Energy Savings through Integrated Personalized, Real-time Traveler Information and Incentive Scheme

Washington DC-Baltimore, Metropolitan Downtown Area, USA

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.

Energy Theme Mobility Theme Social Theme
  • Principal Investigators

    Yafeng Yin

  • Other Contributors

    University of Maryland

Funding Source

U.S. Department of Energy

About the Project

This project aims to reduce energy use of vehicular travels by incentivizing individual travelers to adjust travel choices and driving behaviors to better manage and operate road networks.

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.

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Principal Investigators