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High-speed lines control command and signaling
Published in Andrzej Żurkowski, High-Speed Rail in Poland, 2018
Automatic train operation (ATO) systems automatically operate devices controlling train movements, in accordance with all data received from track-side control command and signaling, replacing drivers in driving the trains. ATO class systems provide a procedure which ensures required changes of the train speed without any driver action, called automatic train driving. ATO class systems frequently complement ATC class systems, which filter commands given by ATO, providing train movement safety. ATO class systems require some additional functionality track-side, which is ensured by automatic train supervision (ATS) systems for monitoring the movements of a number of trains at the same time. Together, ATO class systems and ATS class systems are called automatic people movers (APM). ATO class systems are currently used only for rail transport operated using vehicles with similar or even identical dynamic parameters, where the maximum speed does not exceed 100 km/h. Thereby, at present, ATO class systems are not used for high-speed trains. Such systems would enable driverless operation, but it is not currently acceptable for the railways or for the passengers. However, intensive works aimed at including the ATO functionalities in the unified European control command system are ongoing. It has to be expected that ATO functionality will be used on long sections between main stations, but not while entering stations, driving through the stations, and leaving the stations. The ATO systems will therefore support the drivers in driving trains on sections with the highest maximum speeds, where the time left for driver reaction is the shortest, and will not take over the work of the train drivers. The ATS class systems are also currently used only for supervising groups of vehicles with similar or even identical dynamic parameters, running on separated infrastructure with a maximum speed not exceeding 100 km/h. The ATS functionalities in the case of high-speed rails can be implemented partly by enhancing the functionality of the interlockings on the level of local control centers and of the operational management systems on the level of regional operational centers.
Research on Enhanced Situation Awareness Model with DMI Visualization Cues for High-Speed Train Driving
Published in International Journal of Human–Computer Interaction, 2023
Aobo Wang, Beiyuan Guo, Ziwang Yi, Weining Fang
During train operation the driver’s field of view is gradually narrowed by the high speedy moving surroundings (Guo et al., 2015). The frequency of driver’s operation behavior also gradually decreased by the automation system. As a result, high-speed train drivers can get fatigued or lose their vigilance gradually in the process of driving in monotonous high speed, so that the drivers lose their attention and comprehension of the train operation status, which means the SA of driving is gradually lost. Although vigilance devices (Dead man switches) have been used to mitigate the effects of driver fatigue on driving (Cabon et al., 1993), they can only passively prevent a decline in vigilance (Dunn & Williamson, 2012). The driver is considered the last line of safety for train driving, and in Automatic Train Operation (ATO) mode the driver needs to take over the driving system in the case of driving system failure or unexpected driving event. SA decline or loss is likely to lead to complacency or distraction for drivers and trigger a series of human out-of-the-loop (OOTL) performance problems (Wickens, 2008). For example, a train will drive through a red pass signal without authorization, which is also known as signal passed at danger (SPAD). SPAD is considered to be the result of systematic errors generated by the driver and the train together (Harrison et al., 2022).
Contributions of vehicle dynamics to the energy efficient operation of road and rail vehicles
Published in Vehicle System Dynamics, 2021
Jenny Jerrelind, Paul Allen, Patrick Gruber, Mats Berg, Lars Drugge
Beyond DAS systems, removing the driver completely from the train control system and employing automatic train operation (ATO) potentially allows for the greatest energy saving potential from dynamic train control. Due to the many complexities of mainline rail operations, full ATO implementation tends to be limited to closed rail networks with a limited traffic mix, typically metro systems, and often the overarching control methods tend to be rather simplistic with little focus on trajectory optimisation. With evermore focus on energy efficiency, research work is now shifting towards optimised trajectory ATO with recent studies employing machine learning techniques, such as those described by Li et al. [81] above.
Systems integration theory and fundamentals
Published in Safety and Reliability, 2020
Mohammad Rajabalinejad, Leo van Dongen, Merishna Ramtahalsing
With continuous increase in the need for transportation, more and more passengers and cargo have to be carried by rail. In recent years, railway faced tremendous growth but with limited increase in capacity, making railway network more and more saturated (Lagay & Adell, 2018; Rao, Montigel, & Weidmann, 2013a). Consequently, the railway industry is facing a range of challenges to improve the existing system aiming for a high-quality system which increases capacity and efficiency of the railway network, more eco-friendly systems with energy cost reduction, and higher customer satisfaction. Two major methods to tackle these challenges are real-time rescheduling and automatic train operation (ATO). Real-time rescheduling increases efficiency of infrastructure management by dealing with deviations, breakdowns and incidents, while automatic train operation is an on-board approach available to minimise the loss of efficiency caused by manual operation. ATO is regarded as a promising solution to meet abovementioned challenges (Lagay & Adell, 2018). ATO is an on-board concept for all phases of the train operation, from acceleration to precise stopping, which implements train-level optimisation to help train operators realise automation and exact operation (Lagay & Adell, 2018). With the rapid development of communication, control and computer technologies in the last several decades, the driver achieves more and more supports. ATO is assumed to aid in increasing capacity of the track, minimising disruptions, increasing punctuality, increasing efficiency in deployment of train drivers and aid in more effective energy consumption. As technology advances in railway systems, one theoretically challenging and practically significant problem is how to integrate the ATO system, to make the current railway network more efficient with higher capacity, lower cost and improved quality of service by optimised railway traffic management and train operation (Rao, Montigel, & Weidmann, 2013b). According to Schutte (2001) and Yin et al. (2017) there are different Grades of Automation (GoA) of trains indicated in Table 4 which could aid in achieving distinct goals.