The present application involves a railroad track asset surveying system comprising an image capture sensor, a location determining system, and an image processor. The image capture sensor is mounted to a railroad vehicle. The location determining system holds images captured by the image capture sensor. The image processor includes an asset classifier and an asset status analyser. The asset classifier detects an asset in one or more captured images and classifies the detected asset by assigning an asset type to the detected asset from a predetermined list of asset types according to one or more features in the captured image. The asset status analyser identifies an asset status characteristic and compares the identified status characteristic to a predetermined asset characteristic so as to evaluate a deviation therefrom.

Systems and devices that will notify train system maintenance workers of an approaching vehicle and, conversely, will notify the operators and administrators of train systems of train system maintenance workers within the vicinity of an approaching section of track. Embodiments of the safety systems and methods disclosed herein may use track and direction information and/or LiDAR to assist in determining vehicle positioning and/or speed particularly where there are two or more possible tracks or vehicle paths in close proximity to each other.

A system includes at least one application device, a control unit, and at least one processor. The at least one application device is conductively or inductively coupled with at least one of a first conductive track or a second conductive track. The control unit is configured to control supply of electric current from a power source to the at least one application device to electrically inject at least one examination signal into the conductive tracks. The at least one processor is configured to monitor the one or more electrical characteristics of at least one of the first or second conductive tracks, and to identify a construction feature of the route based on the one or more electrical characteristics, wherein the construction feature corresponds to a man-made aspect of the route.

A track collision avoidance control system including a train having an onboard control system for controlling one or more characteristics of the train. The track collision avoidance control system can further include a warning indication system comprising a transmitter linked to a communication system supported about the train, the communication system comprising a receiver operable to receive a signal from the transmitter, the signal comprising information pertaining to a potentially dangerous condition. The transmitter can be operable alone or in combination with a warning indicator, an actuator and their associated logic circuitry, such that the transmitter is caused to transmit upon the warning indicator being activated. The communication system can interface with an onboard control system of the train. The communication system can receive the signal, which can be used to facilitate an avoidance action to be taken relative to the train.

The present invention relates generally to a train direction detection device and a method of determining the direction of travel. The train direction detection device analyzes the characteristics, such as impedance, of an electrical circuit implemented on a railroad to determine the direction of approach of a train. The invention is adapted to integrate with occupancy or grade crossing circuits commonly used by railroads at grade crossings.

Systems and techniques for adaptively classifying parking lots associated with a hub. In embodiments, adaptively classifying parking lots associated with a hub may include classifying the physical parking lots of the hub into one or more logical parking lots, in which each logical parking lot may have a parking lot classification. In embodiments, the configuration of each logical parking lot (e.g., the classification, layout, size, etc.) may be based on various factors, which may include factors associated with the physical parking lots, factors associated with the hub, environmental factors, cyclical factors, seasonal factors, unit traffic through the hub, proximity of the parking lots to productions tracks, ease of access to the parking lots, direction of traffic flow within the hub, etc. In embodiments, the logical parking lots may represent a logical or virtual classification of the physical parking lots.

A computer-implemented method of determining a position of a vehicle 10 within a transport network comprises: obtaining track geometry data indicating track geometry of at least a part of the transport network; determining, based upon the track geometry data, that the vehicle is approaching a junction; determining, based upon the track geometry data, a plurality of route options from the junction; generating a plurality of Bayesian estimation filter algorithms each associated with a respective one of the plurality of route options and configured to estimate a position of the vehicle based upon the track geometry data indicative of the associated route option, wherein the plurality of Bayesian estimation filter algorithms are configured to output data indicative of probabilities of the vehicle taking the associated route options; monitoring the output of the plurality of Bayesian estimation filter algorithms as the vehicle passes through the junction; and determining the route option taken by the vehicle by selecting one of the plurality of route options which presents the highest probability based upon the output of the plurality of Bayesian estimation filter algorithms.

A method may include detecting a first pressure and a second pressure of a fluid in a brake pipe of a vehicle system that includes a plurality of vehicles and extends from a lead vehicle to an end vehicle. The first pressure may be measured in the lead vehicle and the second pressure may be measured in the end vehicle. The method may further include determining a pressure differential signature between the first pressure and the second pressure and evaluating the pressure differential signature with a machine learning model to determine whether a blockage or a leak exists in the brake pipe. A system may include one or more processors configured to detect a first pressure and a second pressure of a fluid in a brake pipe. The one or more processors may be further configured to determine a pressure differential signature between the first pressure and the second pressure and evaluate the pressure differential signature with a machine learning model to determine whether a blockage exists in the brake pipe.

Methods and systems for train detection apparatus angle alignment is described. A train detection system includes a train detection apparatus configured for installation at an installation site at a defined angle with respect to railroad tracks and a processor configured to execute a method for aligning the train detection apparatus to the defined angle. The processor configured to determine a railroad tracks angle with respect to a reference angle based on an image captured of the installation site, obtain, from a sensor, a train detection apparatus angle with respect to the reference angle, determine an alignment angle based on the railroad tracks angle and the train detection apparatus angle, and send the alignment angle to substantially align the train detection apparatus to the railroad tracks in accordance with the defined angle.

The invention relates to automatically determining an efficient topology of sensor network preferably installed in an infrastructure, such as railway infrastructure. The invention discloses a system, a method, a user device. The invention further provides a processing component configured to receive sensor data from at least one or a plurality of sensor node(s). Further, the invention provides a trajectory module, configured to generate at least a part of trajectory data based on sensor data.