Travel Behavior and Congestion Tolling Strategies in a Bi-Modal Bottleneck Model with Autonomous and Human-Driven Vehicles under Linear Scheduling Preferences
In recent years, activity-based bottleneck models have been widely used to address time allocation between commuting and activities. However, most previous studies adopted constant utility preferences and overlooked the dynamic marginal utility of time. This paper introduces a linear utility preference, assuming that marginal utility changes linearly over time, and recognizes that in-vehicle activities generally yield lower marginal utility than activities at home or at work because of limited physical resources, interpersonal interaction, and comfort. We examine bottleneck congestion in a bi-modal system with autonomous and human-driven vehicles, and analyze commuters’ travel time choices during the morning peak equilibrium. We then investigate congestion pricing and propose two schemes: a time-varying toll and a step toll. The results show that scheduling preferences significantly affect travel patterns and pricing strategies. The total social cost under linear scheduling preferences is substantially lower than that under constant scheduling preferences, suggesting that models with constant scheduling preferences may overestimate social cost.