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Title: Innovative scenarios for modeling intra-city freight delivery

Abstract

There has been a shift in the focus of freight modeling to the short-haul (or last-mile) due to an increase in online shopping. This study considers innovative freight delivery modes and multi-modal shifts, especially for the last-mile portion of intra-city freight delivery. For this study, GPS data were obtained from a truck fleet from a major parcel delivery company's depot near Columbus, Ohio and used to develop a freight delivery demand estimation model. Freight delivery tours were modeled in TransCAD and used to develop scenarios to incorporate various modal shifts to compare energy usage in kilowatt-hour estimates. Innovative modes of freight delivery were considered for the scenarios and were compared to a class six truck: electric class six trucks, electric delivery vans, parcel delivery lockers, drones, and electric passenger vehicles. Initial findings suggest that electric trucks reduce energy usage when the majority of miles traveled are in the long-haul, or stem portion of the route. Parcel delivery lockers reduced energy usage in suburban areas, especially those with large neighborhoods with cul-de-sacs. The findings from this study were intended to provide decision makers, both in government and industry, with information to consider when determining suitable alternatives for energy-efficient intra-city freight transport.

Authors:
Publication Date:
Research Org.:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1772114
Alternate Identifier(s):
OSTI ID: 1559660
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Published Article
Journal Name:
Transportation Research Interdisciplinary Perspectives
Additional Journal Information:
Journal Name: Transportation Research Interdisciplinary Perspectives Journal Volume: 3 Journal Issue: C; Journal ID: ISSN 2590-1982
Publisher:
Elsevier
Country of Publication:
United Kingdom
Language:
English
Subject:
33 ADVANCED PROPULSION SYSTEMS

Citation Formats

Moore, Amy M. Innovative scenarios for modeling intra-city freight delivery. United Kingdom: N. p., 2019. Web. doi:10.1016/j.trip.2019.100024.
Moore, Amy M. Innovative scenarios for modeling intra-city freight delivery. United Kingdom. https://doi.org/10.1016/j.trip.2019.100024
Moore, Amy M. Sun . "Innovative scenarios for modeling intra-city freight delivery". United Kingdom. https://doi.org/10.1016/j.trip.2019.100024.
@article{osti_1772114,
title = {Innovative scenarios for modeling intra-city freight delivery},
author = {Moore, Amy M.},
abstractNote = {There has been a shift in the focus of freight modeling to the short-haul (or last-mile) due to an increase in online shopping. This study considers innovative freight delivery modes and multi-modal shifts, especially for the last-mile portion of intra-city freight delivery. For this study, GPS data were obtained from a truck fleet from a major parcel delivery company's depot near Columbus, Ohio and used to develop a freight delivery demand estimation model. Freight delivery tours were modeled in TransCAD and used to develop scenarios to incorporate various modal shifts to compare energy usage in kilowatt-hour estimates. Innovative modes of freight delivery were considered for the scenarios and were compared to a class six truck: electric class six trucks, electric delivery vans, parcel delivery lockers, drones, and electric passenger vehicles. Initial findings suggest that electric trucks reduce energy usage when the majority of miles traveled are in the long-haul, or stem portion of the route. Parcel delivery lockers reduced energy usage in suburban areas, especially those with large neighborhoods with cul-de-sacs. The findings from this study were intended to provide decision makers, both in government and industry, with information to consider when determining suitable alternatives for energy-efficient intra-city freight transport.},
doi = {10.1016/j.trip.2019.100024},
journal = {Transportation Research Interdisciplinary Perspectives},
number = C,
volume = 3,
place = {United Kingdom},
year = {Sun Dec 01 00:00:00 EST 2019},
month = {Sun Dec 01 00:00:00 EST 2019}
}

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