Implementation of Connected and Autonomous Vehicles in Cities Could Have Neutral Effects on the Total Travel Time Costs: Modeling and Analysis for a Circular City

Medina-Tapia, Marcos; Robusté, Francesc · 2019 · OpenAlex-citations

DOI: 10.3390/su11020482

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Summary

This study investigates the impact of implementing Connected and Autonomous Vehicles (CAVs) on urban travel time costs, addressing the uncertainty regarding whether this technology will alleviate or exacerbate traffic congestion. The authors argue that while CAVs offer direct technical benefits, indirect behavioral changes may counterbalance these gains. To analyze this, the researchers developed a macroscopic model using the Continuous Approximation (CA) method, which allows for the optimization of urban road networks in a continuous space rather than discrete links. The model focuses on a circular city structure with radial and circular primary roads, considering a mixed traffic scenario where CAVs coexist with Manual Vehicles (MVs). The objective function minimizes total system cost, comprising user costs (travel time, operational costs) and agency costs (infrastructure). The methodology incorporates both direct and indirect effects of CAVs. Direct effects include increased road capacity due to platooning and shorter headways, reduced operational costs, and improved intersection efficiency via vehicle-to-infrastructure communication. Indirect effects modeled include a reduction in the value of travel time savings (as passengers engage in other activities), induced demand, increased vehicle-kilometers traveled (VKT) due to "ghost trips" and split journeys, and potential urban sprawl. The model was applied to a case study of a circular city with a 15 km radius, divided into nine zones with a symmetric distribution of 405,000 private trips per hour. The optimization determined the optimal spacing between circular and radial roads for both a current scenario (MVs only) and a future scenario (full CAV implementation). The results indicate that direct effects are positive, reducing the required street supply and lowering congestion levels, potentially decreasing travel costs by 30%. However, indirect effects significantly offset these benefits. A 20% decrease in the value of travel time reduces total costs by a third, but induced demand could increase by up to 50%, bringing total costs back to equivalent levels. Additionally, the model predicts that CAVs would allow for a reduction in the density of the road network, with the optimal distance between circular roads increasing from 3.8 km to 7.5 km. Despite these efficiencies, the increase in VKT and changes in trip distribution suggest that the net effect on total travel time costs is neutral. The study concludes that the implementation of CAVs alone is unlikely to reduce total urban travel time costs due to the counterbalancing nature of induced demand and behavioral shifts. Consequently, the authors argue that city planning agencies must promote complementary modes, such as active mobility, public transit, and shared mobility services, to realize the potential benefits of autonomous technology. The findings highlight the importance of considering behavioral responses in transportation planning, suggesting that technological improvements in vehicle efficiency do not automatically translate to systemic congestion relief without complementary policy interventions.

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