Accounting for Travel Time Reliability and Trip Purpose in an Agent-Based Approach to Toll Pricing with Dynamic Feedback Control

Abstract

Congestion pricing is an effective management policy to reduce traffic congestion on freeways. This study accounted for the travel time difference and reliability on managed and general-purpose lanes in a modified approach to determining toll rates recently developed by others. The original approach was modified by developing an agent-based toll pricing strategy that used dynamic feedback control and accounted for trip purpose, travel time reliability (TTR), and level of income to maximize toll revenue while maintaining a minimum desired level of service on managed lanes. An external module was developed to execute the modified strategy in VISSIM, which exported the traffic data generated to an Excel spreadsheet in real time to calculate TTR, used in the route choice process. Agent-based modeling simulated drivers’ learning process and estimation of TTR from their previous commuting experience. A numerical example illustrated the modified strategy. The simulation results confirmed that, under high traffic demand, drivers with an urgent trip purpose had the highest probability of choosing managed lanes and that travel time on those lanes was more reliable than on general-purpose lanes. The modified strategy, the strategy currently deployed on I-95 express lanes, and the original strategy were compared. The modified one had a steadier increase in toll rate, significantly higher toll revenue, and speeds higher than 45 mph more frequently, compared with the current strategy. Furthermore, the modified approach was more realistic, accounted for TTR in route choice, and generated higher toll revenue under heavy traffic demand than did the original strategy.