Evaluating Autonomous Truck Adoption: An Elasticity-Based Model of Demand, Modal Shift, and Emissions

This study develops a compact elasticity-based framework for assessing how autonomous truck adoption influences corridor-level performance, freight demand, modal competition, and CO2 emissions in multimodal freight Intelligent Transportation Systems. The model specifies the constant elastic (log-linear) responses of traffic performance and generalized costs to adoption, enabling the closed-form characterization of system-level rebound and road–rail reallocation effects. The analytical results show that an internal adoption threshold P* emerges, defined by dE^/dP=0, which separates a beneficial regime (efficiency gains dominate) from an adverse regime (rebound and modal shift dominate). Comparative statics indicate that P* decreases with stronger ITS capability A and increases with rebound intensity R and the road–rail carbon intensity contrast K. Numerical experiments across representative corridor contexts illustrate induced demand effects exceeding 25% under high-rebound conditions and threshold ranges around P*≈0.3–0.4 for plausible parameters. The results provide interpretable guidance for coordinating autonomous truck deployment with intermodal logistics design and decarbonization strategies.