Maria Magdalena Santa

Train Traffic Control Based on Distributed Resource Allocation

Abstract The railway traffic is characterized as a large and dynamic system with uncertain properties related to resource loading, train arrivals and failures. Despite these uncertainties, the control system is expected to guarantee that all the trains behave according to their timelines. The current approach solves the railway traffic control problem using the resource allocation. The trains are considered tasks with specified temporal behaviors that have to fulfill their deadlines. The solutions based on open loop, closed loop with independent, coordinated and heterarchical controllers are defined and compared. The control signals are implemented and verified using time Petri nets. Some algorithms for control system implementation are given. The method evaluations are performed using the meter functions: utility, utilization, reservation and efficiency. The results obtained through simulations show that the proposed distributed controllers solve adequately the control problems and can be used for large scale implementation.

Evolutionary method for railway monitoring systems

The paper presents an evolutionary method based on genetic programming (GP) for synthesizing of a monitor alarm system for the railway control traffic unit. Automatic supervision of railway traffic control is a very important and complex task. A wrong control signal can lead to very serious incidents or accidents. A well-designed monitoring system can prevent these accidents by a simple alarm which signals the appearance of a wrong control signal. The railway network or the plant is modeled by Delay Time Petri Nets (DTPN) and the railway traffic control unit by Time Petri Nets (TPN). The alarm monitor contains transitions joined to the plant and control unit in order to achieve the information on the positions of trains, respectively the control signals of the control unit, and generates an alarm whenever the control signal can cause to an incident or accident. The TPN model of the monitor system is generated by means of the genetic programming method using a Lisp representation of the solution.