Machine Learning Applied to Last Mile Operations: Applying Machine Learning Models for Stops Classification in Urban Logistics

Bernardo Puente-Mejia; Carlos Suárez-Núñez; David Calahorrano; Martin Gavilanes; Daniel Masaquiza

doi:10.1007/978-3-031-06862-1_38

Machine Learning Applied to Last Mile Operations: Applying Machine Learning Models for Stops Classification in Urban Logistics

Bernardo Puente-Mejia^*
, Carlos Suárez-Núñez
, David Calahorrano
, Martin Gavilanes
, Daniel Masaquiza

^*Corresponding author for this work

Universidad San Francisco de Quito

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This study analyzes urban freight GPS data to achieve more efficient public policies and better last-mile operations planning. The main objective is to obtain the time stops of each urban freight vehicle and classify the stop as traffic, delivery, or rest. After classifying each stop using data mining algorithms and applying machine learning models such as K-Means and HDBSCAN, the study demonstrates that it is possible to apply machine learning models capable of grouping GPS stops by similar features. The clusters obtained are relevant inputs to future research and potential applications, such as in public policy decision making or routing optimization allowing private and public entities to optimize urban logistics and last-mile operations.

Original language	English
Title of host publication	Production and Operations Management - POMS 2021
Editors	Jorge Vargas Florez, Irineu de Brito Junior, Adriana Leiras, Sandro Alberto Paz Collado, Miguel Domingo González Alvarez, Carlos Alberto González-Calderón, Sebastian Villa Betancur, Michelle Rodríguez, Diana Ramirez-Rios
Publisher	Springer
Pages	501-512
Number of pages	12
ISBN (Print)	9783031068614
DOIs	https://doi.org/10.1007/978-3-031-06862-1_38
State	Published - 2022
Event	International Conference on Production and Operations Management, POMS 2021 - Virtual, Online Duration: 2 Dec 2021 → 4 Dec 2021

Publication series

Name	Springer Proceedings in Mathematics and Statistics
Volume	391
ISSN (Print)	2194-1009
ISSN (Electronic)	2194-1017

Conference

Conference	International Conference on Production and Operations Management, POMS 2021
City	Virtual, Online
Period	2/12/21 → 4/12/21

Keywords

Cluster
Geospatial data analysis
HDBSCAN
K-means
Last mile operations
Machine learning
Urban logistics

Access to Document

10.1007/978-3-031-06862-1_38

Cite this

Puente-Mejia, B., Suárez-Núñez, C., Calahorrano, D., Gavilanes, M., & Masaquiza, D. (2022). Machine Learning Applied to Last Mile Operations: Applying Machine Learning Models for Stops Classification in Urban Logistics. In J. Vargas Florez, I. de Brito Junior, A. Leiras, S. A. Paz Collado, M. D. González Alvarez, C. A. González-Calderón, S. Villa Betancur, M. Rodríguez, & D. Ramirez-Rios (Eds.), Production and Operations Management - POMS 2021 (pp. 501-512). (Springer Proceedings in Mathematics and Statistics; Vol. 391). Springer. https://doi.org/10.1007/978-3-031-06862-1_38

Puente-Mejia, Bernardo ; Suárez-Núñez, Carlos ; Calahorrano, David et al. / Machine Learning Applied to Last Mile Operations : Applying Machine Learning Models for Stops Classification in Urban Logistics. Production and Operations Management - POMS 2021. editor / Jorge Vargas Florez ; Irineu de Brito Junior ; Adriana Leiras ; Sandro Alberto Paz Collado ; Miguel Domingo González Alvarez ; Carlos Alberto González-Calderón ; Sebastian Villa Betancur ; Michelle Rodríguez ; Diana Ramirez-Rios. Springer, 2022. pp. 501-512 (Springer Proceedings in Mathematics and Statistics).

@inproceedings{ba38c41fffe6434386caeb13178b17be,

title = "Machine Learning Applied to Last Mile Operations: Applying Machine Learning Models for Stops Classification in Urban Logistics",

abstract = "This study analyzes urban freight GPS data to achieve more efficient public policies and better last-mile operations planning. The main objective is to obtain the time stops of each urban freight vehicle and classify the stop as traffic, delivery, or rest. After classifying each stop using data mining algorithms and applying machine learning models such as K-Means and HDBSCAN, the study demonstrates that it is possible to apply machine learning models capable of grouping GPS stops by similar features. The clusters obtained are relevant inputs to future research and potential applications, such as in public policy decision making or routing optimization allowing private and public entities to optimize urban logistics and last-mile operations.",

keywords = "Cluster, Geospatial data analysis, HDBSCAN, K-means, Last mile operations, Machine learning, Urban logistics",

author = "Bernardo Puente-Mejia and Carlos Su{\'a}rez-N{\'u}{\~n}ez and David Calahorrano and Martin Gavilanes and Daniel Masaquiza",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; International Conference on Production and Operations Management, POMS 2021 ; Conference date: 02-12-2021 Through 04-12-2021",

