Operations research in passenger railway transportation

In this paper, we give an overview of state-of-the-art OperationsResearch models and techniques used in passenger railwaytransportation. For each planning phase (strategic, tactical andoperational), we describe the planning problems arising there anddiscuss some models and algorithms to solve them. We do not onlyconsider classical, well-known topics such as timetabling, rollingstock scheduling and crew scheduling, but we also discuss somerecently developed topics as shunting and reliability oftimetables.Finally, we focus on several practical aspects for each of theseproblems at the largest Dutch railway operator, NS Reizigers.passenger railway transportation;operation research;planning problems

Phase Synchronization in Railway Timetables

Timetable construction belongs to the most important optimization problems in public transport. Finding optimal or near-optimal timetables under the subsidiary conditions of minimizing travel times and other criteria is a targeted contribution to the functioning of public transport. In addition to efficiency (given, e.g., by minimal average travel times), a significant feature of a timetable is its robustness against delay propagation. Here we study the balance of efficiency and robustness in long-distance railway timetables (in particular the current long-distance railway timetable in Germany) from the perspective of synchronization, exploiting the fact that a major part of the trains run nearly periodically. We find that synchronization is highest at intermediate-sized stations. We argue that this synchronization perspective opens a new avenue towards an understanding of railway timetables by representing them as spatio-temporal phase patterns. Robustness and efficiency can then be viewed as properties of this phase pattern

Disruption management in passenger railway transportation.

This paper deals with disruption management in passengerrailway transportation. In the disruption management process, manyactors belonging to different organizations play a role. In this paperwe therefore describe the process itself and the roles of thedifferent actors.Furthermore, we discuss the three main subproblems in railwaydisruption management: timetable adjustment, and rolling stock andcrew re-scheduling. Next to a general description of these problems,we give an overview of the existing literature and we present somedetails of the specific situations at DSB S-tog and NS. These arethe railway operators in the suburban area of Copenhagen, Denmark,and on the main railway lines in the Netherlands, respectively.Since not much research has been carried out yet on OperationsResearch models for disruption management in the railway context,models and techniques that have been developed for related problemsin the airline world are discussed as well.Finally, we address the integration of the re-scheduling processesof the timetable, and the resources rolling stock and crew.

Railway timetabling from an operations research

In this paper we describe Operations Research (OR) models andtechniques that can be used for determining (cyclic) railwaytimetables. We discuss the two aspects of railway timetabling: ($i$)the determination of arrival and departure times of the trains atthe stations and other relevant locations such as junctions andbridges, and ($ii$) the assignment of each train to an appropriateplatform and corresponding inbound and outbound routes in everystation. Moreover, we discuss robustness aspects of bothsubproblems.

Timing of Train Disposition: Towards Early Passenger Rerouting in Case of Delays

Passenger-friendly train disposition is a challenging, highly complex online optimization problem with uncertain and incomplete information about future delays. In this paper we focus on the timing within the disposition process. We introduce three different classification schemes to predict as early as possible the status of a transfer: whether it will almost surely break, is so critically delayed that it requires manual disposition, or can be regarded as only slightly uncertain or as being safe. The three approaches use lower bounds on travel times, historical distributions of delay data, and fuzzy logic, respectively. In experiments with real delay data we achieve an excellent classification rate. Furthermore, using realistic passenger flows we observe that there is a significant potential to reduce the passenger delay if an early rerouting strategy is applied

Line planning with user-optimal route choice

We consider the problem of designing lines in a public transport system, where we include user-optimal route choice. The model we develop ensures that there is enough capacity present for every passenger to travel on a shortest route. We present two different integer programming formulations for this problem, and discuss exact solution approaches. To solve large-scale line planning instances, we also implemented a genetic solution algorithms. We test our algorithms in computational experiments using randomly generated instances along realistic data, as well as a realistic instance modeling the German long-distance network. We examine the advantages and disadvantages of using such user-optimal solutions, and show that our algorithms sufficiently scale with instance size to be used for practical purposes

Sensitivity Analysis and Coupled Decisions in Passenger Flow-Based Train Dispatching

