Project : transit demand and routing after autonomous vehicle availability.

Boyles, Stephen D.; Levin, Michael W.; Patel, Rahul; Duell, Melissa; Waller, S. Travis · 2015 · ROSA P / University of Texas at Austin. Data-Supported Transportation Operations & Planning Center (D-STOP)

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Summary

This paper investigates the impact of autonomous vehicles (AVs) on traffic operations, specifically focusing on how AV-specific behaviors—reservation-based intersection control and reduced reaction times—affect travel times and congestion in large-scale networks. While previous studies relied on micro-simulations limited to small networks, this research utilizes dynamic traffic assignment (DTA) models to analyze arterial, freeway, and downtown networks among the 100 most congested roads in Texas. The study aims to quantify the benefits of AV technologies, such as increased road capacity from shorter following headways and improved intersection efficiency, while accounting for selfish route choice behaviors. The methodology employs two primary models: a conflict-region simplification of reservation-based intersection control, which aggregates space-time tiles into capacity constraints to make the protocol tractable for DTA, and a multiclass cell transmission model (CTM) that predicts capacity and backward wave speed based on vehicle class proportions and reaction times. The CTM assumes AVs have a reaction time of 0.5 seconds compared to 1.0 second for human drivers, resulting in higher capacity and faster shockwave propagation. The researchers simulated various demand scenarios (50% to 100%) and AV penetration rates across arterial corridors (Lamar & 38th Street, Congress Avenue), freeway networks (I-35, US-290, Mopac), and the downtown Austin grid. Results indicate that reduced reaction times consistently improved travel times across all network types, with benefits increasing as demand and congestion rose. For instance, on the I-35 freeway at 100% demand, AVs reduced travel time by nearly 72%. However, the efficacy of reservation-based intersection control was mixed. Reservations outperformed traditional traffic signals on arterial networks with non-progressive signals (e.g., Congress Avenue) and in the downtown Austin grid, where they contributed to a 78% total travel time reduction when combined with reduced reaction times. Conversely, reservations performed worse than signals on arterials with closely spaced local road intersections (e.g., Lamar & 38th Street) due to queue spillback caused by first-come-first-serve priority allocations. On freeways, reservations were ineffective at replacing merges and diverges, worsening travel times on I-35 and Mopac, though they provided modest improvements on US-290 where signals controlled access. The study concludes that while AV-induced capacity increases offer significant operational benefits, the replacement of traffic signals with FCFS reservations is not universally advantageous. Signals remain superior in high-demand scenarios involving closely spaced intersections or where merge/diverge geometry exists. The findings suggest that future AV integration should prioritize optimized reservation priority policies rather than simple FCFS approaches, particularly in complex urban grids. This work provides a scalable framework for assessing AV impacts on metropolitan planning, highlighting that network topology and intersection geometry critically determine the success of AV traffic management strategies.

Key finding

The combination of reservation-based intersection control and reduced following headways resulted in a 78% reduction in travel time in the downtown Austin network, while reduced reaction times alone improved travel times across all tested arterial and freeway scenarios.

Methodology

modeling

Provenance

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

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• traffic density

Information type

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• Theoretical Contribution: computational model