Disclosed is method including receiving digital vehicle data for a fleet of vehicles like trucks, trains, planes, drones, etc., the digital vehicle data being one or more of GPS/location-based data, image data or radar data and combining one or more of pieces of data. The method includes inferring, based on the first combined data, a loaded/empty status of a vehicle. The method includes combining other data to yield second combined data, receiving data regarding one or more of supply, demand, and amount of available cargo to yield third combined data, generating information relating to a supply of vehicles available to load at a specified dock and/or deliver a cargo to a specified dock, in each case within a specified period of time and generating suggestions for one or more vehicles regarding future routes based on the data.

Systems and methods are provided for decentralized rail signaling and positive train control. A decentralized train control system may include a plurality of wayside units, configured for placement on or near tracks in a railway network, and one or more train-mounted units, each configured for use in a train operating in a railway network that support use of the decentralized train control system. Each train-mounted unit may configured to receive communicate with any wayside unit and/or train-mounted unit that comes within range, with the communicating including use of ultra-wideband (UWB) signals, and for generating control information based on the UWB signals, for use in controlling one or more functions associated with operation of the train.

An automatic train operation device includes: a step command start position determining unit to determine whether a train passes through a step command start position that is a position a certain distance before a target stop position of the train; a deceleration command generating unit to generate a deceleration command to control braking force of a braking device in a section from the step command start position that the train passes through to the target stop position at which the train stops; and a travel history storage unit to store travel state information and the deceleration command for the section as a plurality of travel histories. When the step command start position is determined by the step command start position determining unit, the deceleration command generating unit generates the deceleration command by using the travel histories.

The present disclosure generally relates to systems and methods of increasing functional safety of locomotives. In exemplary embodiments, a locomotive functional safety system is configured to: receive one or more manual control commands from a locomotive control stand and/or a user interface onboard a locomotive; determine whether the one or more manual control commands pass or satisfy one or more predetermined criteria; if the one or more manual control commands pass or satisfy the one or more predetermined criteria, approve the one or more manual control commands and allow the one or more manual control commands to be relayed onward and/or acted upon; and if the one or more manual control commands do not pass or satisfy the one or more predetermined criteria, disapprove the one or more manual control commands and disallow the one or more manual control commands to be relayed onward and/or acted upon.

A system includes one or more processors and memory storing processor-executable instructions that cause the one or more processors to perform operations. The operations include generating a driving strategy for a traveling route of a train comprising at least one battery-electric locomotive (BEL) based on saved data in the system for optimizing one or more aspects for the train over the traveling route; operating the train according to the driving strategy; receiving update data associated with a current location of the train; revising the driving strategy based on the saved data and the update data for optimizing energy consumption efficiency for a segment of the traveling route by identifying active powered components of the train that are non-essential for traversing the segment and turning off power to one or more of the identified active powered components for a duration of traversing the segment.

Systems and methods are provided for vehicle host interface module (vHIM) based braking solutions and use thereof in trains.

The present invention discloses an iterative learning control (ILC) method for a multi-particle vehicle platoon driving system, and relates to the field of ILC. The method includes: firstly, discretizing a multi-particle train dynamic equation using a finite difference method to obtain a partial recurrence equation, and then transforming the partial recurrence equation into a spatially interconnected system model; secondly, transforming the spatially interconnected system model into an equivalent one-dimensional dynamic model using a lifting technology, and in order to compensate input delay, designing an ILC law based on a state observer; and thirdly, transforming a controlled object into an equivalent discrete repetitive process according to the ILC law, and converting a controller combination problem into a linear matrix inequality based on stability analysis of the repetitive process. The method is simple and easy to implement, considers structure uncertainty of the system, and has a good control performance and robustness.

A machine learning system (100) for train route optimization includes a machine learning module (120) in communication with an optimization module (110) and including instructions stored in a memory that when executed by a processor (82) cause the machine learning module (120) to receive a plurality of schedules for railroad vehicles travelling through a track network transmitted by the optimization module (110), prioritize the plurality of schedules by applying at least one business rule (140) to the plurality of schedules based on dynamically learned behavior, and provide a prioritized list of schedules.

A vehicle system having processors configured to determine permissible regions of a trip where the vehicle system is permitted for automatic control. The permissible regions of the trip are determined based on one or more of parameters of a route, a trend of operating parameters of the vehicle system, or a trip plan that designates one or more operational settings of the vehicle system at different locations, different times, or different distances along a route. The processors also are configured to control transition of the vehicle system between manual control and the automatic control in the permissible regions by alerting an operator of the vehicle system, automatically switching between the manual control and the automatic control, or modifying conditions on which the transition occur.

A system (e.g., a control system) includes a sensor configured to monitor an operating condition of a vehicle system during movement of the vehicle system along a route. The system also includes a controller configured to designate one or more operational settings for the vehicle system as a function of time and/or distance along the route.