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Land consumption of delivery robots and bicycle couriers for on‐demand meal delivery using gps data and simulations based on the time‐area concept

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posted on 2021-11-25, 11:40 authored by Maren Schnieder, Christopher Hinde, Andrew WestAndrew West
Regulating the curbside usage of delivery vehicles and ride‐hailing services as well as micromobility has been a challenge in the last years, a challenge which might worsen with the increase of autonomous vehicles. The contribution of the research outlined in this paper is an evaluation method of the land use of on‐demand meal delivery services such as Deliveroo and UberEats. It evaluates the effect parking policies, operating strategy changes, and scheduling options have on the land consumption of bicycle couriers and sidewalk automated delivery robots (SADRs). Various operating strategies (i.e., shared fleets and fleets operated by restaurants), parking policies (i.e., parking at the restaurant, parking at the customer or no parking) and scheduling options (i.e., one meal per vehicle, multiple meals per vehicle) are simulated and applied to New York City (NYC). Additionally, the time‐area requirements of on‐demand meal delivery services are calculated based on GPS traces of Deliveroo and UberEats riders in two UK cities. The simulation in the paper shows that SADRs can reduce the time‐area requirements by half compared with bicycle couriers. The effect of operating strategy changes and forbidding vehicles to park at the customer’s home is small. Delivering multiple meals in one tour halves the time‐area requirements. The time‐area requirements based on GPS traces is around 300 m2∙min per order. The study allows policymakers to learn more about the land use of on‐demand meal delivery services and how these can be influenced. Hence, they can adjust their policy strategies to ensure that on‐demand meal delivery services are provided in a way that they use land effectively, reduce external costs, improve sustainability and benefit everyone.

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

Published in

Sustainability (Switzerland)

Volume

13

Issue

20

Publisher

Mdpi

Version

  • VoR (Version of Record)

Rights holder

© The authors

Publisher statement

This is an Open Access Article. It is published by MDPI under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/

Acceptance date

2021-10-06

Publication date

2021-10-14

Copyright date

2021

eISSN

2071-1050

Language

  • en

Depositor

Deposit date: 23 November 2021

Article number

11375

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