20.06.17
Research to transform future of traffic management systems released
Two crucial pieces of research that outline how technology will transform the next generation of traffic management systems (TMS) have today been released by the RSSB.
The projects, which are part of the Future Traffic Regulation and Optimisation (FuTRO) programme outline the optimisation tools, algorithms and decision support systems that will be important to increasing capacity on the network and improving railway operational performance.
One of the projects being developed is a collaboration between University College London and the University of Birmingham entitled, ‘Developing and Evaluating Dynamic Optimisation for Train Control Systems’.
Researchers looked at using dynamic algorithms to control train paths and trajectories to ensure that trains arrive at junctions and other fixed infrastructure points at the right time and speed.
“Through reduced stopping and starting, train flow, energy and timetables can be better managed leading to increased capacity, reduced energy usage and fewer disturbances to network running,” a spokesperson for the RSSB said.
“The results have been validated through simulation exercises and elements of the research are already being trialled, with further development required before being deployed on the railway.”
The other project, SafeCap+, which is being led by the University of Newcastle, has built a set of tools and modelling techniques to evaluate energy consumption at railway junctions and provide traffic management advice to ensure wholesystem safety.
“The SafeCap simulator alerts signalling engineers to any unsafe combination of line speed, deceleration and signal spacing while the energy module provides an estimate of energy usage of proposed service patterns including implications of alternative traffic regulation decisions for resolving traffic flow conflicts,” the RSSB stated.
“SafeCap also proposes a new system framework for developing a signalling and dispatching advisory system based on artificial intelligence, which prompts the operator to pursue a recommended course of action, based on the current state of the rail network, helping regulate traffic management in the event of disturbances.”
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