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Emission estimation of on-demand meal delivery services using a macroscopic simulation

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journal contribution
posted on 2023-02-01, 09:55 authored by Maren Schnieder, Christopher Hinde, Andrew WestAndrew West
While macroscopic simulations of passenger vehicle traffic within cities are now common practice, the integration of last mile delivery into a macroscopic simulation to evaluate the emissions has seldomly been achieved. In fact, studies focusing solely on last mile delivery generally focus on evaluating the delivery service itself. This ignores the effect the delivery service may have on the traffic flow in cities, and therefore, on the resulting emissions. This study fills this gap by presenting the results of two macroscopic traffic simulations of New York City (NYC) in PTV VISUM: (i) on-demand meal delivery services, where the emissions are evaluated for each OD-Pairs (i.e., each trip) and (ii) on-demand meal delivery services, where the emissions are evaluated for each link of the network (i.e., street). This study highlights the effect on-demand meal delivery has on the travelled distance (i.e., detours), congestion and emissions per km of every vehicle in the network, not just the delivery vehicles.

Funding

EPSRC Centre for Doctoral Training in Embedded Intelligence

Engineering and Physical Sciences Research Council

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Ford Motor Company

History

School

  • Mechanical, Electrical and Manufacturing Engineering
  • Science

Department

  • Computer Science

Published in

International Journal of Environmental Research and Public Health

Volume

19

Issue

18

Publisher

MDPI

Version

  • VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Acceptance date

2022-09-14

Publication date

2022-09-16

Copyright date

2022

ISSN

1661-7827

eISSN

1660-4601

Language

  • en

Depositor

Deposit date: 31 January 2023

Article number

11667

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