As of October 1, 2016, the SWUTC concluded its 28 years of operation and is no longer an active center of the Texas A&M Transportation Institute. The archived SWUTC website remains available here.


SWUTC Research Project Description

Anticipating Long-Term Energy and GHG Emission Impacts of Autonomous Vehicles

University: University of Texas at Austin

Principal Investigator:
Kara Kockelman
Department of Civil and Environmental Engineering
(512) 471-0210

Project Monitor:
Steven Dellenback
Intelligent Systems Department, and Automation & Data Systems Division
Southwest Research Institute

Funding Source: USDOT and State of Texas General Revenue Funds

Total Project Cost: $76,200

Project Number: 600451-00081

Date Started: 1/1/13

Estimated Completion Date: 12/31/13

Project Summary

Project Abstract:
Autonomous vehicles (AVs) have arrived.  Google has logged over 300,000 miles on California public roadways with both Nevada and California passing AV-enabling legislation.  Fully autonomous vehicles are projected to be commercially available within 5 to 10 years, and this work seeks to anticipate the long-term impacts that AVs will have on energy usage and greenhouse gas (GHG) emissions.  This work will identify factors relating to AV adoption by US households that will allow the project team to define moderate and transformative scenarios for future years.  These scenarios will be tested using behavioral and operational models to estimate impacts to energy use and GHG emissions.  Results will suggest potential economic, environmental, and energy-security benefits that may be realized by pursuing policies that speed AV adoption and facilitate deeper market penetration.

Project Objectives:
This work will seek to anticipate and quantify the long-term energy use and GHG emissions impacts that AVs will bring.  In particular, this work looks to assess potential market penetration outcome scenarios over time, develop and apply behavioral and operational models in order to estimate AV travel behavior and traffic impacts, and apply emissions and energy use models to quantitatively estimate fuel consumption and GHG changes across multiple scenarios.  This work will enable policymakers to better understand how AVs will likely impact energy use and GHG emissions.

Task Descriptions:
Task 1: Analyze existing AV literature and data sets
The project team will examine existing literature regarding potential future AV market penetration, anticipated AV traffic impacts, and possible travel behavior impacts.  This work will also involve the examination and analysis of existing data sets, some of which may not be directly related to AVs.  For example, market penetration rates and adoption curves for other technologies, and existing travel patterns within one or more urban areas will be useful for anticipating future changes with the introduction and proliferation of AVs.  With conclusion of this task, most (if not all) parameters should be assembled that will be required in order to complete the remaining tasks.

Task 2: Define a variety of moderate and transformative future year scenarios
Using the information gathered in Task 1, the project team will define several scenarios that reflect potential AV market penetration and adoption outcomes.  These scenarios will reflect different start dates, adoption curves, traveler behavioral impacts, and technology implementation details.  For example, Honda estimates that AVs may be able to reduce congestion and realize fuel efficiency gains on freeways by avoiding needlessly excessive braking and acceleration (Atiyeh, 2012).  However, when linked to a cloud-based traffic monitoring system, speeds increase by 16% and additional fuel efficiency gains of 5% are realized.  When such a system would be developed and deployed remains a speculation, and these scenarios would assess multiple such implementation details.  Similarly, these scenarios would define parameters and details such as ranges of induced travel from those previously unable to drive and the proportion of AVs that may be used as shared vehicles, operating similar to a driverless taxi-service.

Task 3: Develop and apply behavioral and operational models to estimate impacts to traffic and travel patterns
The goal of Task 3 will be to use the broad traffic and behavioral changes identified in Task 1 in order to develop and apply behavioral and operation models on the scenarios defined in Task 2.  This task will be useful to understand the fundamental travel behavior shifts that AVs may generate.  This task will seek to understand how congestion, travel speeds, and drive cycles will be impacted.  Also assessed in this task will be estimated changes in total vehicle miles traveled (VMT), and simulated travel behavior changes.  These types simulation may include investigations like how many privately-owned vehicles a single shared AV would likely replace, and what proportions of autonomously driven VMT is likely to be driverless (either from shared AVs traveling to pick up their next passenger, or from privately-owned AVs traveling unoccupied between passenger drop-off and parking destinations).

Task 4: Apply emissions and energy-use models to estimate quantitative GHG and energy-use impacts
Once Task 3 establishes a framework for understanding anticipated changes in overall VMT, travel speeds, drive cycles, and travel patterns and behaviors, the project team will apply emissions and energy use models in order to quantitatively estimate changes in GHG emissions and overall energy use.  This task will involve the application of EPA’s MOVES modeling software under the conditions established in prior tasks.  Estimated changes Life-cycle GHG emissions and energy usage will also be assessed under this task.  This component will also involve estimating changes in the total number of vehicles that need to be manufactured (due to fewer cars totaled in crashes and single shared AVs replacing multiple privately-owned vehicles).  Examples of this type of work include Kockelman et al.’s (2008) Special Report 298: Effects of Land Development Patterns on Motorized Travel, Energy and CO2 Emissions, and Tirumalachetty and Kockelman’s (2010) publication Forecasting Greenhouse Gas Emissions from Urban Regions:  Microsimulation of Land Use and Transport Patterns in Austin, Texas.

