Heavy rail transit systems have two major interrelated challenges:
– Cost (capital and operating)
The ATN concept has the potential of addressing these challenges but suffers from two other, major challenges:
– Line capacity to justify the economics of construction
– Vehicle controller equipment costs.
ATN systems can operate smaller, lighter vehicles that require less-costly infrastructure and provide more convenient service. However, currently-available control systems are not capable of achieving traffic densities that improve upon and satisfy the economic, operational and safety cases.
Current onboard control technology, with costs that can be on the order of $200,000 per vehicle, is cost-prohibitive for use in smaller vehicles.
Until now, no real, viable ATN control solution has existed that can technically and economically meet these challenges.
The TCS product addresses all of these challenges with innovative design concepts that achieve a nearly five-fold improvement in traffic density, along with a dramatic reduction in the cost of buying and maintaining control equipment.
TCS designs and builds vehicle control and transportation management software solutions that can:
– Safely control driverless, automated vehicles operating on ATN systems at very close headways at freeway speeds.
– Meet or exceed the Mean Time Between Hazard criteria established by IEEE 1474.1-2004 (CBTC Performance and Functional Requirements) and the ASCE APM standards.
– Use commercially available off-the-shelf hardware for the vehicle controller, dramatically reducing the cost-per-vehicle.
– Safely and effectively merge high-density traffic streams at merge points.
Our emulation platform can be used to control virtual and physical cars. The following video shows each of these features.
With Virtual Cars:
– 50 virtual cars (shown with 6m diameter dots)
– Approximately 5km of track (lengths and curvatures drawn to scale)
– Eight offline stations
With Physical Cars:
– Three 1/32 scale cars
– 7.6 meters of track (244 full scale meters)
– Three offline stations