Johnson, D;
Chaniotakis, E;
(2021)
Innovative last mile delivery concepts: Evaluating last mile delivery using a traffic simulator.
In:
2021 7th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS).
IEEE: Heraklion, Greece.
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Abstract
This paper investigates, through a simulation approach, how novel alternative last-mile solutions (LMS) can harmonise network efficiency with environmental sustainability in a Washington D.C. urban setting. The ability of public buses to scale delivery services with demand was fundamental to reducing Greenhouse Gas (GHG) emissions and harmful air pollutants. Extending the network by increasing the number of delivery points, this paper employed a K-means clustering algorithm to determine optimal locations of Urban Consolidation Centres (UCCs). Utilising UCCs enabled further environmental and efficiency gains to be realised through consolidation and the ability to deliver "very last mile" through E-cargo bikes.
Type: | Proceedings paper |
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Title: | Innovative last mile delivery concepts: Evaluating last mile delivery using a traffic simulator |
Event: | 2021 7th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) |
Dates: | 16 June 2021 - 17 June 2021 |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1109/mt-its49943.2021.9529279 |
Publisher version: | http://dx.doi.org/10.1109/mt-its49943.2021.9529279 |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
Keywords: | Greenhouse effect, Atmospheric modeling, Green products, Clustering algorithms, Air pollution, Intelligent transportation systems |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources |
URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10134160 |
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