The future of fully automated vehicles : opportunities for vehicle- and ride-sharing, with cost and emissions savings.

Fagnant, Daniel; Kockelman, Kara · 2014 · ROSA P / Texas A&M Transportation Institute

archive: archived pipeline: cataloged verified

Get this paper ↗ (full text — opens at the source; we link to it, we don't host it)

Summary

This report investigates the potential impacts of fully automated vehicles (AVs) on transportation systems, focusing on economic benefits, safety improvements, and the emergence of Shared Autonomous Vehicles (SAVs). The research is motivated by the anticipated commercialization of self-driving technology by major manufacturers and technology providers by 2020. The authors aim to quantify the societal benefits of AV adoption while identifying barriers to implementation, such as liability, security, privacy, and vehicle costs. A central objective is to evaluate whether SAVs—combining on-demand service with autonomous capabilities—can reduce vehicle ownership, lower emissions, and improve travel efficiency compared to conventional household vehicles. The study employs a three-part methodology. Part 1 provides a qualitative overview and economic evaluation of AV impacts, estimating benefits based on varying market penetration levels. Parts 2 and 3 utilize agent-based modeling to simulate SAV fleets. Part 2 tests an idealized grid-based urban environment to evaluate vehicle relocation strategies. Part 3 expands this analysis to a realistic network setting using the City of Austin’s transportation network and travel demand data. This model incorporates dynamic ride-sharing (DRS), allowing travelers with similar origins, destinations, and departure times to share rides. The simulation integrates C++ code for fleet assignment and route selection with the MATSim network-simulation model to update hourly link-level travel times dynamically. Key findings indicate that AVs could save the U.S. economy up to $37.7 billion at 10% market penetration and $447.1 billion at 90% penetration, primarily through safety, mobility, and parking improvements. At 10% penetration, over 1,000 lives could be saved annually. The SAV simulations reveal that each SAV could replace approximately 10 conventionally owned household vehicles, with a fleet of 1,715 SAVs serving over 56,000 person-trips in Austin. While unoccupied relocation between trips may increase vehicle-miles traveled (VMT) by 4–8%, dynamic ride-sharing can reduce overall VMT if sufficient travelers are willing to share. SAVs are projected to yield favorable environmental outcomes, including 16% less energy use and 48% lower volatile organic compound emissions per person-trip compared to conventional vehicles. Greenhouse gas emissions may decrease by 5–7%, potentially rising to 10% or more with DRS. Economically, an SAV fleet costing $70,000 per vehicle could achieve a 19% return on investment when charging $1 per trip-mile. The significance of this work lies in its demonstration that SAVs offer a viable pathway to a more efficient and sustainable transport system. By reducing the need for private vehicle ownership and enabling high-occupancy travel through dynamic ride-sharing, SAVs can mitigate the negative environmental and congestion impacts often associated with increased vehicle automation. The study highlights that realizing these benefits requires overcoming significant regulatory and technical barriers, including liability frameworks and security protocols. The findings provide a quantitative basis for policymakers and urban planners to anticipate long-term energy and greenhouse gas emissions impacts, suggesting that shared autonomous mobility could substantially enhance safety and economic efficiency while reducing per-capita environmental footprints.

Key finding

Shared autonomous vehicles with dynamic ride-sharing reduce energy use by 16% and volatile organic compound emissions by 48% per person-trip while replacing approximately ten conventional household vehicles per SAV.

Methodology

modeling

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).

Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• driverless ads
• acceptance adoption