Modelling the performance of delivery vehicles across urban micro-regions to accelerate the transition to cargo-bike logistics
NeurIPS 2022 Workshop on Tackling Climate Change with Machine Learning Light goods vehicles (LGV) used extensively in the last mile of delivery are one of the leading polluters in cities. Cargo-bike logistics has been put forward as a high impact candidate for replacing LGVs, with experts estimating...
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| creator | Schrader, Max Kumar, Navish Collignon, Nicolas Sørig, Esben Yoon, Soonmyeong Srivastava, Akash Xu, Kai Astefanoaei, Maria |
| description | NeurIPS 2022 Workshop on Tackling Climate Change with Machine
Learning Light goods vehicles (LGV) used extensively in the last mile of delivery are
one of the leading polluters in cities. Cargo-bike logistics has been put
forward as a high impact candidate for replacing LGVs, with experts estimating
over half of urban van deliveries being replaceable by cargo bikes, due to
their faster speeds, shorter parking times and more efficient routes across
cities. By modelling the relative delivery performance of different vehicle
types across urban micro-regions, machine learning can help operators evaluate
the business and environmental impact of adding cargo-bikes to their fleets. In
this paper, we introduce two datasets, and present initial progress in
modelling urban delivery service time (e.g. cruising for parking, unloading,
walking). Using Uber's H3 index to divide the cities into hexagonal cells, and
aggregating OpenStreetMap tags for each cell, we show that urban context is a
critical predictor of delivery performance. |
| doi_str_mv | 10.48550/arxiv.2301.12887 |
| format | Article |
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Learning Light goods vehicles (LGV) used extensively in the last mile of delivery are
one of the leading polluters in cities. Cargo-bike logistics has been put
forward as a high impact candidate for replacing LGVs, with experts estimating
over half of urban van deliveries being replaceable by cargo bikes, due to
their faster speeds, shorter parking times and more efficient routes across
cities. By modelling the relative delivery performance of different vehicle
types across urban micro-regions, machine learning can help operators evaluate
the business and environmental impact of adding cargo-bikes to their fleets. In
this paper, we introduce two datasets, and present initial progress in
modelling urban delivery service time (e.g. cruising for parking, unloading,
walking). Using Uber's H3 index to divide the cities into hexagonal cells, and
aggregating OpenStreetMap tags for each cell, we show that urban context is a
critical predictor of delivery performance.</description><identifier>DOI: 10.48550/arxiv.2301.12887</identifier><language>eng</language><subject>Computer Science - Learning</subject><creationdate>2023-01</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,777,882</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2301.12887$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2301.12887$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Schrader, Max</creatorcontrib><creatorcontrib>Kumar, Navish</creatorcontrib><creatorcontrib>Collignon, Nicolas</creatorcontrib><creatorcontrib>Sørig, Esben</creatorcontrib><creatorcontrib>Yoon, Soonmyeong</creatorcontrib><creatorcontrib>Srivastava, Akash</creatorcontrib><creatorcontrib>Xu, Kai</creatorcontrib><creatorcontrib>Astefanoaei, Maria</creatorcontrib><title>Modelling the performance of delivery vehicles across urban micro-regions to accelerate the transition to cargo-bike logistics</title><description>NeurIPS 2022 Workshop on Tackling Climate Change with Machine
Learning Light goods vehicles (LGV) used extensively in the last mile of delivery are
one of the leading polluters in cities. Cargo-bike logistics has been put
forward as a high impact candidate for replacing LGVs, with experts estimating
over half of urban van deliveries being replaceable by cargo bikes, due to
their faster speeds, shorter parking times and more efficient routes across
