By David Nutt
Researchers say the future of transportation will be shaped by three “revolutions” – vehicle electrification, driverless cars and ride-sharing – that could result in fewer automobiles on the road, less fossil fuels extracted from the Earth and less pollution in the air. While the environmental gains may seem self-evident, the health benefits are difficult to quantify.
Now for the first time, a Cornell-led team has used transdisciplinary systems modeling to calculate those health benefits in the United States. By 2050, these innovations could potentially slash petroleum consumption by 50% and carbon dioxide emissions by 75% while simultaneously preventing 5,500 premature deaths, with an annual savings of $58 billion.
“There are all these important emerging trends in the development of transportation, and they are becoming a reality in the near future,” said Oliver Gao, the Howard Simpson Professor of civil and environmental engineering in the College of Engineering, who led the project.
“Have you ever thought about what all these revolutions mean for your health, for our climate, and for our environment, and for our energy systems?” Gao said. “These externalities don’t necessarily come directly in the mind of the general public, the travelers, or even the decision-makers.”
The group’s paper, “Shared Use of Electric Autonomous Vehicles: Air Quality and Health Impacts of Future Mobility in the United States,” published June 26 in Renewable and Sustainable Energy Reviews. The paper’s lead author is former postdoctoral researcher Shuai Pan.
“It is worthwhile to understand the effectiveness of these mitigation strategies, as deep de-carbonization is needed in the transportation sector,” Pan said.
Co-authors include Lewis M. Fulton from the University of California, Davis, and Yunsoo Choi and Jia Jung from the University of Houston. The research was supported by the U.S. Department of Transportation’s Center for Transportation, Environment and Community Health, and by Nanjing University of Information Science and Technology.
While previous studies have looked at certain facets of transportation innovation, such as the impact of electric vehicles on fuel usage and emissions, this is the first time anyone has employed a transdisciplinary systems approach that factored in human health and the associated economic benefits, according to Gao.
Gao’s systematics research group – which uses modeling to understand complex global challenges in engineering, business, societal well-being and sustainability – is uniquely positioned for such a task.
“A transportation engineer cannot address these questions,” Gao said. “Environmental science cannot address these questions. A health researcher cannot address these questions. However, this transdisciplinary group can do this.”
Pan, Fulton and Gao built an integrated assessment system that included a technical-economic mobility model, a chemical transport model and a health impact assessment tool. Then they projected the vehicle stocks, distance traveled, energy usage and carbon dioxide emissions in the continental U.S. through 2050, and quantified the impacts of changing emissions on concentrations of fine particulate matter in the atmosphere, as well as the ensuing health and economic benefits of populations in 10 major metropolitan areas.
Their simulations show that, depending on how widely the three “revolutions” are adopted, reductions in emissions from passenger travel could prevent between 2,300 and 8,100 premature deaths annually in the U.S. in 2050.
The largest number of prevented deaths coincided with large metropolitan areas, such as Los Angeles and Chicago. At the state level, California, Texas, New York, Ohio and Florida would see the largest decreases in premature mortality.
The associated economic benefits could range from $24 billion to $84 billion annually.
The study hangs on a number of assumptions and uncertainties. After all, driverless cars are not yet commercially available, and sales of electric vehicles lag far behind conventional gas guzzlers.
“Another key finding is that for carbon mitigations and health benefits, vehicle electrification is by far the most important piece, followed by shared mobility (ride-sharing) and then automation, ” Pan said. “The net energy impacts of self-driving vehicles are highly uncertain and automation alone may not dramatically affect energy use, emissions or vehicle-related pollution. ”
A complicating factor is that the efficiency improvement and projected cost reduction from automation could actually lead to increased travel and offset other gains.
“If we automate the vehicles, you might make the transportation system more efficient, but probably more people will travel longer distances,” Gao said. “So there is a balance, there is a trade-off.”
The study concludes that policymakers can help encourage the transition to electric vehicles and boost ride sharing, for example, by issuing tighter fuel economy standards, creating economic incentives for shared mobility and investing in charging infrastructure and technological developments.
A future of autonomous flying taxis
Of course, actually creating such transportation innovations is not possible without first determining their viability.
