Transitioning Roadways to Accommodate Connected and Automated Vehicles: A Pennsylvania Case Study

Samaras, Costa; Henrickson, Chris; Harper, Corey · 2017 · ROSA P / Technologies for Safe and Efficient Transportation. University Transportation Center

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Summary

This dissertation investigates the technical, economic, and policy implications of transitioning roadways to accommodate connected and automated vehicles (CAVs). Motivated by the rapid advancement of automotive technology and the need for informed policymaking during the shift from human-driven to automated fleets, the research addresses four key areas: the economic viability of partially automated crash avoidance systems, infrastructure adaptations for truck platooning, changes in parking behavior due to driverless vehicles, and potential increases in travel demand from underserved populations. The study utilizes a multi-method approach, including benefit-cost analysis, traffic simulation, agent-based modeling, and bounding analysis, using data from Pennsylvania and Seattle. The first component evaluates the fleet-wide deployment of Level 1 driver assistance systems, such as lane departure and forward collision warnings. Using crash data from the General Estimate System and Fatality Analysis Reporting System, the analysis estimates that current system effectiveness could yield a net benefit of approximately $4 billion annually. If all relevant crashes were preventable, the annual net benefit could reach $202 billion, addressing roughly 25% of all crashes. The second component examines infrastructure transitions via a case study of the Pennsylvania Turnpike. By simulating dedicated lanes for commercial truck platooning, the research identifies specific sections where such lanes can be implemented without degrading the level of service for other vehicles, providing a method for selecting viable demonstration sites. The third component models the behavior of privately owned driverless vehicles in Seattle, focusing on parking decisions. Using an agent-based model on a rectangular grid, the study finds that while driverless vehicles searching for cheaper, distant parking are unlikely to cause significant increases in vehicle miles traveled (VMT) or energy use, they could severely reduce parking lot revenues. At high penetration rates, operating downtown parking facilities may become economically unsustainable without demand management policies. The final component estimates the upper bound of VMT increases resulting from enhanced mobility for non-drivers, the elderly, and individuals with medical conditions. Through a three-demand wedge bounding analysis, the study projects a potential 14% increase in annual light-duty VMT, equating to an upper bound of 295 billion additional miles for the U.S. population aged 19 and older. The significance of this work lies in its comprehensive assessment of both the benefits and challenges of CAV adoption. It provides policymakers with quantitative evidence to maximize safety and economic benefits while mitigating risks such as parking infrastructure collapse and induced demand. The findings suggest that while partial automation offers substantial immediate safety benefits, the transition to full automation requires proactive infrastructure planning and regulatory frameworks to manage changes in travel behavior and urban land use.

Key finding

Fleet-wide adoption of Level 1 crash avoidance technologies yields a net benefit of approximately $4 billion annually, while high penetration of driverless vehicles threatens the economic sustainability of downtown parking operations despite minimal increases in vehicle miles traveled.

Methodology

mixed_methods

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

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• driverless ads
• acceptance adoption
• exposure measurement

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Empirical Findings: observational prevalence, crash risk outcomes
• Methodological Resource: dataset resource