A train control system minimizes in-train forces in a train with a hybrid consist including a diesel-electric locomotive and a battery electric locomotive. The train control system includes a virtual in-train forces modeling engine configured to simulate in-train forces and train operational characteristics using physics-based equations, kinematic or dynamic modeling of behavior of the train or components of the train when the train is accelerating, and inputs derived from stored historical contextual data characteristic of the train, and a virtual in-train forces model database configured to store in-train forces models. Each of the in-train forces models includes a mapping between combinations of the stored historical contextual data and corresponding simulated in-train forces and train operational characteristics that occur when the consist is changing speed. An energy management system determines an easing function of tractive effort vs. time that will minimize the in-train forces created by changes in tractive effort responsive to power notch changes in a diesel-electric locomotive, and commands execution of the easing function by a battery electric locomotive based at least in part on an in-train forces model with simulated in-train forces and train operational characteristics that fall within a predetermined acceptable range of values.

A train control system minimizes in-train forces in a train with a hybrid consist including a diesel-electric locomotive and a battery electric locomotive. The train control system includes a virtual in-train forces modeling engine configured to simulate in-train forces and train operational characteristics using physics-based equations, kinematic or dynamic modeling of behavior of the train or components of the train when the train is accelerating, and inputs derived from stored historical contextual data characteristic of the train, and a virtual in-train forces model database configured to store in-train forces models. Each of the in-train forces models includes a mapping between combinations of the stored historical contextual data and corresponding simulated in-train forces and train operational characteristics that occur when the consist is changing speed. An energy management system determines an easing function of tractive effort vs. time that will minimize the in-train forces created by changes in tractive effort responsive to power notch changes in a diesel-electric locomotive, and commands execution of the easing function by a battery electric locomotive based at least in part on an in-train forces model with simulated in-train forces and train operational characteristics that fall within a predetermined acceptable range of values.

A plurality of modules housed in a chassis is secured to the railroad tie to capture high resolution images of the underside of a train car as it passes over the plurality of cameras. These images are important to insure the structural integrity of the parts of the rail car as well as the connection meansa coupler and pinbetween two rail cars. The cameras are configured to capture at a predetermined rate or to operate in sync with the velocity of the train as measured by the Linear Speed Sensor. As the images are taken, the images can be downloaded and sent to a remote location for further analysis.

A plurality of modules housed in a chassis is secured to the railroad tie to capture high resolution images of the underside of a train car as it passes over the plurality of cameras. These images are important to insure the structural integrity of the parts of the rail car as well as the connection meansa coupler and pinbetween two rail cars. The cameras are configured to capture at a predetermined rate or to operate in sync with the velocity of the train as measured by the Linear Speed Sensor. As the images are taken, the images can be downloaded and sent to a remote location for further analysis.

A method of controlling one or more locomotives in a train includes using a machine learning engine and a virtual system modeling engine to model and classify sections of track along which the train is traveling according to the tractive power needs for the train traversing each section of track as a function of an effective weight profile for the train in the section and an effective friction profile for the train in the section of track. The method includes using the results of the effective weight profile, the effective friction profile, and an effective power availability profile to train the virtual system modeling engine using the machine learning engine to model designated areas of the track where the total tractive effort force or dynamic braking force applied by all of the locomotives in the train is less than a tractive effort force or dynamic braking force, respectively, that can be provided by a subset of the available locomotives in the train.

A method of controlling one or more locomotives in a train includes using a machine learning engine and a virtual system modeling engine to model and classify sections of track along which the train is traveling according to the tractive power needs for the train traversing each section of track as a function of an effective weight profile for the train in the section and an effective friction profile for the train in the section of track. The method includes using the results of the effective weight profile, the effective friction profile, and an effective power availability profile to train the virtual system modeling engine using the machine learning engine to model designated areas of the track where the total tractive effort force or dynamic braking force applied by all of the locomotives in the train is less than a tractive effort force or dynamic braking force, respectively, that can be provided by a subset of the available locomotives in the train.

A system includes one or more processors and memory coupled to the one or more processors, storing processor-executable instructions that cause the one or processors to perform operations. The operations include, prior to a train departing from a departure location: generating, one or more train departure strategies for a segment of a trip for the train traveling on a track from the departure location where the train is stationary, displaying information associated with the segment of the track, playing a simulation of the train traveling on the track over the segment based on the one or more train departure strategies, the simulation beginning from when the train is stationary at the departure location; receiving a selection of a train departure strategy, and engaging the selected train departure strategy for operating the train from the departure location where the train is stationary.

A system includes one or more processors and memory coupled to the one or more processors, storing processor-executable instructions that cause the one or processors to perform operations. The operations include, prior to a train departing from a departure location: generating, one or more train departure strategies for a segment of a trip for the train traveling on a track from the departure location where the train is stationary, displaying information associated with the segment of the track, playing a simulation of the train traveling on the track over the segment based on the one or more train departure strategies, the simulation beginning from when the train is stationary at the departure location; receiving a selection of a train departure strategy, and engaging the selected train departure strategy for operating the train from the departure location where the train is stationary.

A method and an apparatus for a train control system are disclosed, and are based on virtualization of train control logic and the use of cloud computing resources. A train control system is configured into two main parts. The first part includes physical elements of the train control system, and the second part includes a virtual train control system that provides the computing resources for the required train control application platforms. The disclosed architecture can be used with various train control technologies, including communications based train control, cab-signaling and fixed block, wayside signal technology. Further, the disclosure describes methodologies to convert cab-signaling and manual operations into distance to go operation.

Disclosed is a rolling unit (8) for a test rig (1) for testing an automatic underground train, including: two rolling belts (27), each one provided for a wheel (6) of the train to roll thereon, the wheels driving the movement of the belts; and a rotary inertial body (28); each belt including: a pinion (31) that is rotatably connected to the inertial body; two rollers (33); and a grooved rolling surface (32) mounted on the rollers, meshed with the pinion, and forming a rolling area (34) for a respective wheel between the rollers.