Abstract: This disclosure relates generally to a method and system for dynamically predicting vehicle arrival time using a temporal difference learning technique. Due to varying uncertainties predicting vehicle arrival time and travel time are crucial elements to make the public transport travel more attractive and reliable with increased traffic volumes. The method includes receiving a plurality of inputs in real time and then extracting a plurality of temporal events from a closest candidate trip pattern using a historical database. Further, a trained temporal difference predictor model (TTDPM) is utilized for dynamically predicting the arrival time from the current location of the vehicle to the target destination based on the plurality of nonlinear features. The non-linear features and linear approximator formulation of TTDPM provides fast gradient computation improves training time.

Abstract: This disclosure relates to method and system for optimal policy learning and recommendation for distribution task using deep RL model, in applications where when the action space has a probability simplex structure. The method includes training a RL agent by defining a policy network for learning the optimal policy using a policy gradient (PG) method, where the policy network comprising an artificial neural network (ANN) with a set of outputs. A continuous action space having a continuous probability simplex structure is defined. The learning of the optimal policy is updated based on one of stochastic and deterministic PG. For stochastic PG, a Dirichlet distribution based stochastic policy parameterized by output of the ANN with an activation function at an output layer of the ANN is selected. For deterministic PG, a soft-max function is selected as activation function at the output layer of the ANN to maintain the probability simplex structure.

Abstract: This disclosure relates generally to a method and system for dynamically predicting vehicle arrival time using a temporal difference learning technique. Due to varying uncertainties predicting vehicle arrival time and travel time are crucial elements to make the public transport travel more attractive and reliable with increased traffic volumes. The method includes receiving a plurality of inputs in real time and then extracting a plurality of temporal events from a closest candidate trip pattern using a historical database. Further, a trained temporal difference predictor model (TTDPM) is utilized for dynamically predicting the arrival time from the current location of the vehicle to the target destination based on the plurality of nonlinear features. The non-linear features and linear approximator formulation of TTDPM provides fast gradient computation improves training time.

Abstract: Embodiments of the present disclosure relate to systems, methods, and user interfaces for monitoring and maintaining redundant network and storage paths. Initially, path check information is received at a path check server via one or more management nodes. Each of the one or more management nodes comprises one or more physical nodes corresponding to network and hardware infrastructure. Failed nodes of the one or more physical nodes are identified, the failed nodes indicating physical nodes having path failures. Upon determining the node does not have an active incident in progress, an incident corresponding to the node is generated. In embodiments, a notification may be communicated to one or more team members. The notification may include the incident and a status of the incident. In embodiments, data visualization corresponding to the incident may be provided.

Abstract: Embodiments of the present disclosure relate to systems, methods, and user interfaces for monitoring and maintaining redundant network and storage paths. Initially, path check information is received at a path check server via one or more management nodes. Each of the one or more management nodes comprises one or more physical nodes corresponding to network and hardware infrastructure. Failed nodes of the one or more physical nodes are identified, the failed nodes indicating physical nodes having path failures. Upon determining the node does not have an active incident in progress, an incident corresponding to the node is generated. In embodiments, a notification may be communicated to one or more team members. The notification may include the incident and a status of the incident. In embodiments, data visualization corresponding to the incident may be provided.