Understanding the Impact of Autonomous Vehicles on Long-Distance Passenger and Freight Travel in Texas: Final Report
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Summary
This study investigates the projected impacts of autonomous vehicles (AVs) and automated trucks (ATrucks) on long-distance passenger and freight travel in Texas and the United States. Motivated by the anticipated dominance of AVs in trips between 100 and 500 miles and ATrucks in freight movements exceeding 300 ton-miles, the research aims to quantify changes in mode choice, destination selection, and travel volumes. The project addresses the need to anticipate implications for network vehicle-miles traveled (VMT), trade flows, and congestion as automation technologies become commercially viable. The researchers employed a comprehensive modeling framework integrating multiple data sources, including a newly designed 2021 long-distance passenger AV survey, the 2016/17 National Household Travel Survey (NHTS), EPA Smart Location data, FHWA rJourney datasets, and the Freight Analysis Framework (FAF). For passenger travel, the team synthesized a 10% representative US population comprising 28.1 million individuals across 12.1 million households. They estimated a sequence of seven travel demand models—covering vehicle ownership, trip frequency, season, purpose, party size, destination choice, and mode choice—to generate disaggregated trip data. Freight impacts were analyzed using a four-step travel demand model with feedback loops for congestion, applied to Texas freight flows involving human-driven trucks, ATrucks, carload rail, and intermodal rail. Key findings indicate significant shifts in passenger behavior under a scenario where AVs carry a $3,500 technology cost premium in 2040. Total person-miles traveled (PMT) per capita for long-distance trips is estimated to rise by 35%, from 280 to 379 miles per month. Mode splits shift dramatically: private automobile usage drops from 64.10% to 31.67%, while AVs capture 23.54% and shared autonomous vehicles (SAVs) 18.24% of the market. Air travel share decreases from 5.49% to 3.53%. Survey results reveal that Texans are 40% more willing than other US residents to use AVs for long-distance trips if travel time increases by 0–50%, with safety and reliability cited as primary motivators. In freight modeling, if ATruck shipping costs drop to half that of human-driven trucks, the truck mode share is predicted to increase by 4.2% (from 57.0% to 61.2%), resulting in a 6.0% increase in ton-miles transported by truck. This shift is particularly pronounced for commodities like food, paper, and primary metals, where truck mode splits could increase by over 10%. The study concludes that the introduction of AVs and ATrucks will substantially increase long-distance travel volumes and alter modal competition, particularly reducing reliance on air travel and rail freight. The projected 35% increase in PMT and significant VMT growth highlight the potential for increased congestion and infrastructure demand. These findings provide critical insights for transportation planners and policymakers regarding the long-term effects of automation on travel patterns, emphasizing the need to prepare for expanded network usage and shifting freight logistics.
Key finding
The introduction of autonomous vehicles is projected to increase long-distance per capita person-miles traveled by 35%, while automated trucks are predicted to increase truck mode share by 4.2% and ton-miles by 6.0% if their shipping costs drop to half that of human-driven trucks.
Methodology
modeling
Sample size: 1004
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 |
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| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | — | — | — | 1 | 2026-06-01 |
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| promote | success | — | — | — | 1 | 2026-05-23 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 3 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 24 | 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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