Zhu, Yongqiu; Goverde, Rob M.P. (2021)
Transportation Research Part C: Emerging Technologies
Unexpected disruptions occur in the railways on a daily basis, which are typically handled manually by experienced traffic controllers with the support of predefined contingency plans. When several disruptions occur simultaneously, it is rather hard for traffic controllers to make rescheduling decisions, because (1) the predefined contingency plans corresponding to these disruptions may conflict with each other and (2) no predefined contingency plan considering the combined effects of multiple disruptions is available. This paper proposes a Mixed Integer Linear Programming (MILP) model to reschedule the timetable in case of multiple disruptions that occur at different geographic locations but have overlapping periods and are pairwise connected by at least one train line. The dispatching measures of retiming, reordering, cancelling, adding stops and flexible short-turning are formulated in the MILP model that also considers the rolling stock circulations at terminal stations and platform capacity. We develop two approaches for rescheduling the timetable in a dynamic environment: the sequential approach and the combined approach. In the sequential approach, a single-disruption rescheduling model is applied to handle each new disruption with the last solution as reference. In the combined approach, the multiple-disruption rescheduling model is applied every time an extra disruption occurs by considering all ongoing disruptions. A rolling-horizon solution method to the multiple-disruption model has been developed to handle long multiple connected disruptions in a more efficient way. The sequential and combined approaches have been tested on real-life instances on a subnetwork of the Dutch railways with 38 stations and 10 train lines operating half-hourly in each direction. In a few cases, the sequential approach did not find feasible solutions, while the combined approach obtained the solutions for all considered cases. Besides, the combined approach was able to find solutions with less cancelled train services and/or train delays than the sequential approach. For long disruptions, the proposed rolling-horizon method was able to generate high-quality rescheduling solutions in an acceptable time.
Railway timetable rescheduling with flexible stopping and flexible short-turning during disruptions
Goverde, Rob M.P.; Zhu, Yongqiu; Goverde, Rob M.P. (2019)
Transportation Research Part B: Methodological
Railway operations are vulnerable to unexpected disruptions that should be handled in an efficient and passenger-friendly way. To this end, we propose a timetable rescheduling model where flexible stopping (i.e. skipping stops and adding stops) and flexible short-turning (i.e. full choice of short-turn stations) are innovatively integrated with three other dispatching measures: retiming, reordering, and cancelling. The Mixed Integer Linear Programming model also ensures that each train serving a station is ensured with a platform track. To consider the rescheduling impact on passengers, the weight of each decision is estimated individually according to the time-dependent passenger demand. The objective is minimizing passenger delays. A case study is carried out for hundreds of disruption scenarios on a subnetwork of the Dutch railways. It is found that (1) applying a mix of flexible stopping and flexible short-turning results in less passenger delays; (2) shortening the recovery duration mitigates the post-disruption consequence by less delay propagation but is at the expense of more cancelled train services during the disruption; and (3) the optimal rescheduling solution is sensitive to the disruption duration, but some steady behaviour is observed when the disruption duration increases by the timetable cycle
Reinforcement learning in railway timetable rescheduling
Zhu, Yongqiu; Wang, Hongrui; Goverde, Rob M.P. (2020)
2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)
Real-time railway traffic management is important for the daily operations of railway systems. It predicts and resolves operational conflicts caused by events like excessive passenger boardings/alightings. Traditional optimization methods for this problem are restricted by the size of the problem instances. Therefore, this paper proposes a reinforcement learning-based timetable rescheduling method. Our method learns how to reschedule a timetable off-line and then can be applied online to make an optimal dispatching decision immediately by sensing the current state of the railway environment. Experiments show that the rescheduling solution obtained by the proposed reinforcement learning method is affected by the state representation of the railway environment. The proposed method was tested to a part of the Dutch railways considering scenarios with single initial train delays and multiple initial train delays. In both cases, our method found high-quality rescheduling solutions within limited training episodes.
Information to passengers under overcrowding situations
Zhu, Yongqiu; Corman, Francesco (2023)
RailBelgrade 2023: Book of Abstracts
In public transport systems, vehicles can sometimes be so crowded that passengers experience onboard discomfort or are even denied boarding. The overcrowding situations can happen during peak hours of a day or during special events (e.g. music festivals and sports) that introduce huge travel demand for specific stations. Publishing the information about the overcrowded vehicles could benefit some passengers but at the same time negatively affect others. In this paper, we show the (dis)advantages of information provision to specific passengers during overcrowding, and investigate the conditions under which providing information is good or not from the perspective of the whole system. We develop different information provision strategies, which differ in the information content; and analyze the effects of these strategies considering the resulting delays over all passengers in our designed overcrowding scenarios. The results indicate that whether to give information and how much information should be given are relevant to the ratio between the benefiting demand and the negatively- affected demand, the proportions of passengers who can benefit from any information and the passengers who can only benefit from specific information, and the travel time differences between different alternatives. According to the results, we suggest that operators can play with the information to distribute the passenger flows towards a system-optimum condition that leads to the smallest passenger delays when vehicle capacities are in short supply.

Publications 1 - 10 of 19

Yongqiu Zhu

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