ATO in trains results in punctual, energy saving and comfortable rides
Whereas the focus of Automated Train Operation (ATO) is mostly on metro, Xiaolu Rao discusses implementation of ATO methods in mainline railways, a far more challenging innovation, she believes. Rao will present her model at the Intelligent Rail Summit in Vienna this November, but gave Railtech some interesting insights ahead.
Rao proposes combining two traffic optimisation methods into an integrated model for mainline railways. The first is to improve the efficiency of traffic management by providing conflict resolutions, while the second is to improve train driving behavior by providing driver assistance or introducing train automation. “These two focuses will merge in the very near future, because there will be more and more demands for such collaboration”, believes Rao.
“Trains can avoid potential traffic conflicts by reacting to the proposals from traffic management, while traffic management can improve its calculation according to the real-time feedback from train automation. A decision-making procedure is introduced to select the most attractive output according to different optimisation objectives, such as punctuality, energy saving and riding comfort,” she suggests.
Mainline railways are much more complicated than metro railways, where ATO is widely implemented, argues Rao. “Metro railways have a simple timetable design and infrastructure topology. In many cases each metro line is independent of other lines and the trains are homogeneous sets of vehicles. In contrast, mainline railways vary in infrastructure topology, signaling system, locomotive types, timetable and many other aspects.”
Moreover, there are safety concerns when it comes to ATO in mainline railways, she continues. “The system must detect obstacles on the track and consider passenger’s safety while exiting and entering trains. Most mainline railways are open lines and stations are not equipped with the platform screen doors you often see at metro stations, and enable an ATO train to achieve a precise train stopping. Therefore, additional solutions are required before ATO methods can safely be implemented at mainline railways.
Dr. Xiaolu Rao is a senior system engineer and railway researcher working at Systransis Ltd in Switzerland. She completed her doctoral study at ETH Zurich in June 2015, but spent most of her research time at the company Systransis Ltd (owned by Siemens Switzerland Ltd). Her PhD thesis “Holistic rail network operation by integration of train automation and traffic management” has won the ETH Medal for outstanding PhD thesis.
The Intelligent Rail Summit 2017 will take place on 28, 29 and 30 November in the Infocenter of Wiener Linien. Please visit the conference website for more information: https://www.railtech.com/intelligent-rail-summit-2017/
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