Route choice model based on cellular automata and cumulative prospect theory

In order to investigate the impact of travelers' adaptive adjustment behaviors on traffic network flowdiversion under the assumption of bounded rationality, a multi-agent route choice model with individual interaction mechanism is established by using cumulative prospect theory and evolutionary cellular automata. In the model, travelers are divided into risk-seeking and risk-aversion ones. Based on the reliability of travel time and the idea of cellular genetic algorithm, the dynamic reference points and their evolution rules for travelers with heterogeneous characteristics are designed to enable individual travelers dynamically adjust their travel time budget according to the changes in the decision-making environment. Finally, the evolution rule of multi-agent reference points is combined with the traditional method of successive average algorithm to design the multi-agent bounded rational route choice evolution algorithm for the solving the problem of traffic flow assignment in a road network. The research main contributions show that the evolution model has well inherited the characteristics of the route flow diversion in the traditional model.