Bringing smart transport to Texans : ensuring the benefits of a connected and autonomous transport system in Texas—final report.
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, commissioned by the Texas Department of Transportation (TxDOT) and conducted by the University of Texas at Austin’s Center for Transportation Research, addresses the integration of connected and autonomous vehicles (CAVs) into Texas’s transportation infrastructure. The primary objective is to ensure that the deployment of these technologies maximizes benefits in driver safety, congestion reduction, and agency cost savings, while mitigating risks associated with poor implementation. The study aims to provide TxDOT with actionable recommendations for policy, design, and operational strategies as CAVs are projected to impact the U.S. economy by up to $1.3 trillion annually. The research employed a multidisciplinary approach combining legal analysis, public opinion surveys, traffic simulation, emissions modeling, and field demonstrations. The team analyzed existing legal frameworks regarding liability, privacy, and security for CAVs in Texas and nationally. They conducted statewide surveys to assess public willingness-to-pay for automation and connectivity, forecasting long-term adoption rates under various pricing and regulatory scenarios. Technically, the project utilized dynamic microtolling simulations and Autonomous Intersection Management (AIM) models to evaluate network dynamics and delay reductions. Safety benefits were estimated using the Safety Surrogate Assessment Model (SSAM) across urban bottlenecks and freeway ramps, while emissions impacts were modeled using the MOVES framework with specific driving cycles. Additionally, the team developed algorithms for real-time traffic flow monitoring using Inertial Measurement Units (IMUs) and conducted field demonstrations of Dedicated Short-Range Communications (DSRC) applications, including wrong-way driving alerts and road condition monitoring. Key findings indicate that well-implemented CAV technologies could reduce current crash costs by at least $390 billion per year. The simulations demonstrated significant delay reductions through smart ramp merges and intersection operations. The study identified substantial potential for emissions savings through smoother, automated driving profiles. Public opinion data revealed varying levels of acceptance and willingness-to-pay for different automation features, influencing forecasts for fleet evolution. The legal analysis highlighted complexities in tort liability and the need for updated regulatory frameworks to address privacy and security concerns. Field demonstrations successfully validated DSRC-based applications for emergency vehicle alerts and wrong-way driving detection. The report concludes with comprehensive recommendations for TxDOT, emphasizing the need to increase in-house technical expertise, update design manuals, and develop robust policies for CAV testing and deployment. It advocates for the use of simulation tools to evaluate new systems before implementation and highlights the economic implications for industries ranging from automotive to insurance. The findings underscore that while CAVs offer transformative benefits for safety and efficiency, proactive planning and strategic policy development are essential to capture these advantages and avoid negative outcomes.
Key finding
The study provides a multi-faceted framework for CAV integration, demonstrating that advanced technologies can significantly reduce crash costs and congestion while identifying critical policy and infrastructure updates required to realize these benefits.
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).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | rosap | — | — | 2 | 2026-05-23 |
| archive | success | — | — | — | 1 | 2026-05-23 |
| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | success | — | — | — | 1 | 2026-05-23 |
| promote | success | — | — | — | 1 | 2026-05-23 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 3 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 19 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
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.
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
- Methodological Resource: dataset resource
- Theoretical Contribution: conceptual framework