Another research project from Gao’s lab – published July 6 in Transportation Research Part A: Policy and Practice – explores the feasibility of an airport shuttle service that uses autonomous flying taxis as a means to mitigate urban congestion. The paper’s lead author is Emily Lewis ’20.
“While you are stuck in traffic from JFK [International Airport] to Manhattan, have you ever thought, oh, I wish I could be a bird, just to fly there. Actually, that dream is not too far away,” said Gao, who directs Cornell’s Center for Transportation, Environment and Community Health. “But how do you even architect a whole system, from the technology to market prediction and to operation? Would such an idea make economic sense at all?”
The study focuses on the concept of urban air mobility – essentially a transportation service for low-altitude airspace in metropolitan areas that features autonomous unmanned aerial vehicles.
Gao’s team – which included co-authors Jesse Ponnock ’20, Qamar Cherqaoui ’20, Scott Holmdahl ’20,Yus Johnson ’20 and Alfred Wong ’20 – focused on the three busiest airports in the U.S.: Atlanta, Los Angeles and Dallas.
They used a holistic, system-architecture analysis to identify each area’s key stakeholders and the goals that meet their needs, such as fleet management, infrastructure, traffic control, safety, user experience, financial viability and performance. The modeling also took into account the relationships between annual profit, mean time between safety incidents, upfront costs and the number of passengers shuttled per day.
“Because of its geographic, meteorological and also demand factors, Los Angeles turns out to be the best case for a pilot city,” Gao said.
The analysis identified wealthy commuters, long-distance commuters, business executives, event attendees, emergency transportation and vacationers as potential early adopters of an air mobility system.
What would such a system actually look like from a passenger’s perspective? It might not be too different from the ride-sharing services of today. The analysis recommended the system use FIFO (first in, first out) queuing and a smartphone interface for passengers, which may sound familiar to anyone who has ever hailed an Uber on their phone.
Also recommended: a hybrid energy source that incorporates electric energy for the autonomous vehicles.
But vehicles and apps are only part of it. For an air mobility system to become a reality, it would need the infrastructure to support it.
“This is not actually as mature as electrification or even automation,” Gao said. “This is even further away down the road. We are not comparing urban air mobility to other modes or arguing this is a better mode. We’re just saying that now, given the interest, first you need to be able to architect this. And then you will have a better sense about cost.”
Cornell Chronicle: ‘Transportation innovations could boost public health”
Prediction/Causality Tradeoffs and Data Size Issues in Transportation Modeling: The Example of Highway-Safety Analysis
May 21, 2021 at 4:40 p.m. ET
Associate Dean for Research, College of Engineering
Professor of Civil and Environmental Engineering
University of South Florida
Abstract: The analysis of transportation data is largely dominated by traditional statistical methods (standard regression-based approaches), advanced statistical methods (such as models that account for unobserved heterogeneity), and data-driven methods (machine learning, neural networks, and so on). In the analysis of highway safety data, these methods have been applied mostly using data from observed crashes, but this can create a problem in uncovering causality since individuals that are inherently riskier than the population as a whole may be over-represented in the data. In addition, when and where individuals choose to drive could affect data analyses that use real-time data since the population of observed drivers could change over time. This issue, the size of the data (which can often influence the analysis method), and the implementation target of the analysis imply that analysts must often tradeoff the predictive capability (dominated by data-driven methods) and the ability to uncover the underlying causal nature of crash-contributing factors (dominated by statistical and econometric methods). However, the selection of the data-analysis method is often made without full consideration of this tradeoff, even though there are potentially important implications for the development of safety countermeasures and policies. This talk provides a discussion of the issues involved in this tradeoff with regard to specific methodological alternatives, and presents researchers with a better understanding of the trade-offs often being inherently made in their analysis.
