This study introduces an agent-based dynamic feedback-control toll pricing strategy that accounts for the trip purpose, travel time reliability, departure time choice and level of income such that the toll revenue is maximised while maintaining a minimum desired level of service on the managed lanes. An agent-based modelling was applied to simulate drivers’ learning process based on their previous commuting experience. The study also analysed how drivers’ heterogeneity in value of time, and value of reliability for each trip purpose will influence route decisions and thus affect the optimal toll rates. Comparative evaluation between the newly developed strategy, the strategy currently deployed on Interstate 95 express lanes, and another strategy previously developed by the authors shows that the agent-based strategy produced a steadier increase in toll rate during the peak hours and a significantly higher toll revenue at speeds higher than 45 mph.

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Accounting for travel time reliability, trip purpose and departure time choice in an agent-based dynamic toll pricing approach

References

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