Task 5: Report Writing and Results Dissemination
Results from this work will be produced in the form of reports and research papers. These will include details of data assembly and analysis, and life-cycle estimates of energy and other impacts/outcomes of a wide variety of development settings.  Key results relating to AV adoption trends, energy and emissions savings, safety benefits, and other AV-related    implications will be highlighted for planners, designers, policymakers, other stakeholders, and the public at large.

The results of this work will be disseminated to the Texas Department of Transportation, the Texas Commission on Air Quality (TCEQ), and the Texas State Legislature’s Standing Committee on Transportation.  Some of these dissemination efforts may be conducted in concert with other proposed AV work for TxDOT (if such proposals are selected).

Implementation of Research Outcomes:
This work pursued two major lines of investigation. The first conducted an assessment of the potential implications of fully autonomous vehicles, at varying levels of market penetration, in terms of safety, mobility, parking, social and travel behavioral changes, and economic impact. Additionally, it examined the potential barriers to a successful rollout, and recommended policy actions to be undertaken at the national level.

The second line of investigation explored in detail the concept of a shared fully autonomous vehicle (SAV), which would essentially act as a demand-responsive transit vehicle. This new transportation mode was explored using micro-simulation of thousands of SAVs and tens or hundreds of thousands of daily travelers who could use them. Additionally, dynamic ride sharing was also explored in this investigation, where two or more travelers with similar (though not necessarily identical) origins, destinations and departure times could share an SAV ride at the same time.

Products developed by this research:

Paper 1 Preparing a Nation for Autonomous Vehicles: Implications, Barriers and Policy Recommendations, Daniel Fagnant and Kara Kockelman, published by the Eno Center for Transportation, under review for publication in Transportation Research Part A, chosen as the Annual William P. Eno Research Paper and presented to the U.S. House Subcommittee on Highways and Transit.

Paper 2 Environmental Implications for Autonomous Shared Vehicles Using Agent-Based Model Scenarios, Daniel Fagnant, chosen as the winner of the ITS America Student Essay competition, published by Transportation Research Part C.

Presentations:  Papers 1 and 2 have been presented three times each independently, and another 6 times jointly, at conferences, at tier-one research universities, and to major corporations (e.g., Google, State Farm).

Presentations A Convergence in Shared Mobility: Demand-Responsive Fully Automated Vehicles, for Car-Sharing and Ride-Sharing Across Austin, Texas, presented at the 2014 Transportation Research Board Conference on Innovations in Travel Modeling, Baltimore, MD, April 27-30, 2014 and at the 2014 Automated Vehicles Symposium, San Francisco, CA, July 15-17, 2014.

Models Developed – This work resulted in the creation of two substantive pieces of computer code, approximately 6000 lines each, in length.  This code simulates how shared fully automated vehicles can serve a population of travelers, similar what Google and other autonomous vehicle technology providers are hoping to introduce within the next five to six years. The code is flexible and may be adapted to fit the needs of any city or region, for deployment evaluation. The methodologies for vehicle relocation strategies, dynamic ride-sharing and other SAV operations are described in the latter two papers noted above, and have been widely shared through both presentation and publication.

Impacts/Benefits of Implementation:
This research simulates a fleet of shared automated vehicles (SAVs) operating within the city of Austin, using Austin’s transportation network and travel demand flows. This model developed incorporates dynamic ride-sharing (DRS), allowing two or more travelers with similar origins, destinations and departure times to share a ride.

Model results indicate that each SAV could replace around 10 conventionally-owned household vehicles while serving over 56,000 person-trips. SAVs’ ability to relocate while unoccupied between serving one traveler and the next may cause an increase of 4-8% more travel; however, DRS can result in reduced overall vehicle miles traveled (VMT), given enough SAV-using travelers willing to ride-share. SAVs should produce favorable emissions outcomes, with an estimated 16% less energy use and 48% lower volatile organic compound (VOC) emissions, per person-trip formerly served by a household vehicle.

This work should inform companies such as Google, Induct, Robosoft and other manufacturers who are looking to deploy low-speed SAVs in the coming years. This work envisions an eventual scaling up to full-speed SAVs at much higher market share, and should inform these and other automated vehicle (AV) technology providers regarding their long-term potential. This work has also been presented to representatives of other automotive manufacturers who are entering the AV space, and one or more of them may become interested in exploring this new mode as well.  Finally, this work has been presented to public agency staff, and will be a featured presentation to MPO planners in July at the 2014 Automated Vehicles Symposium. The results of this work should help inform such persons attempting to plan and prepare for SAV’s pending arrival on U.S. city streets.

This work has been reported on by The Atlantic Cities, Bloomberg News, The Washington Post, CBS News, and the AP, among other national media.  In addition, it has been presented at conferences, at press releases, and directly to persons in the U.S. House of representatives, FHWA, FTA, and numerous persons employed in AV-related technology, automotive and insurance industries.  Such efforts have created much discussion and excitement among the public, regarding both the great potential and the potential pitfalls that SAVs (and AVs in general) present. Assuming that SAVs are eventually deployed, this research has demonstrated how they should create substantial environmental benefits, while also reducing parking requirements for cities and ultimately changing the built environment.

Web Links:
Final Technical Report