The project aims to reduce energy use of vehicular travels by incentivizing individual travelers to adjust travel choices and driving behaviors. Implemented through smartphone apps, a comprehensive loyalty program is structured with real-time incentives to influence travelers daily travel choices to collectively minimize energy consumption in a multimodal transportation system.
Washington, D.C. has the second highest percentage of public transit commuters in the United States.
Of the energy released during combustion of fuel in a gasoline-powered vehicle is lost in its engine.
A gallon of gasoline produces 20 pounds of carbon dioxide (CO2) when burned.
The project provides a comprehensive loyalty program, implemented through smartphone apps, that employs long-term, pre-trip, and real-time incentives to influence travelers daily travel choices. The proposed system can guide travelers, through personalized incentives, to individually adjust their mode, departure time, route, and driving style choices, and to collectively minimize energy consumption in a multimodal transportation system. The scheme encompasses a system model (SM) consisting of integrated person-level travel behavior, dynamic traffic, and energy use simulators, and a control optimizer (CO) for optimal incentive allocation. This incentive structure features simple rules for earning points, produces travel benefits to users, creates gaming-type activities and membership levels for long-term loyalty, balances monetary and non-monetary incentives. Through credible behavior research on over one thousand D.C.-Baltimore residents, a personalized and multidimensional incentive structure was designed through a principal-agent theoretical model. It was then linked to a computationally efficient and robust control optimizer for system-level incentive allocation.
The research team demonstrated the system in a large-scale, real-world case study in the Washington, D.C.-Baltimore metropolitan region to highlight its capabilities and applicability to address real-world, energy-efficient mobility challenges. This innovative solution can help existing incentive systems operate in a much more effective and efficient manner. The case study demonstrates that with 7.5% of agent adoption, the energy savings from a scheme prototype can be as high as 8.7%, given the particular scenario analyzed by the numerical example. Personalized and multi-dimensional behavioral changes result from the incentivization. For example, travelers can be incentivized to avoid peak hour departures instead of modal shifts to transit, if their trip origin-destination is not well-served by a transit service.
The technology can be applied to achieve other control objectives. At some point in the not too distant future, traffic streams will be mixed with human-driven vehicles without connectivity (regular vehicles), human-driven vehicles with connectivity (connected vehicles), and vehicles with various levels of automation. The research team believes that leveraging connected vehicles in traffic streams will have the effect to better manage and operate road networks. More specifically, participatory traffic control is implemented, where a community of connected vehicles will be incentivized to opt in for traffic control and management. An online, mobile platform will target specific participating commuters and incentivize them to a) behave as “travel demand distributors” to better distribute the commuting demand across time periods and transportation facilities; and b) function as “traffic stream regulators” to regulate traffic stream to prevent or delay the activation of recurrent bottlenecks. The working hypothesis is that, by incentivizing the behavioral changes of a small number of targeted participants, the platform can influence a larger number of untargeted commuters’ travel decisions to improve the overall system performance.
Fig. 1. The Framework of the Integrated Personalized, Real-time Traveler Information and Incentive Scheme.
Fig. 2. Energy Savings (in Percentile) from Geographical Locations (Analyzed Based on Trip Origins)
Studying rideshare options like Lyft and Uber, with special focus on individuals with limited transportation choices.
Collecting travel data to help Benton Harbor improve travel options for residents, with the goal of increased employment participation and retention.
Facilitating an on-demand, seamless, and efficient mobility service for the Benton Harbor community, especially among low-mobility families.
Rethinking how transit infrastructure can expand access to food, health, learning, and mobility services by creating multimodal hubs.
A major source of bridge deterioration requiring constant maintenance is mechanical expansion joints installed between adjacent simple span bridge decks.
Mapping detailed geographies of digital access and exclusion across Detroit’s neighborhoods.
Using wearable-based technology to help seniors stay mobile and age in place, while avoiding exposure to falls and environmental risks or hazards.
The first in a series of health clinic prototypes that bring technology-enabled chronic health care monitoring to remote, underserved global populations.
Using remote sensing and security camera data to better understand how people are using the Detroit RiverFront Conservancy public spaces.
A grassroots train-the-trainer program on how to install, operate and maintain faucet-mounted point-of-use filters to protect for lead in drinking water.
The Sensors in a Shoebox project focuses on empowering Detroit youth as agents of change for their city.
Dr. Yafeng Yin joined The Department of Civil and Environmental Engineering in January 2017 coming to U-M from the University of Florida. Dr. Yin is a member of our Next Generation Transportation Systems and Intelligent Systems groups. Dr. Yin is an internationally recognized expert on transportation systems analysis and modeling, and has published approximately 100 refereed papers in leading academic journals. He is the Editor-in-Chief of Transportation Research Part C: Emerging Technologies, one of the leading academic journals in the transportation domain.
Dr. Yin received his Ph.D. from the University of Tokyo in 2002, his master’s and bachelor’s degrees from Tsinghua University, Beijing, China in 1996 and 1994, respectively. Prior to his current appointment at the University of Michigan, he was a faculty member at University of Florida between 2005 and 2016. He worked as a postdoctoral researcher and then assistant research engineer at University of California at Berkeley between 2002 and 2005. Between 1996 and 1999, he was a lecturer at Tsinghua University. Dr. Yin has received recognition from different institutions.
He was one of the five recipients of the 2012 Doctoral Mentoring Award from University of Florida in recognition of his outstanding graduate student advising and mentoring. One of his papers won the 2016 Stella Dafermos Best Paper Award and the Ryuichi Kitamura Paper Award from Transportation Research Board of the National Academies of Sciences, Engineering, and Medicine. He was also recently elected to serve on the prestigious International Advisory Committee of the International Symposium of Transportation and Traffic Theory (ISTTT).
Dr. Yin’s research interests include the analysis, modeling, design and optimization of transportation systems toward achieving sustainability and economic efficiency. His ongoing research involves investigating the implications of emerging technologies on mobility systems. “I closely follow the development of new technologies, such as smart mobile devices and apps, sensor technologies, electric vehicles, drones, and connected and automated vehicles,” says Dr. Yin. “I examine how they could potentially affect both the demand and supply sides of transportation systems, and then explore how to leverage these new technologies to better design, operate and manage transportation systems and improve the efficiency, reliability, safety, and diversity of the transportation services.” Beyond transportation, Dr. Yin also studies the interdependency of urban infrastructure systems, such as transportation, power and communications networks.