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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.
Yongqiu Zhu; Rob M.P. Goverde. Dynamic railway timetable rescheduling for multiple connected disruptions. Transportation Research Part C: Emerging Technologies 2021, 125, 103080 .
AMA StyleYongqiu Zhu, Rob M.P. Goverde. Dynamic railway timetable rescheduling for multiple connected disruptions. Transportation Research Part C: Emerging Technologies. 2021; 125 ():103080.
Chicago/Turabian StyleYongqiu Zhu; Rob M.P. Goverde. 2021. "Dynamic railway timetable rescheduling for multiple connected disruptions." Transportation Research Part C: Emerging Technologies 125, no. : 103080.
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.
Yongqiu Zhu; Rob M.P. Goverde. Integrated timetable rescheduling and passenger reassignment during railway disruptions. Transportation Research Part B: Methodological 2020, 140, 282 -314.
AMA StyleYongqiu Zhu, Rob M.P. Goverde. Integrated timetable rescheduling and passenger reassignment during railway disruptions. Transportation Research Part B: Methodological. 2020; 140 ():282-314.
Chicago/Turabian StyleYongqiu Zhu; Rob M.P. Goverde. 2020. "Integrated timetable rescheduling and passenger reassignment during railway disruptions." Transportation Research Part B: Methodological 140, no. : 282-314.
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.
Yongqiu Zhu; Rob M.P. Goverde. Dynamic and robust timetable rescheduling for uncertain railway disruptions. Journal of Rail Transport Planning & Management 2020, 15, 100196 .
AMA StyleYongqiu Zhu, Rob M.P. Goverde. Dynamic and robust timetable rescheduling for uncertain railway disruptions. Journal of Rail Transport Planning & Management. 2020; 15 ():100196.
Chicago/Turabian StyleYongqiu Zhu; Rob M.P. Goverde. 2020. "Dynamic and robust timetable rescheduling for uncertain railway disruptions." Journal of Rail Transport Planning & Management 15, no. : 100196.
Passenger assignment models for major disruptions that require trains to be cancelled/short-turned in railway systems are rarely considered in literature, although these models could make a significant contribution to passenger-oriented disruption timetable design/rescheduling. This paper proposes a dynamic passenger assignment model, where the passengers who start travelling before, during and after the disruption are all considered. The model ensures that on-board passengers are given priority over waiting passengers, and waiting passengers are boarding under the first-come-first-serve rule. Moreover, the model allows information interventions by publishing information about service variations and train congestion at different locations with the aim of distributing passengers wisely to achieve less travel time increase due to the disruption. Discrete event simulation is adopted to implement the model, where loading/unloading procedures are realized and passengers re-plan their paths based on the information they receive. The model tracks individual travels, which helps to evaluate a disruption timetable in a passenger-oriented way.
Yongqiu Zhu; Rob Goverde. Dynamic Passenger Assignment for Major Railway Disruptions Considering Information Interventions. Networks and Spatial Economics 2019, 19, 1249 -1279.
AMA StyleYongqiu Zhu, Rob Goverde. Dynamic Passenger Assignment for Major Railway Disruptions Considering Information Interventions. Networks and Spatial Economics. 2019; 19 (4):1249-1279.
Chicago/Turabian StyleYongqiu Zhu; Rob Goverde. 2019. "Dynamic Passenger Assignment for Major Railway Disruptions Considering Information Interventions." Networks and Spatial Economics 19, no. 4: 1249-1279.
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 time.
Yongqiu Zhu; Rob M.P. Goverde. Railway timetable rescheduling with flexible stopping and flexible short-turning during disruptions. Transportation Research Part B: Methodological 2019, 123, 149 -181.
AMA StyleYongqiu Zhu, Rob M.P. Goverde. Railway timetable rescheduling with flexible stopping and flexible short-turning during disruptions. Transportation Research Part B: Methodological. 2019; 123 ():149-181.
Chicago/Turabian StyleYongqiu Zhu; Rob M.P. Goverde. 2019. "Railway timetable rescheduling with flexible stopping and flexible short-turning during disruptions." Transportation Research Part B: Methodological 123, no. : 149-181.
Passenger-oriented rescheduling problems receive increasing attention. However, the passenger assignment models used for evaluating the rescheduling solutions are usually simplified by many assumptions. To estimate passenger inconvenience more accurately, this paper establishes a dynamic passenger assignment model during disruptions, in which the time-dependent demand, disruption-induced service variations and vehicle capacities are all taken into account. Event-based simulation is adopted to implement the model of the dynamic loading and unloading procedures of passengers. Based on the model, individual travels can be tracked, thus making the estimation of individual passenger delay possible. By aggregating individual inconvenience, the performance of a given rescheduling solution/contingency plan can be evaluated. Furthermore, recommendations such as adding train units can also be proposed, as illustrated in the case study.
Yongqiu Zhu; Rob M. P. Goverde. Dynamic passenger assignment during disruptions in railway systems. 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) 2017, 146 -151.
AMA StyleYongqiu Zhu, Rob M. P. Goverde. Dynamic passenger assignment during disruptions in railway systems. 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS). 2017; ():146-151.
Chicago/Turabian StyleYongqiu Zhu; Rob M. P. Goverde. 2017. "Dynamic passenger assignment during disruptions in railway systems." 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) , no. : 146-151.