Abstract: Disclosed is method and system for continuously re-generating reactive on-line train schedules for trains running in a large railway network. Railway network partitioned based on user configuration, into first type comprising trunk line and feeder line sub-networks, and second type comprising supervisory dispatch control territories. Sense and respond cycle is continuously executed on multi-processor computing environment, senses dynamic data from field about train movements, and other changes from users. For each first type sub-network, degree of deviation is computed from incumbent plans and congestion in sub-networks. Using degree of deviation and congestion, trains are rerouted and suitable scheduling methods are chosen for each sub-network and executed in parallel and first level train schedules are sent to second level train schedulers working on second type sub-networks which in parallel identify and resolve conflicts among first level train schedules.

Abstract: Disclosed is method and system for continuously re-generating reactive on-line train schedules for trains running in a large railway network. Railway network partitioned based on user configuration, into first type comprising trunk line and feeder line sub-networks, and second type comprising supervisory dispatch control territories. Sense and respond cycle is continuously executed on multi-processor computing environment, senses dynamic data from field about train movements, and other changes from users. For each first type sub-network, degree of deviation is computed from incumbent plans and congestion in sub-networks. Using degree of deviation and congestion, trains are rerouted and suitable scheduling methods are chosen for each sub-network and executed in parallel and first level train schedules are sent to second level train schedulers working on second type sub-networks which in parallel identify and resolve conflicts among first level train schedules.

Abstract: The present disclosure relates to a system and a method for vehicle movement modeling in a network. The modeling may be characterized by vehicle related intelligence gathering, processing and dissemination thereof for an adaptive rescheduling of the vehicle movement in the railway network. Predefined data associated with the vehicle in the railway network is acquired and is further processed to resolve one or more conflicts associated with the vehicle movement. The processing may include allocating resources, developing plans for voyages, and continuously gathering deviation data. The vehicle movement modeling may also include generating detailed layouts of vehicle movements for particular time-periods over the railway network.

Abstract: The present disclosure relates to a system and a method for vehicle movement modeling in a network. The modeling may be characterized by vehicle related intelligence gathering, processing and dissemination thereof for an adaptive rescheduling of the vehicle movement in the railway network. Predefined data associated with the vehicle in the railway network is acquired and is further processed to resolve one or more conflicts associated with the vehicle movement. The processing may include allocating resources, developing plans for voyages, and continuously gathering deviation data. The vehicle movement modeling may also include generating detailed layouts of vehicle movements for particular time-periods over the railway network.