More trains, fewer delays thanks to optimal traffic management

Science topics November 2018 TransportInnovationEnergy

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Click to see the video - Crédit Ifsttar

  • The congestion reduction project on the RER A line, in the Paris Region ( by Grégory Marlière)                                                                              (video in french language)

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Who has never experienced delays when travelling by train? Apart from extreme situations, such as strikes or major breakdowns, we can all remember an incident that has disrupted our daily lives. Perhaps the legendary punctuality of trains should be called into question when more and more of them are running on our infrastructure…

IFSTTAR scientists are studying the causes of these delays and proposing innovative solutions to increase the number of trains without reducing punctuality.

First, understand the cause of the delays…Passengers in a London station blocked by a train breakdown - Credit Epictura

Although the punctuality of our rail system is fairly satisfactory, it is deteriorating in some parts of the network. This is mainly due to the increase in the number of trains, which automatically leads to more delays. 

Breakdowns caused by increased stress on infrastructure and rolling stock (trainsets, locomotives and railcars) are the most significant source of delays. In other words, today many trains run on infrastructure that is at the limit of its capacity. Under these conditions, even a small delay can become more severe and cause numerous knock-on delays due to the "snowball effect".

Reducing the number of trains or building new railway lines are not feasible solutions. The costs of new infrastructure are prohibitive, and the demand for new rail services is too high.

Developing new tools to manage traffic better

One solution, which is being studied at IFSTTAR, is to develop traffic optimization algorithms. This computational technique is designed to manage rail traffic in real time in order to limit the impact of perturbations. It allows "optimal" use of the available capacity and therefore answers the question: can more trains be run without reducing punctuality?

These algorithms are based on mathematical and computing techniques from the fields of operational research and artificial intelligence. They allow a very large number of alternative solutions to be explored in response to a problem.

It is possible to look in more detail at how trains use the infrastructure. The aim is to gain a better understanding of how trains can share the infrastructure without interfering with each other or, failing this, doing so as little as possible. In this case, we use the term "microscopic" optimization model. 

These microscopic models can also be made available to staff several months in advance to facilitate timetabling.  And each day, they will help to determine, in real time, the routes and sequence of conflicting trains in order to limit delays due to bottlenecks on the network.

Foreseeing developments on the rail network 

More than fifteen years ago, IFSTTAR pioneered the use of microscopic models for rail traffic management. Since then, other teams have followed suit and this type of model is even beginning to be adopted in professional circles. 

Today, the quality of the results obtained, both in terms of reducing delays in highly disrupted situations and running more trains through the infrastructure, is well established.

However, there are still many challenges to be met in order to coordinate several microscopic models and thus extend geographical coverage. The forthcoming  autonomous trains also raises new questions about the management of mixed traffic consisting of driverless and manually operated trains.

 

 


To find out more...

D. Arenas, P. Pellegrini, S. Hanafi, and J. Rodriguez. Timetable rearrangement to cope with railway maintenance activities. Computers and Operations Research, 95:123–138, 2018.
P. Pellegrini, G. Marlière, and J. Rodriguez. RECIFE-SAT: a MILP-based algorithm for the railway saturation problem. Journal of Rail Transport Planning & Management, 7(1-2):19–32, 2017
P. Pellegrini, G. Marlière, and J. Rodriguez. Optimal train routing and scheduling for managing traffic perturbations in complex junctions. Transportation Research Part B: Methodological, 59C:58–80, 2014