cities. By modelling the relative delivery performance of different vehicle
types across urban micro-regions, machine learning can help operators evaluate
the business and environmental impact of adding cargo-bikes to their fleets. In
this paper, we introduce two datasets, and present initial progress in
modelling urban delivery service time (e.g. cruising for parking, unloading,
walking). Using Uber's H3 index to divide the cities into hexagonal cells, and
aggregating OpenStreetMap tags for each cell, we show that urban context is a
critical predictor of delivery performance.</description><subject>Computer Science - Learning</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotkM1OhDAURrtxYUYfwJV9ARAKpWVpJv4lY9zMnlzaW6ax0ElbibPx2QV0dXNzvpzFIeSuLPJacl48QPi2c86qosxLJqW4Jj_vXqNzdhpoOiE9YzA-jDAppN7QBdkZw4XOeLLKYaSggo-RfoUeJjra5csCDtZPkSa_UIUOAyTcbCnAFG1a6AoVhMFnvf1E6vxgY7Iq3pArAy7i7f_dkePz03H_mh0-Xt72j4cMGiEyQMGafnFrVQCi1qbnTDPgFTO84mVbC14rbkypUPB1yRusRd1KqaTRbbUj93_aLUB3DnaEcOnWEN0WovoF0t1c-w</recordid><startdate>20230130</startdate><enddate>20230130</enddate><creator>Schrader, Max</creator><creator>Kumar, Navish</creator><creator>Collignon, Nicolas</creator><creator>Sørig, Esben</creator><creator>Yoon, Soonmyeong</creator><creator>Srivastava, Akash</creator><creator>Xu, Kai</creator><creator>Astefanoaei, Maria</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20230130</creationdate><title>Modelling the performance of delivery vehicles across urban micro-regions to accelerate the transition to cargo-bike logistics</title><author>Schrader, Max ; Kumar, Navish ; Collignon, Nicolas ; Sørig, Esben ; Yoon, Soonmyeong ; Srivastava, Akash ; Xu, Kai ; Astefanoaei, Maria</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a677-ae726bccedc0aeeddfb52d2a532f535194754c5ff1ce756bcc56e474988c8fd93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Computer Science - Learning</topic><toplevel>online_resources</toplevel><creatorcontrib>Schrader, Max</creatorcontrib><creatorcontrib>Kumar, Navish</creatorcontrib><creatorcontrib>Collignon, Nicolas</creatorcontrib><creatorcontrib>Sørig, Esben</creatorcontrib><creatorcontrib>Yoon, Soonmyeong</creatorcontrib><creatorcontrib>Srivastava, Akash</creatorcontrib><creatorcontrib>Xu, Kai</creatorcontrib><creatorcontrib>Astefanoaei, Maria</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Schrader, Max</au><au>Kumar, Navish</au><au>Collignon, Nicolas</au><au>Sørig, Esben</au><au>Yoon, Soonmyeong</au><au>Srivastava, Akash</au><au>Xu, Kai</au><au>Astefanoaei, Maria</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Modelling the performance of delivery vehicles across urban micro-regions to accelerate the transition to cargo-bike logistics</atitle><date>2023-01-30</date><risdate>2023</risdate><abstract>NeurIPS 2022 Workshop on Tackling Climate Change with Machine
Learning Light goods vehicles (LGV) used extensively in the last mile of delivery are
one of the leading polluters in cities. Cargo-bike logistics has been put
forward as a high impact candidate for replacing LGVs, with experts estimating
over half of urban van deliveries being replaceable by cargo bikes, due to
their faster speeds, shorter parking times and more efficient routes across
cities. By modelling the relative delivery performance of different vehicle
types across urban micro-regions, machine learning can help operators evaluate
the business and environmental impact of adding cargo-bikes to their fleets. In
this paper, we introduce two datasets, and present initial progress in
modelling urban delivery service time (e.g. cruising for parking, unloading,
walking). Using Uber's H3 index to divide the cities into hexagonal cells, and
aggregating OpenStreetMap tags for each cell, we show that urban context is a
critical predictor of delivery performance.</abstract><doi>10.48550/arxiv.2301.12887</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Learning |
| title | Modelling the performance of delivery vehicles across urban micro-regions to accelerate the transition to cargo-bike logistics |
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