Redesigning Trip-Based Travel Demand Models for the Connected Automated Vehicle Era
Abstract
This study introduces a systematic redesign of trip-based travel demand models to explicitly incorporate connected automated vehicles (CAVs). The framework models auto ownership of CAVs and human-driven households separately, accounts for zero-occupancy vehicle (ZOV) trips, and modifies trip distribution, mode choice, and temporal patterns to reflect anticipated behavioral and operational changes because of CAV presence. The framework is then applied to the Triangle Region of North Carolina, using Triangle Regional Model Generation 2, and reveals that CAV adoption increases vehicle miles traveled (VMT) and encourages longer trips, with average discretionary and work trips rising by 28%–31% under a 70% CAV adoption rate. Although VMT increases, effective capacity gains reduce total network delay by as much as 60% relative to the 2050 baseline. At the facility level, demand-to-capacity ratios decrease, indicating that some roadway expansion projects could potentially be deferred. Sensitivity analysis reveals that system performance is highly dependent on realized capacity improvements and cautious assumptions, producing nearly 40% more delay than the expected scenario. The proposed redesign framework provides transportation agencies with a scalable and practical approach to incorporating CAVs into long-range planning, project prioritization, and investment decision-making.