Simulating a Shift to E-delivery: Impacts on VMT (Project F6)

  • Whalin, Robert R. (PI)
  • Hadi, Mohammed M. (PI)
  • Bhagat-conway, Matthew M. (PI)
  • Michalaka, Dimitra D. (PI)
  • Samandar, M M. (PI)
  • Rouphail, Nagui N. (CoPI)
  • Uddin, Nasim N. (PI)
  • Sisiopiku, Virginia V. (PI)
  • Tsai, Yichang (james) Y. (PI)
  • Mcdonald, Noreen N. (PI)
  • Watkins, Kari K. (PI)
  • Elefteriadou, Lily (PI)
  • Manjunatha, Pruthvi P. (CoPI)
  • Hunter, Michael M. (CoPI)
  • Guin, Angshuman A. (PI)
  • Wang, Feng F. (CoPI)
  • Du, Lili L. (PI)
  • Steiner, Ruth R. (PI)
  • Combs, Tabitha T. (CoPI)
  • Turochy, Rod R. (PI)
  • Yin, Yafeng Y. (PI)
  • Kirby, Jason J. (PI)
  • Motevalli, Vahid (PI)
  • Davis, William W. (PI)
  • Zhou, Huaguo Hugo H.H. (PI)
  • List, George G. (PI)
  • Monast, Kai K. (PI)
  • Sullivan, Andrew A. (PI)
  • Bardaka, Eleni E. (PI)
  • Zhao, Xilei X. (PI)
  • Cunningham, Chris C. (PI)
  • Tu, Shuang S. (PI)
  • Conway, Matthew M. (PI)
  • Cunningham, Christopher C. (PI)
  • Peeta, Srinivas (PI)
  • Du, Lili L. (CoPI)
  • Huntsinger, Leta L. (CoPI)
  • Mohebbi, Mehri M. (PI)
  • Jin, Xia X. (CoPI)
  • Lamondia, Jeffrey J. (CoPI)
  • Classen, Sherrilene S. (PI)
  • Mason, Justin J. (CoPI)
  • Laval, Jorge J. (PI)
  • Zhou, Huaguo H. (CoPI)
  • Noei, Shirin S. (CoPI)
  • Yan, Xiang X. (PI)
  • Hajbabaie, Ali A. (PI)
  • Alakshendra, Abhinav A. (PI)
  • Click, Steven S. (CoPI)
  • Martin, James J. (PI)
  • Murray, Eugene E. (CoPI)
  • Sherif, Muhammad M. (PI)

Project: Research project

Project Details


STRIDE project K5 is creating better estimates of shopping vehicle miles traveled (VMT) in the US, using detailed travel survey data. These estimates provide a valuable baseline against which to compare estimates of the transport impacts of e-shopping and delivery services. The proposed project represents a logical extension, using the information gathered in Project K5 to provide estimates of the effect of a shift to e-shopping on travel outcomes. The project has three components. First, a regression model will be estimated using the data from Project K5 to predict the marginal VMT of a shopping trip, based on attributes of the destination and the trip maker. Second, a predicting online and in-person shopping and the tradeoffs or complementarity between them will be constructed based on COVID Future survey data. Third, the lessons learned from these models will be integrated into a regional travel demand model to improve forecasting of shopping travel, especially as e-shopping becomes more prevalent. The research team anticipates that this project will result in more thorough consideration of shopping travel and e-shopping in infrastructure planning.
Effective start/end date12/2/169/30/23


  • U.S. Department of Transportation: $12,000.00
  • U.S. Department of Transportation: $12,000.00


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