Travel modeling in an era of connected and automated transportation systems: an investigation in the Dallas-Fort Worth area.
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Summary
This technical report, produced by the Data-Supported Transportation Operations & Planning Center (D-STOP) at The University of Texas at Austin, addresses the integration of Connected and Autonomous Vehicles (CAVs) into long-range transportation planning for the Dallas-Fort Worth area. Commissioned by the North Central Texas Council of Governments (NCTCOG), the study aims to analyze the current state of CAV technology, project adoption timelines, and develop planning scenarios to account for the societal and economic impacts of these technologies. The research is motivated by the need for metropolitan planning organizations to anticipate mobility changes over multi-decade horizons before widespread implementation occurs. The study is structured into three parts. Part I examines the technological status of autonomous vehicles (AVs) and connected vehicles (CVs), detailing sensor suites (LIDAR, GPS, cameras, radar), software systems, and communication protocols like Dedicated Short-Range Communication (DSRC). It contrasts private automaker development of AVs with federal government-led CV initiatives. Part II analyzes adoption patterns, reviewing predictions from private consultants and academics while estimating CAV uptake based on historical adoption rates of similar technologies. Part III outlines a methodology for creating planning scenarios, considering factors such as travel demand, behavior, and system performance. The authors propose 112 potential planning scenarios derived from various assumptions regarding automation levels, connectivity penetration, and behavioral shifts. Key findings indicate that discrete technologies for both AVs and CVs are nearing market readiness. The report projects that Level 4 autonomous vehicles, which can navigate without human intervention in most conditions, could be commercially available by 2020, driven by decreasing costs of components like LIDAR and increased processing power. Conversely, Level 5 full autonomy remains further off. The study anticipates decades of mixed traffic involving human-driven, semi-autonomous, and fully autonomous vehicles. A significant finding is the potential for ridesharing services to disrupt traditional AV adoption models, as automakers increasingly integrate mobility services. Furthermore, the implementation of mature Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) systems could prevent over 80% of light vehicle crashes. The significance of this work lies in its provision of a structured framework for regional planners to assess CAV impacts. The report concludes that traditional modeling tools may be insufficient to capture the full range of CAV effects, suggesting a need for advanced tools like activity-based models and dynamic traffic assignment. By establishing 112 distinct scenarios, the study enables planners to evaluate a wide spectrum of potential futures, accounting for uncertainties in technology timelines, policy mandates, and market disruptors. This approach supports more resilient long-range planning in the face of rapid technological change.
Key finding
The discrete technologies enabling connected and autonomous vehicles are nearing market readiness, with Level 4 autonomous vehicles projected for commercial availability by 2020, while connected vehicle mandates could avoid over 80 percent of light vehicle crashes upon implementation.
Methodology
review
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.
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- Theoretical Contribution: computational model, conceptual framework