System analysis of train operations and track occupancy at railway stations

A system analysis approach is presented for investigation of train operations in railway stations based on network, timetable and train detection data. The estimated blocking times, buffer times and track occupancies are compared with real operations data recorded automatically by track circuits. Statistical analysis of train operations between two Dutch major railway stations in The Hague clearly reveals that the trains operate at lower than design speed and the capacity of the critical routes to/from the platform tracks via level crossings is occupied up to 80 %. Furthermore, the dwell times at platform tracks are systematically extended due to hinder by other trains and behaviour of railway personnel. The scheduled headway between arrival and departure of some pairs of trains at critical route nodes proves to be insufficient, because the scheduled dwell times at stations and running times at junctions are generally exceeded and often leads to route conflicts if the headway times are short. The quality of timetable design and train operations in stations would be improved significantly if the feasibility of the scheduled arrival and departure times at major transfer stations was proven by a detailed estimation of the blocking and buffer times based on observed running times and delays during operations. The buffer time at junctions and level crossings should reflect the distribution of real train speeds and blockingtimes

Human–Machine Interface in Transport Systems: An Industrial Overview for More Extended Rail Applications

This paper provides an overview of Human Machine Interface (HMI) design and command systems in commercial or experimental operation across transport modes. It presents and comments on different HMIs from the perspective of vehicle automation equipment and simulators of different application domains. Considering the fields of cognition and automation, this investigation highlights human factors and the experiences of different industries according to industrial and literature reviews. Moreover, to better focus the objectives and extend the investigated industrial panorama, the analysis covers the most effective simulators in operation across various transport modes for the training of operators as well as research in the fields of safety and ergonomics. Special focus is given to new technologies that are potentially applicable in future train cabins, e.g., visual displays and haptic-shared controls. Finally, a synthesis of human factors and their limits regarding support for monitoring or driving assistance is propose

Evaluation of railway system performance under changing levels of automation using a simulation framework

Modern mainline railways are under constant pressure to meet the demands of higher capacity and to improve their punctuality. Railway system designers and operators are increasingly looking to use automation as tool to enable proactive strategies to optimise the timetable, improve the reliability of the infrastructure & rolling stock, to allow for a more dynamic command & control system which can respond to passenger demand and overall to linearize the response behaviour of the system under duress. In the first part of this thesis, I, the author, will discuss the development of automation over the years and the techniques that have been developed to analyse automation changes in a system. Further to this, I outline the various changes to the railway technology over the last century in brief. In the second part, I apply the techniques described earlier to design an automation framework to develop a grade of automation for the railway system to meet the demands of improved capacity and performance. Further to this, I develop parallel testable levels of automation using existing railway technology to demonstrate the effectiveness of a framework developed using the methodology discussed before. These levels are then tested on a network topology using micro-simulation to verify if they produce improved capacity and performance. In the final part, A case study is developed for the network from Kings Cross station to Welwyn Garden on the East Coast Main Line with the traffic dense branch line from Hertford north joining this line. The network is simulated under similar conditions to that adopted for the theoretical network and the results are compared with the previous outcomes. Results from the above studies have several significant outcomes. Firstly, the methodology developed over the course of this thesis can produce automation levels that are distinct from each other. Secondly, these simulation results show that there is a step change in the performance of the systems when organised into distinct levels of automation. Thirdly, and perhaps the most important conclusion from the studies, I show that automation of a single railway sub-system does not yield beneficial results unless there are complementary solutions produced for the surrounding sub-systems. In the theoretical phase of the study, the journey time calculations were repeated for 5000 iterations using a Quasi Monte Carlo framework. The results indicate a clear separation between each of the level and stages of automation proposed within the framework. The results from the simulation show that the reduction in journey times between the various levels can be as much as 5%. In the case study, the results were not as distinct but the overall trendlines indicate a reduction in journey times for both intercity and suburban services. Publications produced during the research period: • Venkateswaran, K., Nicholson, G., Chen, L. & Pelligrini, P. 2017. D3.3.2 Analysis of European best practices and levels of automation for traffic management under large disruptions In: IFFSTAR (ed.) Capacity for Rail. UIC. • Venkateswaran, K. G., Nicholson, G. L., Roberts, C. & Stone, R. Impact of Automation on the Capacity of a Mainline Railway: A Preliminary Hypothesis and Methodology. 2015 IEEE 18th International Conference on Intelligent Transportation Systems, pages 2097-2102

Simulation of stochastic elements in railway systems using self learning processes.

The railway traffic follows deterministic rules, whose selection and application depend on the choices of human operators. These choices may be different in similar situations and produce different effects on the circulation. The difficulty to code, in a general and comprehensive way, these behaviours suggested to test the use of systems capable to reproduce events without requiring a previous definition of the operating rules but acting by means of self-learning processes. The present research deals with: an analysis of the critical behavioural parameters, difficult to be effectively modelled by means of analytical simulation tools; the selection of the self-learning process for the application to the reliability of a railway network capable to work as a part of a wider simulation model of railway traffic; the development of a preliminary version of the model simulating the stochastic failure events and its application to a case study