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Validating the results of a route choice simulator
Finally, both the scenarios of link capacity degradation and random link capacity are used to illustrate the model’s applications.
However, it is not reasonable to adjust traffic flow continuously due to travelers’ activities constraints.Some researchers use deterministic models or stochastic models to study the day-to-day flow evolution. proposed three dynamic systems by the deterministic traffic assignment processes [3, 4].The flow evolution trajectory can be provided explicitly in deterministic models.It is hard for them to make the best choice every time due to their bounded rationality and the uncertainty of environment. If the travel time of a path was short in the past days, its probability to be chosen is big in the current day. These characteristics are similar with the features of the reinforcement learning (RL) theory.Under RL, if an action yielded a high payoff in the past days, the probability assigned to it increases in the current round, or the behavior associated with the action gets reinforced . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Day-to-day traffic dynamics are generated by individual traveler’s route choice and route adjustment behaviors, which are appropriate to be researched by using agent-based model and learning theory.Klügl and Bazzan used a simple heuristic model to stimulate the process of travelers’ decision-making and studied the effect of traffic forecast .They set up an agent-based model to stimulate travelers’ route choice and found that travelers could avoid the Braess Paradox by learning .Daganzo and Sheffi put forward the stochastic user equilibrium to model the evolution of the traffic pattern .Stochastic models mainly focus on the probability distribution of flow states [6, 7].