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Validating the results of a route choice simulator

RL asserts that decision makers’ behaviors are moved towards the direction of random choice due to the environmental uncertainty [24].

Stochastic models mainly focus on the probability distribution of flow states [6, 7].

They found that the network flow does not necessarily converge to the user equilibrium (UE) and travelers’ cognitions of each route do not become homogeneous by learning [12–14]. assumed that each traveler used a disutility function to perceive travel time and schedule delay for evaluating the alternative travel choices, and then chose an alternative route according to the utility maximization principle [15].

Chen and Mahmassani modeled travelers’ leaning process by using Bayesian theory and an agent-based simulation framework to study networks’ dynamic properties [16].

In some literatures, traffic dynamics are modeled as continuous time processes [3, 4].

However, it is not reasonable to adjust traffic flow continuously due to travelers’ activities constraints.

Comments Validating the results of a route choice simulator

  • A Day-to-Day Route Choice Model Based on Reinforcement Learning

    Aug 28, 2014. In this paper, we propose a day-to-day route choice model based on reinforcement learning and multiagent simulation. Travelers' memory, learning rate, and experience cognition are taken into account. Then the model is verified and analyzed. Results show that the network flow can converge to user.…