DITTO - Development of Integrated Tools to Optimise Rail Systems

Funded under the Future Traffic Regulation Optimisation (FuTRO) programme, this multi-disciplinary research project brings together University-based traffic engineers and transport operations researchers (from Leeds and Southampton) and computer scientists (from Swansea) to develop fundamental principles and to establish proofs of concept for the optimisation of rail operations.

Aims and objectives 

The objective of DITTO is to develop the formulations, algorithms and processes that will deliver a step change in rail system performance and meet future customer needs. This will be done by taking into account developments in human and automatic control on trains and in control centres.

We propose enhancements to existing practice through fundamental research in four Work Packages (WPs):

• WP1 incorporates safety into system optimisation and will develop tools to assess future changes in traffic regulations proposed in later WPs.
• WP2 is concerned with reliability, and investigates its relationships with capacity utilisation and develops optimisation techniques to determine junction capacity under perturbations.
• WP3 focuses on dynamic decision-making and network-wide traffic management under FuTRO,   and develops simulation and optimisation tools, combining with multi-commodity optimisation approaches, to design and test traffic management and control mechanisms for the FuTRO systems.
• WP4 integrates our work at network level and applies Artificial Intelligence tools for automated real-time rescheduling of services to maximise capacity.

The key outcome of our work will be the development of an integrated set of tools that can optimise rail system performance, which we will illustrate using a section of the East Coast Main Line as a demonstrator. 

At Leeds we are leading the development of dynamic simulation and optimisation tools for the real-time operations of FuTRO systems. The modelling will include transferring some well-tested and researched ideas from “car following” models for road traffic. Our modelling will allow trains to be run close together, forming platoons with undisturbed headways and uniform speeds, thus improving safety and capacity. 

We will present numerical analyses which show the sensitivity of the model parameter values on the performance of the control mechanism in terms of safety and stability of the resulting traffic flows. Simulation models will be used to test theoretical control algorithms and evaluate their performances. 

Budget (ITS portion): £425,000

Publications and outputs

Project deliverables 

• Deliverable 1.3 DITTO Tool Integration: Towards Tool Interaction and Safety Assessment. June 2016
• Deliverable 3.1 Dynamic Simulation for Real-Time Operations of ERTMS Level 3. September 2015
• Deliverable 3.2 Simulation and Control of ERTMS Level 2. September 2016
• Deliverable 4.1 Interim Report and Prototype. April 2016
• Deliverable 4.2 Good Practice Guide. June 2017

Journal papers

• Liu, R, Whiteing, AE and Koh A (2013) Challenging established rules for train control through a fault tolerance approach: applications at a classic railway junction. Journal of Rail and Rapid Transit, Vol 227(6), pp685-692. 
• Chen, J., Liu, R., Ngoduy, D. and Shi, Z. (2016) A new multi-anticipative car-following model with consideration of the desired following distance. Nonlinear Dynamics, 85(4), 2705-2717. 
• Guo, X., Wu, J., Sun, H., Liu, R. and Gao, Z. (2016) Timetable coordination of first trains in urban subway network: A case study of Beijing. Applied Mathematical Modelling. Vol 40, 8048-8066. 
• Ye, H., and Liu, R. (2016) A multiphase optimal control method for multi-train control and scheduling on railway lines. Transportation Research, Part B, 93(A), 377-393. 
• Li, F., Gao, Z., Wang, David Z.W., Liu, R., Tang, T., Wu, J., Yang, L.  (2017) A subjective capacity for single-track railway system with ?-balanced traffic and ?-tolerance level. Transportation Research, Part B, 105, 43-66. 
• Ye, H. and Liu, R (2017) Nonlinear programming methods based on closed-form expressions for optimal train control. Transportation Research, Part C, 82, 102-123. 

Conference presentations

• Liu, R. (2016) Simulation model of speed control mechanisms for the moving-block systems under ERTMS Level 3. 2016 IEEE International Conference on Intelligent Rail Transportation, Birmingham, 22 – 25 August 2016. 
• Wang, Y., Liu, R. (2016) Scheduling and control algorithms for the enhancement of the robustness of a single-track railway system. World Conference on Transportation Research (WCTR), Shanghai, 10-15 July 2016. 
• Liu, R. and Ye, H. (2017) Nonlinear programming methods based on closed-form expressions for optimal train control. 7th International Conference on Railway Operations Modelling and Analysis (RailLille), Lille, 4-7 April 2017. 
• Wang, Y. Liu, R and Kwan R (2018). Simultaneous rerouting and rescheduling on rail networks under weather impact. 97th Transportation Research Board Annual Conference, 7 – 11 Jan 2018, Washington DC. 
• Wang, Y., Liu, R. and Kwan, R (2017) Train timetable rescheduling under adverse weather conditions. 7th International Conference on Railway Operations Modelling and Analysis (RailLille), Lille, 4-7 April 2017