Methodology

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.

*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.