Frequent train delays make passenger-oriented train dispatching a task of high practical relevance. In case of delays, dispatchers have to decide whether trains should wait for one or several delayed feeder trains or should depart on time. To support dispatchers, we have recently introduced the train dispatching framework PANDA (CASPT 2015). In this paper, we present and evaluate two enhancements which are also of general interest. First, we study the sensitivity of waiting decisions with respect to the accuracy of passenger flow data. More specifically, we develop an integer linear programming formulation for the following optimization problem: Given a critical transfer, what is the minimum number of passengers we have to add or to subtract from the given passenger flow such that the decision would change from waiting to non-waiting or vice versa? Based on experiments with realistic passenger flows and delay data from 2015 in Germany, an important empirical finding is that a significant fraction of all decisions is highly sensitive to small changes in passenger flow composition. Hence, very accurate passenger flows are needed in these cases. Second, we investigate the practical value of more sophisticated simulations. A simple strategy evaluates the effect of a waiting decision of some critical transfer on passenger delay subject to the assumption that all subsequent decisions are taken according to standard waiting time rules, as usually employed by railway companies like Deutsche Bahn. Here we analyze the impact of a higher level of simulation where waiting decisions for a critical transfer are considered jointly with one or more other decisions for subsequent transfers. We learn that such "coupled decisions" lead to improved solution in about 6.3% of all considered cases

Railway Crew Rescheduling with Retiming

Railway operations are disrupted frequently, e.g. the Dutch railway network experiences about three large disruptions per day on average. In such a disrupted situation railway operators need to quickly adjust their resource schedules. Nowadays, the timetable, the rolling stock and the crew schedule are recovered in a sequential way. In this paper, we model and solve the crew rescheduling problem with retiming. This problem extends the crew rescheduling problem by the possibility to delay the departure of some trains. In this way we partly integrate timetable adjustment and crew rescheduling. The algorithm is based on column generation techniques combined with Lagrangian heuristics. In order to prevent a large increase in computational time, retiming is allowed only for a limited number of trains where it seems very promising. Computational experiments with real-life disruption data show that, compared to the classical approach, it is possible to find better solutions by using crew rescheduling with retiming.

Multi-objective model for optimizing railway infrastructure asset renewal

Trabalho inspirado num problema real da empresa Infraestruturas de Portugal, EP.A multi-objective model for managing railway infrastructure asset renewal is presented. The model aims to optimize three objectives, while respecting operational constraints: levelling investment throughout multiple years, minimizing total cost and minimizing work start postponements. Its output is an optimized intervention schedule. The model is based on a case study from a Portuguese infrastructure management company, which specified the objectives and constraints, and reflects management practice on railway infrastructure. The results show that investment levelling greatly influences the other objectives and that total cost fluctuations may range from insignificant to important, depending on the condition of the infrastructure. The results structure is argued to be general and suggests a practical methodology for analysing trade-offs and selecting a solution for implementation.info:eu-repo/semantics/publishedVersio

Passenger-Aware Real-Time Planning of Short Turns to Reduce Delays in Public Transport

Delays and disruptions are commonplace in public transportation. An important tool to limit the impact of severely delayed vehicles is the use of short turns, where a planned trip is shortened in order to be able to resume the following trip in the opposite direction as close to the schedule as possible. Short turns have different effects on passengers: some suffer additional delays and have to reschedule their route, while others can benefit from them. Dispatchers therefore need decision support in order to use short turns only if the overall delay of all affected passengers is positively influenced. In this paper, we study the planning of short turns based on passenger flows. We propose a simulation framework which can be used to decide upon single short turns in real time. An experimental study with a scientific model (LinTim) of an entire public transport system for the German city of Stuttgart including busses, trams, and local trains shows that we can solve these problems on average within few milliseconds. Based on features of the current delay scenario and the passenger flow we use machine learning to classify cases where short turns are beneficial. Depending on how many features are used, we reach a correct classification rate of more than 93% (full feature set) and 90% (partial feature set) using random forests. Since precise passenger flows are often not available in urban public transportation, our machine learning approach has the great advantage of working with significantly less detailed passenger information