A control system including one or more processors that may determine vehicle locations of one or more vehicles and states of charge of vehicle energy storage devices onboard the one or more vehicles. The control system may include an energy chassis having a fuel source holding a supply of fuel, an energy converter to convert at least a portion of the supply of the fuel from the fuel source into electric energy, and a communication device to communicate with the processors. The processors may direct which of the one or more vehicles are to couple with and be powered by the electric energy of the energy chassis based on one or more of: the vehicle locations, the states of charge of the vehicle energy storage devices, an amount of the supply of the fuel of the energy chassis, and a chassis location of the energy chassis.

A control system including one or more processors that may determine vehicle locations of one or more vehicles and states of charge of vehicle energy storage devices onboard the one or more vehicles. The control system may include an energy chassis having a fuel source holding a supply of fuel, an energy converter to convert at least a portion of the supply of the fuel from the fuel source into electric energy, and a communication device to communicate with the processors. The processors may direct which of the one or more vehicles are to couple with and be powered by the electric energy of the energy chassis based on one or more of: the vehicle locations, the states of charge of the vehicle energy storage devices, an amount of the supply of the fuel of the energy chassis, and a chassis location of the energy chassis.

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 train driver assistance method includes: acquiring basic data of a train under a complex and severe condition; determining whether a traction power system is normal according to the basic data; if so, acquiring an energy-efficient optimized speed profile of the train in a normal state according to the basic data of the train in the current state; and if not, acquiring an energy-efficient optimized speed profile of the train in an abnormal state according to the basic data of the train in the current state. In this way, the method can acquire the energy-efficient optimized speed profile of the train in its current state. The comprehensive electric train driver assistance method can enable the train to adapt to the complex and severe environment and realize the energy-efficient operation of the train and self-rescue of the train.

This disclosure relates to a system and method for recommending intelligent railway network access plans and modified access plans based on current status of execution of tasks and train timetables. Herein, the system and method need to consider multiple variables including train timetable changes, activity efficiencies etc. to derive an overall optimum access management solution for the railway network. They can interface with existing operational timetables and cost management systems. It is to be noted that the disclosure herein uses existing data sets for operations and cost data. The cost data is manipulated to identify fixed and variable costs and variability of variable costs with access duration and activity bundling. A trade-off between cost and value is considered which results in a longer continuous time window being available for maintenance, less visits to same location to complete a maintenance task, less time spent in setup/unwind activities and higher labour utilization.

This disclosure relates to a system and method for recommending intelligent railway network access plans and modified access plans based on current status of execution of tasks and train timetables. Herein, the system and method need to consider multiple variables including train timetable changes, activity efficiencies etc. to derive an overall optimum access management solution for the railway network. They can interface with existing operational timetables and cost management systems. It is to be noted that the disclosure herein uses existing data sets for operations and cost data. The cost data is manipulated to identify fixed and variable costs and variability of variable costs with access duration and activity bundling. A trade-off between cost and value is considered which results in a longer continuous time window being available for maintenance, less visits to same location to complete a maintenance task, less time spent in setup/unwind activities and higher labour utilization.

The present disclosure provides an operation adjustment method for a metro train in a unidirectional jam, including: generating a train operation routing scheme in the jam according to a jam position; setting priorities for turnaround stations; determining an affected train set; predicting, according to the affected train set, arrival times of each affected train at the turnaround stations of the different priorities; determining, according to the time, planned train service to be executed by the affected train after turning around; and acquiring a planned train service canceled during the jam, and allocating, according to the planned train service canceled during the jam, a train resource for train addition or storage. The present disclosure avoid the conventional complicated operation of manually determining the affected train one by one, and reasonably adds the extra passenger train to arrive at the station on the line for passenger carrying.

A train control system includes independent virtual in-train forces modelling engines onboard each of a plurality of locomotives in a train. Each of the plurality of locomotives may also include an analytics engine and a calibration engine configured to assimilate, analyze, and calibrate real time information from other locomotives and from draft gears and couplers interconnecting the locomotives with determinations made by the independent virtual in-train forces modelling engine onboard the respective locomotive, with the plurality of locomotives of the train being configured to operate collectively and coordinate their own acceleration values based on a common goal of minimizing in-train forces without being dependent on a command from a lead locomotive or central command.

A train control system includes independent virtual in-train forces modelling engines onboard each of a plurality of locomotives in a train. Each of the plurality of locomotives may also include an analytics engine and a calibration engine configured to assimilate, analyze, and calibrate real time information from other locomotives and from draft gears and couplers interconnecting the locomotives with determinations made by the independent virtual in-train forces modelling engine onboard the respective locomotive, with the plurality of locomotives of the train being configured to operate collectively and coordinate their own acceleration values based on a common goal of minimizing in-train forces without being dependent on a command from a lead locomotive or central command.

A method uses a track-side obstacle identification arrangement and a vehicle-side obstacle identification arrangement in order to identify obstacles in a danger zone in front of a rail vehicle. In order to allow improved autonomous driving of the rail vehicle, it is provided that, as it approaches a relevant one of the obstacles, at least one reaction or response of the rail vehicle is derived depending on an evaluation signal of the track-side obstacle identification arrangement and an actual value of at least one variable which characterizes the effectiveness or performance of the vehicle-side obstacle identification arrangement. A system for identifying obstacles is also provided.