TY - JOUR
T1 - An integrated and personalized traveler information and incentive scheme for energy efficient mobility systems
AU - Xiong, Chenfeng
AU - Shahabi, Mehrdad
AU - Zhao, Jun
AU - Yin, Yafeng
AU - Zhou, Xuesong
AU - Zhang, Lei
N1 - Funding Information:
This research is financially supported by Maryland State Highway Administration (MD-SHA), and the Department of Energy ARPA-E TRANSNET Program. The opinions in this paper do not necessarily reflect the official views of MD-SHA, U.S. DOE, or ARPA-E. They assume no liability for the content or use of this paper. The authors are solely responsible for all statements in this paper.
Publisher Copyright:
© 2019 The Authors. Published by Elsevier B.V.
PY - 2018
Y1 - 2018
N2 - Recently, the employment of different types of incentives in transportation systems to form advanced transportation congestion management solutions has garnered significant attention. Instead of using presumed or fixed-amount incentives, this paper develops an integrated and personalized traveler information and incentive scheme to incentivize toward a more energy-efficient travel and mobility decisions. We have developed a behavior research and empirical modeling system to quantify the personalized monetary incentives. Then, it is integrated with a control optimizer for optimized incentive allocation. This scheme innovatively integrates behavioral modeling and optimization for travel incentive design. Through a demonstrative case study for a large-scale transportation system in the Washington D.C. and Baltimore regions, the capability of the proposed scheme is highlighted with significant system-level energy savings, reasonable insights on individual travel behavior responses, as well as superior computational efficiency.
AB - Recently, the employment of different types of incentives in transportation systems to form advanced transportation congestion management solutions has garnered significant attention. Instead of using presumed or fixed-amount incentives, this paper develops an integrated and personalized traveler information and incentive scheme to incentivize toward a more energy-efficient travel and mobility decisions. We have developed a behavior research and empirical modeling system to quantify the personalized monetary incentives. Then, it is integrated with a control optimizer for optimized incentive allocation. This scheme innovatively integrates behavioral modeling and optimization for travel incentive design. Through a demonstrative case study for a large-scale transportation system in the Washington D.C. and Baltimore regions, the capability of the proposed scheme is highlighted with significant system-level energy savings, reasonable insights on individual travel behavior responses, as well as superior computational efficiency.
KW - Control optimizer
KW - Incentives
KW - Monetary incentives
KW - System model
KW - Traveler information
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U2 - 10.1016/j.trpro.2019.05.010
DO - 10.1016/j.trpro.2019.05.010
M3 - Conference article
AN - SCOPUS:85074926880
SN - 2352-1457
VL - 38
SP - 160
EP - 179
JO - Transportation Research Procedia
JF - Transportation Research Procedia
T2 - 23rd International Symposium on Transportation and Traffic Theory, ISTTT 2019
Y2 - 24 July 2018 through 26 July 2018
ER -