An operation status reproducing device includes an onboard information obtaining unit that obtains onboard information corresponding to date and time from an onboard device mounted on a designated train being a train specified by a train number and the date and time externally designated, a specific event detecting unit that specifies time at which a specific event occurs by analyzing the onboard information, a ground information obtaining unit that obtains ground information in a certain period including the time at which the specific event occurs from a ground device, an external information obtaining unit that obtains external information including weather information in a certain period including the time at which the specific event occurs, and a reproduction image generating unit that generates a reproduction image illustrating a past operation status of the designated train based on the onboard information, the ground information, and the external information.

An operation status reproducing device includes an onboard information obtaining unit that obtains onboard information corresponding to date and time from an onboard device mounted on a designated train being a train specified by a train number and the date and time externally designated, a specific event detecting unit that specifies time at which a specific event occurs by analyzing the onboard information, a ground information obtaining unit that obtains ground information in a certain period including the time at which the specific event occurs from a ground device, an external information obtaining unit that obtains external information including weather information in a certain period including the time at which the specific event occurs, and a reproduction image generating unit that generates a reproduction image illustrating a past operation status of the designated train based on the onboard information, the ground information, and the external information.

A system and method for monitoring failure of a train within a tunnel is disclosed. The method comprises receiving, by the central server, an estimated crossing time for the train to cross the tunnel and the latest location of the train from a telemetry system onboard the train. The telemetry system comprises a Head-of-Train device communicatively coupled to an End-of-Train device. The method further comprises detecting a failure event associated with the train, by the central server, if an attempt to establish communication with the telemetry system is unsuccessful after the estimated crossing time. The method further comprises broadcasting, by the central server, an emergency alert message indicating the failure event to one or more receiving devices based on the latest location of the train. The receiving device is associated with at least one of an operator control station and an adjacent train.

The present invention relates to a method and system using multiple data sources for unsupervised and/or semi supervised algorithms to derive features such as speed of the train, length of the train, type of wagons, etc. Thus, classifying train categories. The invention provides a method and a system configured for analysing railway related vibration data. The invention is configured for collecting at least a first dataset from a sensor applied to the railway infrastructure. Further, it is configured for collecting at least a second dataset from a scheduling component. The at least one subset of the first dataset is curated with the second dataset to obtain first training database. The invention further discloses a method comprising the step of predicting at least a likelihood of one train belonging to at least one train-type.

The present invention relates to a method and system using multiple data sources for unsupervised and/or semi supervised algorithms to derive features such as speed of the train, length of the train, type of wagons, etc. Thus, classifying train categories. The invention provides a method and a system configured for analysing railway related vibration data. The invention is configured for collecting at least a first dataset from a sensor applied to the railway infrastructure. Further, it is configured for collecting at least a second dataset from a scheduling component. The at least one subset of the first dataset is curated with the second dataset to obtain first training database. The invention further discloses a method comprising the step of predicting at least a likelihood of one train belonging to at least one train-type.

A rail vehicle system, a rail vehicle, and a visual sensing device are provided. The rail vehicle system includes a system control device, a rail, and a rail vehicle. The rail vehicle includes a processing device and the visual sensing device. In a process where the rail vehicle travels along the rail, the visual sensing device captures images in front of the rail vehicle, and the visual sensing device emits a laser beam toward a front side of the rail vehicle. The visual sensing device receives the reflected laser beam to generate a laser sensing data. The processing device determines whether or not to change at least one of a travel direction and a travel speed of the rail vehicle according to the images captured by the visual sensing device and the laser sensing data.

A rail vehicle system, a rail vehicle, and a visual sensing device are provided. The rail vehicle system includes a system control device, a rail, and a rail vehicle. The rail vehicle includes a processing device and the visual sensing device. In a process where the rail vehicle travels along the rail, the visual sensing device captures images in front of the rail vehicle, and the visual sensing device emits a laser beam toward a front side of the rail vehicle. The visual sensing device receives the reflected laser beam to generate a laser sensing data. The processing device determines whether or not to change at least one of a travel direction and a travel speed of the rail vehicle according to the images captured by the visual sensing device and the laser sensing data.

A system includes one or more processors. The one or more processors are configured to receive crossing obstruction information from an optical sensor disposed proximate a crossing of a route traversed by a vehicle, with the crossing obstruction information indicating a presence of an obstruction to the crossing; obtain position information indicating a position of the vehicle traversing the route; determine proximity information of the vehicle indicating proximity of the vehicle to the crossing using the position information; determine a presence or absence of an alert state indicating a potential of the crossing being obstructed using the crossing obstruction information and the proximity information; and perform a responsive activity based responsive to a determination of the presence of the alert state.

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.