Day-to-day route choice decision simulation based on dynamic feedback information

In reality, the individual's day-to-day route choice behavior is a long-time evolution process, and travelers choose their traveling routes according to the combination of historical experience and real-time traffic information. Considering two classes of users, one equipped with advanced traveler information systems (ATIS) and the other without, this paper investigates the travel efficiency under two different information feedback strategies, namely, travel time feedback strategy and mean velocity feedback strategy. The simulation results given by the cellular automaton model show that the more users rely on historical experience, the quicker the system evolves to the equilibrium. To all users, the travel efficiency decreases with travel demand and firstly increases then decreases with the dependent degree to historical experience. However, as far as the ATIS market penetration rate is concerned, the users' travel efficiency under two feedback strategies exhibits different trends. The equipped travelers have shorter travel time, particularly under mean velocity feedback strategy. In addition, the difference of travel efficiencies between two classes of users decreases as days evolve.