The present disclosure provides a method and apparatus for determining coupling and decoupling positions between trains. In at least one embodiment, the present disclosure provides a method performed by an apparatus for determining coupling and decoupling positions between trains, the method comprising collecting performance data, simulation data, and real-time data, calculating a first parameter and a second parameter, and determining the coupling and decoupling positions between the trains.

The present disclosure provides a method and apparatus for determining coupling and decoupling positions between trains. In at least one embodiment, the present disclosure provides a method performed by an apparatus for determining coupling and decoupling positions between trains, the method comprising collecting performance data, simulation data, and real-time data, calculating a first parameter and a second parameter, and determining the coupling and decoupling positions between the trains.

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.

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.

The present invention relates to a multi-layer coupling relationship-based train operation deviation propagation condition recognition method, where the method includes the following steps: (1) recognizing an effective train event time sequence, including an arrival event and a departure event of a train at each passing station; (2) uniformly extracting train activity data, including a stop activity, a section operation activity, a turn-back activity, and an arrival or departure interval activity; (3) constructing coupling relationship groups between a train event and a train activity and between train activities; and (4) performing statistics on changes of train operation deviation in each relationship group, and outputting a respective distribution function and a time-space distribution visualized result. Compared with the prior art, the present invention has the advantages of being practical, automatic recognition, feedback optimization, and the like.

The present invention relates to a multi-layer coupling relationship-based train operation deviation propagation condition recognition method, where the method includes the following steps: (1) recognizing an effective train event time sequence, including an arrival event and a departure event of a train at each passing station; (2) uniformly extracting train activity data, including a stop activity, a section operation activity, a turn-back activity, and an arrival or departure interval activity; (3) constructing coupling relationship groups between a train event and a train activity and between train activities; and (4) performing statistics on changes of train operation deviation in each relationship group, and outputting a respective distribution function and a time-space distribution visualized result. Compared with the prior art, the present invention has the advantages of being practical, automatic recognition, feedback optimization, and the like.

The method comprises, for a given route between a starting point and a destination, and for an arrival at the destination at a given desired time: a preliminary step of determining and recording a plurality of vehicle mission profiles, at least a first instant during the journey, a step of determining an instantaneous position of the vehicle at this first instant and a desired time remaining to reach the destination, a step of identifying, among the mission profiles, the mission profile(s) whose curves are closest to the desired time remaining at the instantaneous position of the vehicle, and a step of determining new driving parameters to follow a new mission profile, the new mission profile being determined on the basis of the determined mission profiles.