GE Make Strides Towards “Conductorless” Trains

Projections based off current freight rail growth suggest freight traffic will increase 90% by 2035. Although the rails need to accommodate for this growth, there is still only so much space to expand the rails. GE has been creating solutions to reduce traffic between freight cars and reduce the projected increase in pollution. GE’s Trip Optimizer system strives to use data affiliated with existing train types and train rails to reduce fuel efficiency. Most emissions in freight rails coming from speed variations and standing trains and Trip Optimizer seeks focus on constant, consistent travel, rather than speed, which can lead to traffic jams and track holdups. The Trip Optimizer system studies the typical speeds of engineers on routes and then takes the chooses more gradual changes in speed for the same routes. This reduces average moving time speed, but significantly reduces waiting time and energy used.

The Trip Optimizer system studies the typical speeds of engineers on routes and then takes the chooses more gradual changes in speed for the same routes.

The Trip Optimizer system studies the typical speeds of engineers on routes and then takes the chooses more gradual changes in speed for the same routes.

This technology communicates with GE’s RailEdge technology. This technology communicates with other trains in the network to avoid intersections and traffic jams. This system is made to compensate for any speed lost from the Trip Optimizer. The CEO of GE Transportation explains, “RailEdge optimizes the railroad resources that are already in place — something that only technology can truly help us achieve.” Through these systems freight routes and exact speeds are determined and given to the engineers onboard live.

RailEdge Console

 

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2 thoughts on “GE Make Strides Towards “Conductorless” Trains

  1. I wonder how thinking about infrastructure at this very large scale could somehow inform a proposal for a prototype that would operate as a tool for design / data model interface. What can we learn from systems applied to the national train network for building a tool for designing in the (spatial, temporal, social, environmental, political, etc.) fabric of the city? Certainly trip optimization is something that many people use, with various wayfinding apps. Beyond this, I wonder if thinking about rail infrastructure might help us think about control systems that mediate between intersections/collisions between scales in the city – where a freeway meets a small neighborhood, where a highspeed train meets a divy station, etc.

    • Speed in the city is worth considering. Paul Virilio has described the city as “a stopover, a point on the synoptic path of a trajectory” and an “uncertain [place] because it is situated between two speeds of transit, acting as a brake against the acceleration of penetration.” The friction that arrises at these points of intersection between speeds, and the outflow it produces, is responsible, one might argue, for much of the cultural production of the city. What sort of tool would allow the designer access and agency in these uncertain trajectories and their intersections? What kinds of overlaps exist between the wayfinding/speed control algorithms of a train network, a car GPS, and a fed ex parcel? Is there a relationship between the data from street grid traffic flows and the Nike running shoe GPS chip?

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