Bio: Fred Mannering is currently the Associate Dean for Research in the College of Engineering and a Professor of Civil and Environmental Engineering (with a courtesy appointment in Economics) at the University of South Florida. He received his undergraduate degree from the University of Saskatchewan, masters from Purdue University, and doctorate from the Massachusetts Institute of Technology. He was previously a professor at Penn State, A professor and Department Chair at the University of Washington, and School Head and Chaired professor at Purdue University. His research interests are in the application of econometric and statistical methods to the analysis of highway safety, transportation economics, vehicle demand, travel behavior and a variety of other engineering-related problems. He has published extensively with over 150 journal articles and two books: Principles of Highway Engineering and Traffic 001Analysis (now in its seventh edition) and Statistical and Econometric Methods for Transportation Data Analysis (now in its third edition). His body of work has been cited over 13,000 in Scopus, over 10,000 times in the Web of Science Core Collection, and over 25,000 times in Google Scholar. Dr. Mannering is currently Editor-in-Chief (and founding Editor) of the Elsevier Science journal Analytic Methods in Accident Research and previous Editor-in-Chief (2003-12) and current Distinguished Editorial Board Member of the Elsevier Science journal Transportation Research Part B – Methodological. He is also the Interim Executive Director of the Center for Urban Transportation Research (CUTR) at the University of South Florida and an Associate Director of the TOMNET University Transportation Center.
With the support of Yu Zhang’s (USF) Ph.D. student, Hualong Tang, their research titled “Design of an automated advanced air mobility flight planning system (AAFPS)” was selected to receive the Amazon Research Award (ARA).
Dr. Yu Zhang and Hualong Tang were among the 101 recipients of the 2020 Amazon Research Awards, who represent 59 universities in 13 countries. Each award is intended to support the work of one to two graduate students or postdoctoral students for one year, under the supervision of a faculty member. Proposals were reviewed for the quality of their scientific content, their creativity, and their potential to impact both the research community, and society more generally.
Deployable Decentralized Routing Strategies using Envy-Free Incentive Mechanisms for Connected and Autonomous Vehicle Environments Webinar
April 30, 2021 at 4:40 p.m. ET
Frederick R. Dickerson Chair and Professor,
School of Civil and Environmental Engineering,
H. Milton Stewart School of Industrial and Systems Engineering
Georgia Institute of Technology
Register for Webinar: https://ucdavis.zoom.us/webinar/register/WN_dVfkMJXTSsyL0dFyKpp3SQ
Routing strategies using dynamic traffic assignment have been proposed in the literature to optimize system performance. However, challenges have persisted in their deployability and effectiveness due to inherent strong assumptions on traveler behavior and availability of network-level real-time traffic information, and the high computational burden associated with computing network-wide flows in real-time. To address these gaps, this study proposes an incentive-based decentralized routing strategy to nudge the network performance closer to the system optimum in a traffic system with connected and autonomous vehicles (CAVs). The strategy consists of three stages. The first stage incorporates a local route switching dynamical system to approximate the system optimal route flow in a local area based on vehicles’ knowledge of local traffic information. This system is decentralized in the sense that it only updates the local route choices of vehicles in this area to circumvent the high computational burden associated with computing the flows on the entire network. The second stage optimizes the route for each CAV by considering individual heterogeneity in traveler preferences (e.g., the value of time) to maximize the utilities of all travelers in the local area. Constraints are also incorporated to ensure that these routes can achieve the approximated local system optimal flow of the first stage. The third stage leverages an expected envy-free incentive mechanism to ensure that travelers in the local area can accept the optimal routes determined in the second stage. They prove that the incentive mechanism is expected individual-rational and budget-balanced. The study analytically shows that the proposed incentive-based decentralized routing strategy can enhance network performance and user satisfaction in a connected and autonomous traffic environment.
Bio: Srinivas Peeta is the Frederick R. Dickerson Chair and Professor in the School of Civil and Environmental Engineering and the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology. He is also Principal Research Faculty at the Georgia Tech Research Institute. Previously, he was the Jack and Kay Hockema Professor at Purdue University and the Director of the NEXTRANS Center, formerly the U.S. Department of Transportation’s (USDOT’s) Federal Region 5 University Transportation Center. He was also the Associate Director of USDOT’s Center for Connected and Automated Transportation (CCAT). He received his B.Tech., M.S. and Ph.D. degrees from the Indian Institute of Technology (Madras), Caltech and The University of Texas at Austin, respectively. Dr. Peeta’s research interests are multidisciplinary, and broadly span transportation and infrastructure systems.