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Train driver’s cabin (23 January 2012 by Karl Baron)


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Towards Autonomous Driving Trains in Mixed Traffic Networks: Setups and Optimization Methods for Energy Efficient Driving and Integration into Traffic Management Systems

While driving the train, the driver’s primary goals are to ensure safety, maintain the schedule of the service, and if the above are covered, improve energy efficiency of service delivery and passenger comfort, while respecting standard operating procedures set by the railway undertakings.
Operating the train in this way requires the driver to continuously monitor the progress of the train against a series of passing marks and scheduled stops. Without driving advisory/support, the drivers have no guide about progress within the schedule, and only route knowledge and experience allow the driver to judge if the service is running early or late between two timing points. Any temporary speed restrictions/unexpected signals within a route, while reflected in the timetable, make the driver’s judgements about progress and recovery time more difficult.

Driver Advisory Systems (DASs) are becoming established as essential operating tools to achieve improvements in train running performance. A DAS, in its simplest form, enables the driver to monitor the scheduled path of a train to ascertain whether the train will reach its next timing point on schedule energy-efficiently and safely, and to give an advisory speed for this to be achieved. So far there are some DASs on the market. However, most of the existing DASs cannot handle complex traffic on mainline railways: the traffic is typically mixed, with freight trains, intercity, and regional trains sharing the same infrastructure with different stopping patterns, and the network may contain different signaling systems.

This project aims at improving DAS functionalities towards its use in mixed traffic networks. To reach this aim, the following key objectives will be pursued:

  • to take into account the drivers needs of avoiding extra workloads and improving drivers’ acceptance of DASs;
  • to develop a method for calculating the train trajectory in an uncertain environment in which the values of system parameters are difficult to determine;
  • to specify interoperable solutions for DASs over different signaling systems; and
  • to explore functions of cooperating with the traffic management system (TMS) and improving the train movement performances in case of potential conflicts.
Lead
Prof. Dr. Francesco Corman, Dr. Pengling Wang
Financing
NWO (Netherlands Organisation for Scientific Research), Rubicon
Project timeframe
June 2018 – May 2020