Simulation Study of Texas' Roadways Estimates that CAVs Can Save More Than 2,400 Lives Each Year by the Time 90 Percent Market Penetration is Reached.
Texas Study Estimates Reductions in Travel Times, Congestion Costs, Crash Fatalities and Annual Per Person Productivity Gains Resulting from the Availability of Connected and Autonomous Vehicles.
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United States

An Assessment of Autonomous Vehicles: Traffic Impacts and Infrastructure Needs-Final Report

Summary Information

A research team based out of the University of Texas-Austin's Center for Transportation Research sought to provide a systematic synthesis of contemporary smart driving technologies, such as connected and autonomous vehicles (CAVs), including their technological maturity and their potential crash, congestion and other impacts in Texas at various levels of market penetration. The project designed and disseminated a Texas-wide survey with 1,364 completed responses to analyze and gain an understanding of the U.S. general public’s perception towards and willingness to adopt such technologies. Additionally, the research team studied the effects of rising CAV ownership on transit ridership, CAV repositioning trips, and total personal-vehicle demand using the traditional four-step planning process for static traffic assignment, and how dynamic traffic flow models can represent capacity increases from using CAVs and their effects on congestion and travel times. The study also analyzed how shared (and connected) autonomous vehicles (SAVs) may perform relative to privately held CAVs. To assess potential benefits to the transportation system and its users stemming from CAVs, a benefit-cost analysis was also performed focusing on congestion and crashes assuming CAV elasticities with respect to studied metrics that are consistent with the literature.


A static four-step model was created using a generalized-cost function of travel time, monetary costs (like parking charges and tolls), and fuel costs. Three mode choices of driving and parking (using an autonomous vehicle (AV) or human-driven vehicle (HV)), traveling in a repositioning AV, and transit were considered in the four-step model that used downtown the Austin, TX network, during the two-hour period of morning rush hour (2-hour AM peak). The second part of the research team’s travel demand modeling work examined the use of SAVs with dynamic traffic assignment (DTA) using an event-based framework for implementing SAV behavior in existing traffic simulation models. Next, the team used flow and travel demand link-based mesoscopic models to simulate and model CAVs and to find their effects on congestion and travel times compared to a control set of HVs. Reservation-based control for CAVs (with the aim of reserving a safe path through the intersection without much delay) was simulated to explore the possibilities of traditional signal substitutes once AV market penetration reaches 100 percent. Simulated networks included two arterial networks, three freeway networks, and one downtown city network in Austin, TX. In most simulations, differences between CAVs and HVs were highlighted by assuming a perception-reaction time of 1 second for HVs and 0.5 seconds for CAVs. The simulated networks were assumed to have zero pedestrians and cyclists, along the routes and at intersections. Finally, benefit-cost estimates were obtained based on assumptions consistent with those found in the literature, such as the congestion costs and crash savings corresponding to different market penetration percentages of CAVs.


Key Findings

  • Reservation-based control of CAVs used instead of traffic signals was found to be highly effective in the downtown network — more effective than in freeway or arterial networks, cutting travel times by an additional 55 percent at 100 percent demand. When combined with reduced reaction times, the total reduction in travel time was 78 percent. Reducing the reaction time to 0.5 seconds for CAVs nearly doubled road and intersection capacity.
  • Average willingness to pay (WTP) (of the respondents with a non- zero WTP) to add connectivity, and advanced automation technologies (Level 3 and Level 4) upgrades to their vehicles (new or existing) are $110, $5,551, and $14,589, respectively. More than half of respondents are not willing to pay anything to add advanced automation technologies (Level 3 and Level 4) to their current vehicles.
  • Ride-sharing reduces congestion and maximizes the utilization of each SAV because travelers accumulate as they wait for one of the few SAVs to arrive for pick-up. A fleet of 4000 SAVs serving a downtown area corresponds to a 93.6 percent reduction in the number of vehicles; each SAV services an average of 15.7 travelers. 
  • CAVs could potentially save around 185 lives per year on Texas roads, even at the 10 percent market penetration level. More than 2,400 lives could be saved each year on Texas roadways by the time 90 percent market penetration is reached, with over $14 billion in economic savings, or more than $62 billion in comprehensive crash costs, a 75 percent total reduction in comprehensive crash costs. Comprehensive crash costs include external measures such as quality-adjusted life years and willingness-to-pay measures for avoiding crashes.


Table 1. Summary of Anticipated CAV Benefits across Texas
CAV Market Penetration 10% 50% 90%
Congestion reduction ($/Vehicle/Year) $318 $159 $233
Economic crash savings ($/Vehicle/Year) $454 $601 $689
Comprehensive crash savings ($/Vehicle/Year) $1,943 $2,565 $2,941
Productivity and leisure ($/Vehicle/Year) $1,357 $1,357 $1,357
Sum of benefits ($/Vehicle/Year) $3,618 $4,081 $4,535
Net Present Values (using comprehensive crash cost savings and over the 11.4-year life of the CAV) ($/Vehicle) $13,960 $22,024 $27,000

*Price for automation and connectivity capabilities is estimated to be $10,000, $5,000, and $3,000 for 10, 50 and 90 percent market penetration and is subtracted from the Net Present Values of the benefits.

An Assessment of Autonomous Vehicles: Traffic Impacts and Infrastructure Needs-Final Report

An Assessment of Autonomous Vehicles: Traffic Impacts and Infrastructure Needs-Final Report
Publication Sort Date
Kockelman, Kara; Boyles, Stephen; Stone, Peter; Fagnant, Dan; Patel, Rahul; Levin, Michael W.; Sharon, Guni; Simoni, Michele; Albert, Michael; Fritz, Hagen; Hutchinson, Rebecca; Bansal, Prateek; Domnenko, Gleb; Bujanovic, Pavle; Kim, Bumsik; Pourrahmani, Elham; Agrawal, Sudesh; Li, Tianxin; Hanna, Josiah; Nichols, Aqshems; Li, Jia
Prepared by the Center for Transportation Research at the University of Texas at Austin for TDOT
Other Reference Number
Report No. FHWA/TX-17/0-6847-1