Congestion Pricing in a World of Self-driving vehicles: an Analysis of Different Strategies in Alternative Future Scenarios (1803.10872v4)

Abstract: The introduction of autonomous (self-driving) and shared autonomous vehicles (AVs and SAVs) will affect travel destinations and distances, mode choice, and congestion. From a traffic perspective, although some congestion reduction may be achieved (thanks to fewer crashes and tighter headways), car-trip frequencies and vehicle miles traveled (VMT) are likely to rise significantly, reducing the benefits of driverless vehicles. Congestion pricing (CP) and road tolls are key tools for moderating demand and incentivizing more socially and environmentally optimal travel choices. This work develops multiple CP and tolling strategies in alternative future scenarios, and investigates their effects on the Austin, Texas network conditions and traveler welfare, using the agent-based simulation model MATSim. Results suggest that, while all pricing strategies reduce congestion, their social welfare impacts differ in meaningful ways. More complex and advanced strategies perform better in terms of traffic conditions and traveler welfare, depending on the development of the mobility landscape of autonomous driving. The possibility to refund users by reinvesting toll revenues as traveler budgets plays a salient role in the overall efficiency of each CP strategy as well as in the public acceptability.

• The paper uses agent-based modeling to simulate congestion pricing strategies under different autonomous vehicle adoption scenarios in a major city network.
• Simulation results show that all tested congestion pricing strategies effectively reduced traffic congestion, although with varying social welfare gains.
• Advanced, dynamic congestion pricing strategies yield the highest social welfare gains, especially when their revenues are reinvested to improve public transit.

Analysis of Congestion Pricing in Autonomous Vehicle Scenarios

The transportation landscape is swiftly evolving, with the proliferation of autonomous vehicles (AVs) and shared autonomous vehicles (SAVs) profoundly impacting travel behavior, mode choice, and congestion levels. This essay discusses the implications of such advancements, focusing on congestion pricing (CP) as a viable strategy to mitigate the adverse effects of increased travel demand and vehicle miles traveled (VMT). The referenced paper undertakes an agent-based modeling approach to simulate different CP strategies within the Austin, Texas network. Various future scenarios envision different levels of AV and SAV adoption, providing useful insights into the efficacy of traditional and advanced CP strategies.

Traffic Dynamics and Congestion Pricing

The paper explores multiple CP approaches including distance-based, link-based marginal cost pricing (MCP), and travel time congestion-based schemes. These strategies aim to reduce congestion by internalizing the social and economic costs of marginal road usage, with a particular focus on strategic tolling which can leverage AV and SAV capabilities for precise implementation. Specifically, the travel time congestion-based approach utilizes dynamic tolling akin to transport network companies' surge pricing, adjusting costs based on real-time traffic conditions.

Simulation Results

The MATSim agent-based framework was employed to model travel behavior and traffic externalities, yielding significant insights into the impacts of CP under varied AV and SAV penetrations. Notably, all CP strategies effectively diminished congestion, albeit with differences in social welfare gains and modal shifts. Scenarios with high SAV adoption showed SAVs contributing to increased VMT due to empty repositioning trips, emphasizing the need for strategic trip management and coordination to alleviate congestion.

Theoretical and Practical Implications

The paper suggests that advanced CP strategies could transition from second-best to first-best solutions that address congestion externalities more efficiently, with AV technology facilitating real-time data-sharing and dynamic toll adjustment. The simulation demonstrates that relying on link-level congestion targeting is beneficial in scenarios dominated by AVs, contrasting with distance- and travel-time-focused strategies in SAV-oriented contexts. Importantly, advanced CP schemes delivered notable improvements in social welfare, particularly when toll revenues were reinvested into public transit enhancements or distributed equitably among residents.

Economic Analysis and Policy Recommendations

The paper underscores the economic rationale of CP, emphasizing the calculation of consumer surplus and total welfare changes. The MCP and travel-time congestion schemes manifested the largest welfare gains, owing to their dynamic nature and alignment with traffic patterns. This invites future considerations for policy frameworks that integrate these strategies with broader urban planning and sustainability measures. Reinforcing public transit infrastructure using CP revenues could enhance modal shifts and reduce overall travel demand, suggesting a synergistic approach to autonomous mobility and congestion management.

Concluding Remarks

This research substantially contributes to the understanding of CP in scenarios dominated by AVs and SAVs, offering quantitative evidence for transport policy pathways that favor advanced technological integration. Future work could further refine agent-based simulations with detailed land-use variables and address broader economic impacts. Additionally, investigating the implications of dynamic ride-sharing in SAV fleets vis-à-vis CP strategies could reveal new dimensions in traffic optimization.

As autonomous transportation continues to evolve, the insights from this paper provide groundwork for subsequent research aiming to capture the multi-faceted impacts of AVs and SAVs on urban mobility and devise effective pricing mechanisms that promote long-term socio-economic benefits.