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Detecting the undetectable of human-machine interaction to improve rail resilience

Abstract : Resilience engineering is an important issue for railway domain. Usually, rail resilience concept is limited to the capacity of the system to confront extreme weather or environmental conditions such as earthquakes or floods. The contribution of the rail staff or passengers is rarely taken into account to assess the capacity of the rail system to recover unprecedented situations, i.e. to make the rail system resilient. This paper aims at developing an original approach to detect possible negative or positive events related to interactions between human and machine in railway domain. It will present three case studies in order to illustrate such an interest. The first study involves and a tram driver and a car driver equipped with a Collision Avoidance System. The second case is an empirical example involving a train speed controller (i.e., the KVB) and a train driver confronted to several environmental conditions (e.g., presence of dead leaves on the tracks, rail track with important downhill, etc.). The last case relates to an experimental protocol for which the tram drivers are invited to respect a recommended speed given by an eco-driving tool in order to support the energy consumption. Each case is analyzed in order to discover operational situations with impact on several criteria such as safety, consumption, or traffic flow.
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Submitted on : Tuesday, November 2, 2021 - 11:43:13 AM
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  • HAL Id : hal-03411436, version 1



Salvatore La Delfa, Simon Enjalbert, Philippe Polet, Frédéric Vanderhaegen. Detecting the undetectable of human-machine interaction to improve rail resilience. The Sixth International Human Factors Rail Conference, Nov 2017, London, United Kingdom. ⟨hal-03411436⟩



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