Fuzzy logic-based scheduling of rail vehicles under reduced traffic flow conditions

Scheduling of Traffic Entities Under Reduced Traffic Flow by Means of Fuzzy Logic Control

“…The overall model and the selected traffic control laws are simulated within Matlab software environment [20]. The simulation scenarios have included the following two cases [17]: 1) Fixed green light intervals proportional to transition time (T L2 =10 min, T L1 =αT L2 =15 min); 2) Fuzzy logic-based green light adaptation, with base time interval T 0 =10 min.…”

“…Moreover, ANNs have been successfully used for vehicle location prediction to increase transportation safety through communicating vehicle dynamic variables and navigation data between neighbouring vehicles [15]. The fuzzy logic-based approach has also shown great promise in transport routing optimisation [16], including those cases when emergency traffic re-routing is mandated by the occurrence of planned or incident-related traffic obstruction [17]. In particular, reference [17] shows that by adapting the duration of green light intervals for both directions of vehicle motion while utilising a single traffic lane (or railway track) to circumvent the traffic obstruction, a notable reduction of vehicle queues can be achieved compared to the traditional traffic control automaton with fixed green light interval durations.…”