Evaluating the Applicability of Advanced Techniques for Practical Real-time Train Scheduling

AbstractThis paper reports on the practical applicability of published techniques for real-time train scheduling. The final goal is the development of an advanced decision support system for supporting dispatchers’ work and for guiding them toward near-optimal real-time re-timing, re-ordering and re-routing decisions. The paper focuses on the optimization system AGLIBRARY that manages trains at the microscopic level of block sections and block signals and at a precision of seconds. The system outcome is a detailed conflict-free train schedule, being able to avoid deadlocks and to minimize train delays. Experiments on a British railway nearby London demonstrate that AGLIBRARY can quickly compute near-optimal solutions

Analyzing the theoretical capacity of railway networks with a radial-backbone topology

In this work we propose a mechanism to optimize the capacity of the main corridor within a railway network with a radial-backbone or X-tree structure. The radial-backbone (or Xtree) structure is composed of two types of lines: the primary lines that travel exclusively on the common backbone (main corridor) and radial lines which, starting from the common backbone, branch out to individual locations. We define possible line configurations as binary strings and propose operators on them for their analysis, yielding an effective algorithm for generating an optimal design and train frequencies. We test our algorithm on real data for the high speed line Madrid-Seville. A frequency plan consistent with the optimal capacity is then proposed in order to eliminate the number of transfers between lines as well as to minimize the network fleet size, determining the minimum number of vehicles needed to serve all travel demand at maximum occupancy.Ministerio de Economía y Competitividad MTM2012-37048Junta de Andalucía P09-TEP-5022Junta de Andalucía P10-FQM-5849Canadian Natural Sciences and Engineering Research Council 39682-1

Train-scheduling optimization model for railway networks with multiplatform stations

This paper focuses on optimizing the schedule of trains on railway networks composed of busy complex stations. A mathematical formulation of this problem is provided as a Mixed Integer Linear Program (MILP). However, the creation of an optimal new timetable is an NP-hard problem; therefore, the MILP can be solved for easy cases, computation time being impractical for more complex examples. In these cases, a heuristic approach is provided that makes use of genetic algorithms to find a good solution jointly with heuristic techniques to generate an initial population. The algorithm was applied to a number of problem instances producing feasible, though not optimal, solutions in several seconds on a laptop, and compared to other proposals. Some improvements are suggested to obtain better results and further improve computation time. Rail transport is recognized as a sustainable and energy-efficient means of transport. Moreover, each freight train can take a large number of trucks off the roads, making them safer. Studies in this field can help to make railways more attractive to travelers by reducing operative cost, and increasing the number of services and their punctuality. To improve the transit system and service, it is necessary to build optimal train scheduling. There is an interest from the industry in automating the scheduling process. Fast computerized train scheduling, moreover, can be used to explore the effects of alternative draft timetables, operating policies, station layouts, and random delays or failures.Postprint (published version

An Alternative Approach for High Speed Railway Carrying Capacity Calculation Based on Multiagent Simulation

It is a multiobjective mixed integer programming problem that calculates the carrying capacity of high speed railway based on mathematical programming method. The model is complex and difficult to solve, and it is difficult to comprehensively consider the various influencing factors on the train operation. The multiagent theory is employed to calculate high speed railway carrying capacity. In accordance with real operations of high speed railway, a three-layer agent model is developed to simulate the operating process of high speed railway. In the proposed model, railway network agent, line agent, station agent, and train agent are designed, respectively. To validate the proposed model, a case study is performed for Beijing–Shanghai high speed railway by using NetLogo software. The results are consistent with the actual data, which implies that the proposed multiagent method is feasible to calculate the carrying capacity of high speed railway

Integer programming based solution approaches for the train dispatching problem

Railroads face the challenge of competing with the trucking industry in a fastpaced environment. In this respect, they are working toward running freight trains on schedule and reducing travel times. The planned train schedules consist of departure and arrival times at main stations on the rail network. A detailed timetable, on the other hand, consists of the departure and arrival times of each train in each track section of its route. The train dispatching problem aims to determine detailed timetables over a rail network in order to minimize deviations from the planned schedule. We provide a new integer programming formulation for this problem based on a spacetime network; we propose heuristic algorithms to solve it and present computational results of these algorithms. Our approach includes some realistic constraints that have not been previously considered as well as all the assumptions and practical issues considered by the earlier works

Locating optimal timetables and vehicle schedules in a transit line

This paper deals with the Transit Network Timetabling and Scheduling Problem (TNTSP) in a public transit line. The TNTSP aims at determining optimal timetables for each line in a transit network by establishing departure and arrival times of each vehicle at each station. We assume that customers know departure times of line runs offered by the system. However, each user, traveling after or before than their desired travel time, will give rise to an inconvenience cost, or a penalty cost if that user cannot be served according to the scheduled timetable. The provided formulation allocates each user to the best possible timetable considering capacity constraints. The problem is formulated using a p-median based approach and solved using a clustering technique. Computational results that show useful applications of this methodology are also included.Ministerio de Ciencia e Innovación MTM2010-19576-C02-01Ministerio de Ciencia e Innovación MTM2009-14243Ministerio de Ciencia e Innovación MTM2012-37048Junta de Andalucía P09-TEP-5022Junta de Andalucía FQM-584

A Set Packing Inspired Method for Real-Time Junction Train Routing

Efficiently coordinating the often large number of interdependent, timetabled train movements on a railway junction, while satisfying a number of operational requirements, is one of the most important problems faced by a railway company. The most critical variant of the problem arises on a daily basis at major railway junctions where disruptions to rail traffi c make the planned schedule/routing infeasible and rolling stock planners are forced to reschedule/re-route trains in order to recover feasibility. The dynamic nature of the problem means that good solutions must be obtained quickly. In this paper we describe a set packing inspired formulation of this problem and develop a branch-and-price based solution approach. A real life test instance arising in Germany and supplied by the major German railway company, Deutsche Bahn, indicates the efficiency of the proposed approach by confirming that practical problems can be solved to within a few percent of optimality in reasonable time

A novel two-stage approach for energy-efficient timetabling for an urban rail transit network

Urban rail transit (URT) is the backbone transport mode in metropolitan areas to accommodate large travel demands. The high energy consumption of URT becomes a hotspot problem due to the ever-increasing operation mileages and pressing agendas of carbon neutralization. The high model complexity and inconsistency in the objectives of minimizing passenger travel time and operational energy consumption are the main challenges for energy-efficient timetabling for a URT network with multiple interlinked lines. This study proposes a general model framework of timetabling and passenger path choice in a URT network to minimize energy consumption under passenger travel time constraints. To obtain satisfactory energy-efficient nonuniform timetables, we suggest a novel model reformulation as a tree knapsack problem to determine train running times by a pseudo-polynomial dynamic programming algorithm in the first stage. Furthermore, a heuristic sequencing method is developed to determine nonuniform headways and dwell times in the second stage. The suggested model framework and solution algorithm are tested using a real-world URT network, and the results show that energy consumption can be considerably reduced given certain travel time increments