2021 Amazon Last Mile Routing Research Challenge: Data Set
The 2021 Amazon Last Mile Routing Research Challenge, hosted by Amazon.com’s Last Mile Research team, and scientifically supported by the Massachusetts Institute of Technology’s Center for Transportation and Logistics, prompted participants to leverage real operational data to find new and better wa...
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creator | Merchán, Daniel Arora, Jatin Pachon, Julian Konduri, Karthik Winkenbach, Matthias Parks, Steven Noszek, Joseph |
description | The 2021 Amazon Last Mile Routing Research Challenge, hosted by Amazon.com’s Last Mile Research team, and scientifically supported by the Massachusetts Institute of Technology’s Center for Transportation and Logistics, prompted participants to leverage real operational data to find new and better ways to solve a real-world routing problem. In this article, we describe the data set released for the research challenge, which includes route-, stop-, and package-level features for 9,184 historical routes performed by Amazon drivers in 2018 in five metropolitan areas in the United States. This real-world data set excludes any personally identifiable information: all route and package identifiers have been randomly regenerated and related location data have been obfuscated to ensure anonymity. Although multiple synthetic benchmark data sets are available in the literature, the data set of the 2021 Amazon Last Mile Routing Research Challenge is the first large and publicly available data set to include instances based on real-world operational routing data.
History:
This paper has been accepted for the
Transportation Science
Special Issue on Machine Learning Methods and Applications in Large-Scale Route Planning Problems. |
doi_str_mv | 10.1287/trsc.2022.1173 |
format | Article |
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History:
This paper has been accepted for the
Transportation Science
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History:
This paper has been accepted for the
Transportation Science
Special Issue on Machine Learning Methods and Applications in Large-Scale Route Planning Problems.</description><subject>Data collection</subject><subject>Datasets</subject><subject>last-mile delivery</subject><subject>Logistics</subject><subject>machine learning</subject><subject>Metropolitan areas</subject><subject>optimization</subject><subject>Route optimization</subject><subject>Routing</subject><subject>sequencing</subject><subject>Traffic flow</subject><subject>vehicle routing</subject><issn>0041-1655</issn><issn>1526-5447</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNqFkM1LwzAYh4MoOKdXzwHPrXmTNkl3G_UTJsLUc0jTZOvo2plkB_3rbang0dN7eZ7fCw9C10BSoFLcRh9MSgmlKYBgJ2gGOeVJnmXiFM0IySABnufn6CKEHSGQC8hnaDEIgJd7_d13eKVDxC9Na_G6P8am2-C1DVZ7s8XlVret7TZ2ge901PjNxkt05nQb7NXvnaOPh_v38ilZvT4-l8tVYhinMamYk7RmtjKsKmomKsdrxpkR1BFX11ZmUhbEyKKitBYZAVcJYymBSmQgJGdzdDPtHnz_ebQhql1_9N3wUtFCchCFyOVApRNlfB-Ct04dfLPX_ksBUWMfNfZRYx819hkEPAnW9F0T_nApioJLBnRAkglpOtf7ffhv8gc5OG-n</recordid><startdate>202401</startdate><enddate>202401</enddate><creator>Merchán, Daniel</creator><creator>Arora, Jatin</creator><creator>Pachon, Julian</creator><creator>Konduri, Karthik</creator><creator>Winkenbach, Matthias</creator><creator>Parks, Steven</creator><creator>Noszek, Joseph</creator><general>INFORMS</general><general>Institute for Operations Research and the Management Sciences</general><scope>OQ6</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8BJ</scope><scope>FQK</scope><scope>JBE</scope><orcidid>https://orcid.org/0000-0003-1822-083X</orcidid><orcidid>https://orcid.org/0000-0002-8018-9565</orcidid><orcidid>https://orcid.org/0000-0002-8237-625X</orcidid><orcidid>https://orcid.org/0000-0002-6214-5585</orcidid></search><sort><creationdate>202401</creationdate><title>2021 Amazon Last Mile Routing Research Challenge: Data Set</title><author>Merchán, Daniel ; Arora, Jatin ; Pachon, Julian ; Konduri, Karthik ; Winkenbach, Matthias ; Parks, Steven ; Noszek, Joseph</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c362t-b3f82d3ebc3b9d37bf6d363c72f0fdde848890c89b22d7401fb7ce201b7417863</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Data collection</topic><topic>Datasets</topic><topic>last-mile delivery</topic><topic>Logistics</topic><topic>machine learning</topic><topic>Metropolitan areas</topic><topic>optimization</topic><topic>Route optimization</topic><topic>Routing</topic><topic>sequencing</topic><topic>Traffic flow</topic><topic>vehicle routing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Merchán, Daniel</creatorcontrib><creatorcontrib>Arora, Jatin</creatorcontrib><creatorcontrib>Pachon, Julian</creatorcontrib><creatorcontrib>Konduri, Karthik</creatorcontrib><creatorcontrib>Winkenbach, Matthias</creatorcontrib><creatorcontrib>Parks, Steven</creatorcontrib><creatorcontrib>Noszek, Joseph</creatorcontrib><collection>ECONIS</collection><collection>CrossRef</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>International Bibliography of the Social Sciences</collection><collection>International Bibliography of the Social Sciences</collection><jtitle>Transportation science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Merchán, Daniel</au><au>Arora, Jatin</au><au>Pachon, Julian</au><au>Konduri, Karthik</au><au>Winkenbach, Matthias</au><au>Parks, Steven</au><au>Noszek, Joseph</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>2021 Amazon Last Mile Routing Research Challenge: Data Set</atitle><jtitle>Transportation science</jtitle><date>2024-01</date><risdate>2024</risdate><volume>58</volume><issue>1</issue><spage>8</spage><epage>11</epage><pages>8-11</pages><issn>0041-1655</issn><eissn>1526-5447</eissn><abstract>The 2021 Amazon Last Mile Routing Research Challenge, hosted by Amazon.com’s Last Mile Research team, and scientifically supported by the Massachusetts Institute of Technology’s Center for Transportation and Logistics, prompted participants to leverage real operational data to find new and better ways to solve a real-world routing problem. In this article, we describe the data set released for the research challenge, which includes route-, stop-, and package-level features for 9,184 historical routes performed by Amazon drivers in 2018 in five metropolitan areas in the United States. This real-world data set excludes any personally identifiable information: all route and package identifiers have been randomly regenerated and related location data have been obfuscated to ensure anonymity. Although multiple synthetic benchmark data sets are available in the literature, the data set of the 2021 Amazon Last Mile Routing Research Challenge is the first large and publicly available data set to include instances based on real-world operational routing data.
History:
This paper has been accepted for the
Transportation Science
Special Issue on Machine Learning Methods and Applications in Large-Scale Route Planning Problems.</abstract><cop>Baltimore</cop><pub>INFORMS</pub><doi>10.1287/trsc.2022.1173</doi><tpages>4</tpages><orcidid>https://orcid.org/0000-0003-1822-083X</orcidid><orcidid>https://orcid.org/0000-0002-8018-9565</orcidid><orcidid>https://orcid.org/0000-0002-8237-625X</orcidid><orcidid>https://orcid.org/0000-0002-6214-5585</orcidid></addata></record> |
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subjects | Data collection Datasets last-mile delivery Logistics machine learning Metropolitan areas optimization Route optimization Routing sequencing Traffic flow vehicle routing |
title | 2021 Amazon Last Mile Routing Research Challenge: Data Set |
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