Priority based technique for rescheduling trains

No AbstractKeywords: rescheduling; mathematical modelling; service disruptions; priorit

Analysis of a Train-operating Company’s Customer Service System during Disruptions:Conceptual Requirements for Gamifying Frontline Staff Development

This paper provides an account of an action research study into the systemic success factors which help frontline staff react to and recover from a rail service disruption. This study focuses on the effective use of information during a disruption to improve customer service, as this is a priority area for train-operating companies (TOCs) in Great Britain. A novel type of systems thinking, known as Process-Oriented Holonic Modelling (PrOH), has been used to investigate and model the ‘Passenger Information During Disruption’ (PIDD) system. This paper presents conceptual requirements for a gamified learning environment; it describes ‘what’; ‘how’ and ‘when’ these systemic success factors could be gamified using a popular disruption management reference framework known as the Mitigate, Prepare, React and Recover (MPRR) framework. This paper will interest managers of and researchers into customer service system disruptions, as well as those wishing to develop new gamified learning environments to improve customer service systems

Real-Time Track Reallocation for Emergency Incidents at Large Railway Stations

After track capacity breakdowns at a railway station, train dispatchers need to generate appropriate track reallocation plans to recover the impacted train schedule and minimize the expected total train delay time under stochastic scenarios. This paper focuses on the real-time track reallocation problem when tracks break down at large railway stations. To represent these cases, virtual trains are introduced and activated to occupy the accident tracks. A mathematical programming model is developed, which aims at minimizing the total occupation time of station bottleneck sections to avoid train delays. In addition, a hybrid algorithm between the genetic algorithm and the simulated annealing algorithm is designed. The case study from the Baoji railway station in China verifies the efficiency of the proposed model and the algorithm. Numerical results indicate that, during a daily and shift transport plan from 8:00 to 8:30, if five tracks break down simultaneously, this will disturb train schedules (result in train arrival and departure delays)

Integrated optimization of train timetables rescheduling and response vehicles on a disrupted metro line

When an unexpected metro disruption occurs, metro managers need to reschedule timetables to avoid trains going into the disruption area, and transport passengers stranded at disruption stations as quickly as possible. This paper proposes a two-stage optimization model to jointly make decisions for two tasks. In the first stage, the timetable rescheduling problem with cancellation and short-turning strategies is formulated as a mixed integer linear programming (MILP). In particular, the instantaneous parameters and variables are used to describe the accumulation of time-varying passenger flow. In the second one, a system-optimal dynamic traffic assignment (SODTA) model is employed to dynamically schedule response vehicles, which is able to capture the dynamic traffic and congestion. Numerical cases of Beijing Metro Line 9 verify the efficiency and effectiveness of our proposed model, and results show that: (1) when occurring a disruption event during peak hours, the impact on the normal timetable is greater, and passengers in the direction with fewer train services are more affected; (2) if passengers stranded at the terminal stations of disruption area are not transported in time, they will rapidly increase at a speed of more than 300 passengers per minute; (3) compared with the fixed shortest path, using the response vehicles reduces the total travel time about 7%. However, it results in increased travel time for some passengers.Comment: 32 pages, 21 figure

The multi-objective railway timetable rescheduling problem

Unexpected disruptions occur for many reasons in railway networks and cause delays, cancelations, and, eventually, passenger inconvenience. This research focuses on the railway timetable rescheduling problem from a macroscopic point of view in case of large disruptions. The originality of our approach is to integrate three objectives to generate a disposition timetable: the passenger satisfaction, the operational costs and the deviation. from the undisrupted timetable. We formulate the problem as an Integer Linear Program that optimizes the first objective and includes epsilon-constraints for the two other ones. By solving the problem for different values of epsilon, the three-dimensional Pareto frontier can be explored to understand the trade-offs among the three objectives. The model includes measures such as canceling, delaying or rerouting the trains of the undisrupted timetable, as well as scheduling emergency trains. Furthermore, passenger flows are adapted dynamically to the new timetable. Computational experiments are performed on a realistic case study based on a heavily used part of the Dutch railway network. The model is able to find optimal solutions in reasonable computational times. The results provide evidence that adopting a demand-oriented approach for the management of disruptions not only is possible, but may lead to significant improvement in passenger satisfaction, associated with a low operational cost of the disposition timetable. (C) 2017 Elsevier Ltd. All rights reserved

Modelling disruptions and resolving conflicts optimally in a railway schedule

Resolving disruptions, by dispatching and rescheduling conflicting trains is an NP-complete problem. Earlier literature classify railway operations as: (i) tactical scheduling, (ii) operational scheduling, and (iii) rescheduling. We distinguish the three based on operational criticality. Existing optimisation models do not distinguish precisely between scheduling and rescheduling based on constraints modelling; the only difference is in their objective function. Our model is the first of its kind to incorporate disruptions in an MILP model and to include conflicts-resolving constraints in the model itself. The major advantage of such a formulation is that only those trains which are disrupted are rescheduled and other nonconflicting trains retain their original schedules. Our model reschedules disrupted train movements on both directions of a single track layout with an objective to minimise total delay of all trains at their destinations. Using a small sized data it is proved that all possible conflicts out of a disruption are resolved. Apart from achieving optimal resolutions, we infer through experimental verification that a non-standard dispatch ordering is a requisite for global optimality, as cogitated by other authors. (C) 2012 Elsevier Ltd. All rights reserved

Railway traffic scheduling with use of reinforcement learning

The reliability of railway traffic is commonly evaluated with train punctuality, where the\ud deviations of actual train arrivals/departures and train arrivals/departures published in the\ud timetable are compared. Minor train delays can be mitigated or even eliminated with running\ud time supplements, while major delays can lead to so-called secondary delays of other trains\ud on the network. Railway lines with high capacity utilization are more likely subject to delays,\ud since a greater number of trains means a larger number of potential conflicts and more\ud interactions between trains. Consequently, the secondary delays are harder to limit. Railway\ud manager and carrier personnel are responsible for safe, undisturbed and punctual railway\ud traffic. But unforeseen events can lead to delays, which calls for train rescheduling, where\ud new train arrivals and departures are calculated. Train rescheduling is a complex\ud optimization problem, currently solved based on dispatcher’s expert knowledge. With the\ud increasing number of trains the complexity of the problem grows, the need for a decision\ud support system increases. Train rescheduling is considered an NP-complete problem, where\ud conventional mathematical and computer optimization methods fail to find the optimal\ud solution, but artificial intelligence approaches have some measure of success. In this\ud dissertation an algorithm for train rescheduling based on reinforcement learning, more\ud precisely Q-learning, was developed. The Q-learning agent learns from rewards and\ud punishments received from the environment, and looks for the optimal train dispatching\ud strategy depending on the objective function