A system for monitoring a wheel-rail contact force, the system providing a measuring unit for measuring a vertical acceleration of a wheel being connected to a bogie. The wheel is configured to run on a rail. The system includes a calculation unit for simulating a wheel-rail interaction using the measured vertical acceleration and for calculating a wheel-rail contact force based on the simulated wheel-rail interaction. Also, a method for the same.

According to one embodiment, a train operation planning support system displays a timetable of a train in which a first required time and a first buffer time are placed for running time of the train and a second required time and a second buffer time are placed for dwell time of the train. The first required time is time required for a train running between a departure station and an arrival station. The first buffer time is buffer time obtained by subtracting the first required time from running time of the train set as a target value. The second required time is time required for the train stopping at the arrival station. The second buffer time is buffer time obtained by subtracting the second required time from dwell time of the train which is set as a target value.

According to one embodiment, an information processing apparatus includes a processor. The processor is configured to detect a state of a device by applying device information on the device to a state detection model generated based on past device information on the device and characteristic information indicating characteristics of a moving body equipped with the device.

A system for simulating a railroad track. The system comprises one or more modular track simulation units that are smaller than conventional track simulators and are easy to use with and be connected to a device being tested (e.g., grade crossing predictor). Each track simulation unit may have one of a plurality of impedances associated with a corresponding railroad track length. The units are combinable such that the system can simulate multiple, different track lengths. Each unit has a plurality of test points that can be connected to the device under test and/or used to alter conditions of the simulated track.

A train control system uses artificial intelligence for maintaining synchronization between centralized and distributed train control models. A machine learning engine receives training data from a data acquisition hub, a first set of output control commands from a centralized virtual system modeling engine, and a second set of output control commands from a distributed virtual system modeling engine. The machine learning engine compares the first set of output control commands and the second set of output control commands, and trains a learning system using the training data to enable the machine learning engine to safely mitigate any difference between the first and second sets of output control commands using a learning function including at least one learning parameter.

A system may include a data acquisition hub connected to databases and sensors associated with locomotives, systems, or components of a train and configured to acquire real-time and historical configuration, structural, and operational data in association with inputs derived from real time and historical contextual data relating to a plurality of trains. The system may include a virtual system modeling engine configured to receive results of a non-destructive evaluation of a train component, simulate in-train forces, determine a predicted time of failure for the train component based on an evaluation of stresses that have already been applied to the component and expected future stresses, and implement repair, replacement, or operational protocols for the train component before or at a repair facility that will be reached by the train ahead of a predetermined minimum threshold time period before the predicted time of failure.

A train control system controls the ramp rate at which a train accelerates after braking. A machine learning engine receives training data from a data acquisition hub, including a plurality of input conditions of the train and a plurality of outputs associated with the input conditions. A virtual system modeling engine simulates in-train forces and train operational characteristics using physics-based equations, kinematic or dynamic modeling of behavior of the train or components of the train during operation of the train when the train is accelerating after braking, and inputs derived from stored historical contextual data related to the train. The machine learning engine trains a learning system using the training data to generate an output based on an input using a learning function including at least one learning parameter. The learning parameter is modified as needed to improve the accuracy of the learning function in generating the output.

A train control system may include a data acquisition hub connected to a database and sensors associated with one or more locomotives, systems, or components of a train and configured to acquire data in association with inputs derived from contextual data relating to a plurality of trains being operated by experienced train engineers under a variety of different conditions and in different geographical areas for use as training data. A machine learning engine of the train control system may receive the training data from the data acquisition hub, encode a modified learning function as a statistical model of desirable train handling behavior, evaluate train handling behavior by comparing to the statistical model, and update a certification of one or more of a train engineer, a semi-autonomous control system, or a fully autonomous control system.

A method of communication-based train control system migration includes scanning a guideway to generate surveying data and processing surveying data to calculate a 3-D representation of the guideway. Appropriate locations are determined for the communication-based control devices on the guideway. Communication-based train control devices are installed in a guideway at the determined appropriate locations and vehicles are retrofitted with an autonomy platform. Testing of the control devices and retrofit vehicles is performed. A communication-based train control system is used to control the retrofit vehicles when they operate within the guideway.

A method (600) for generating schedules for railroad vehicles travelling within a railroad network using a genetic algorithm (GA) (100), through operation of at least one processor (82), includes providing an initial population (200) of initial schedules (220), each schedule (220) having first information of a railroad track network and second information of railroad vehicles travelling in the railroad track network for a specific time instance, selecting multiple schedules of the initial schedules (220), and generating a final population (480) of final schedules (420) utilizing crossover operation (400) between selected initial schedules (220), wherein the second information of the railroad vehicles travelling in the railroad track network are exchanged between the selected initial schedules (220).