Stochastic learning automata (SLA) theory is used to model the learning behavior of commuters within the context of the combined departure time route choice (CDTRC) problem. The SLA model uses a reinforcement scheme to model the learning behavior of drivers. A multiaction linear reward-ϵ-penalty reinforcement scheme was introduced to model the learning behavior of travelers based on past departure time choice and route choice. A traffic simulation was developed to test the model. The results of the simulation are intended to show that drivers learn the best CDTRC option, and the network achieves user equilibrium in the long run. Results indicate that the developed SLA model accurately portrays the learning behavior of drivers, while the network satisfies user equilibrium conditions.
Application of Stochastic Learning Automata for Modeling Departure Time and Route Choice Behavior
Transportation Research Record: Journal of the Transportation Research Board
Transportation Research Record: Journal of the Transportation Research Board ; 1807 , 1 ; 154-162
01.01.2002
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Application of Stochastic Learning Automata for Modeling Departure Time and Route Choice Behavior
Online Contents | 2002
|Application of Stochastic Learning Automata for Modeling Departure Time and Route Choice Behavior
British Library Conference Proceedings | 2002
|Modeling Route Choice Behavior with Stochastic Learning Automata
Online Contents | 2001
|Modeling Route Choice Behavior with Stochastic Learning Automata
Transportation Research Record | 2001
|Punctuality-based route and departure time choice
Taylor & Francis Verlag | 2014
|