year = "2022",

doi = "10.1007/978-3-031-06862-1\_38",

language = "Ingl{\'e}s",

isbn = "9783031068614",

series = "Springer Proceedings in Mathematics and Statistics",

publisher = "Springer",

pages = "501--512",

editor = "\{Vargas Florez\}, Jorge and \{de Brito Junior\}, Irineu and Adriana Leiras and \{Paz Collado\}, \{Sandro Alberto\} and \{Gonz{\'a}lez Alvarez\}, \{Miguel Domingo\} and Gonz{\'a}lez-Calder{\'o}n, \{Carlos Alberto\} and \{Villa Betancur\}, Sebastian and Michelle Rodr{\'i}guez and Diana Ramirez-Rios",

booktitle = "Production and Operations Management - POMS 2021",

}

Puente-Mejia, B, Suárez-Núñez, C, Calahorrano, D, Gavilanes, M & Masaquiza, D 2022, Machine Learning Applied to Last Mile Operations: Applying Machine Learning Models for Stops Classification in Urban Logistics. in J Vargas Florez, I de Brito Junior, A Leiras, SA Paz Collado, MD González Alvarez, CA González-Calderón, S Villa Betancur, M Rodríguez & D Ramirez-Rios (eds), Production and Operations Management - POMS 2021. Springer Proceedings in Mathematics and Statistics, vol. 391, Springer, pp. 501-512, International Conference on Production and Operations Management, POMS 2021, Virtual, Online, 2/12/21. https://doi.org/10.1007/978-3-031-06862-1_38

Machine Learning Applied to Last Mile Operations: Applying Machine Learning Models for Stops Classification in Urban Logistics. / Puente-Mejia, Bernardo; Suárez-Núñez, Carlos; Calahorrano, David et al.
Production and Operations Management - POMS 2021. ed. / Jorge Vargas Florez; Irineu de Brito Junior; Adriana Leiras; Sandro Alberto Paz Collado; Miguel Domingo González Alvarez; Carlos Alberto González-Calderón; Sebastian Villa Betancur; Michelle Rodríguez; Diana Ramirez-Rios. Springer, 2022. p. 501-512 (Springer Proceedings in Mathematics and Statistics; Vol. 391).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Machine Learning Applied to Last Mile Operations

T2 - International Conference on Production and Operations Management, POMS 2021

AU - Puente-Mejia, Bernardo

AU - Suárez-Núñez, Carlos

AU - Calahorrano, David

AU - Gavilanes, Martin

AU - Masaquiza, Daniel

PY - 2022

Y1 - 2022

N2 - This study analyzes urban freight GPS data to achieve more efficient public policies and better last-mile operations planning. The main objective is to obtain the time stops of each urban freight vehicle and classify the stop as traffic, delivery, or rest. After classifying each stop using data mining algorithms and applying machine learning models such as K-Means and HDBSCAN, the study demonstrates that it is possible to apply machine learning models capable of grouping GPS stops by similar features. The clusters obtained are relevant inputs to future research and potential applications, such as in public policy decision making or routing optimization allowing private and public entities to optimize urban logistics and last-mile operations.

AB - This study analyzes urban freight GPS data to achieve more efficient public policies and better last-mile operations planning. The main objective is to obtain the time stops of each urban freight vehicle and classify the stop as traffic, delivery, or rest. After classifying each stop using data mining algorithms and applying machine learning models such as K-Means and HDBSCAN, the study demonstrates that it is possible to apply machine learning models capable of grouping GPS stops by similar features. The clusters obtained are relevant inputs to future research and potential applications, such as in public policy decision making or routing optimization allowing private and public entities to optimize urban logistics and last-mile operations.

KW - Cluster

KW - Geospatial data analysis

KW - HDBSCAN

KW - K-means

KW - Last mile operations

KW - Machine learning

KW - Urban logistics

UR - https://www.scopus.com/pages/publications/85140789427

U2 - 10.1007/978-3-031-06862-1_38

DO - 10.1007/978-3-031-06862-1_38

M3 - Contribución a la conferencia

AN - SCOPUS:85140789427

SN - 9783031068614

T3 - Springer Proceedings in Mathematics and Statistics

SP - 501

EP - 512

BT - Production and Operations Management - POMS 2021

A2 - Vargas Florez, Jorge

A2 - de Brito Junior, Irineu

A2 - Leiras, Adriana

A2 - Paz Collado, Sandro Alberto

A2 - González Alvarez, Miguel Domingo

A2 - González-Calderón, Carlos Alberto

A2 - Villa Betancur, Sebastian

A2 - Rodríguez, Michelle

A2 - Ramirez-Rios, Diana

PB - Springer

Y2 - 2 December 2021 through 4 December 2021

ER -

Puente-Mejia B, Suárez-Núñez C, Calahorrano D, Gavilanes M, Masaquiza D. Machine Learning Applied to Last Mile Operations: Applying Machine Learning Models for Stops Classification in Urban Logistics. In Vargas Florez J, de Brito Junior I, Leiras A, Paz Collado SA, González Alvarez MD, González-Calderón CA, Villa Betancur S, Rodríguez M, Ramirez-Rios D, editors, Production and Operations Management - POMS 2021. Springer. 2022. p. 501-512. (Springer Proceedings in Mathematics and Statistics). doi: 10.1007/978-3-031-06862-1_38

Machine Learning Applied to Last Mile Operations: Applying Machine Learning Models for Stops Classification in Urban Logistics

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this