A spatio-functional logistics profile clustering analysis method for metropolitan areas

Andrés Regal, Jesús Gonzalez-Feliu, Michelle Rodríguez

Producción científica: Contribución a una revistaArtículo de revista revisión exhaustiva

1 Cita (Scopus)


This paper proposes a framework to define a zoning procedure using clustering taking into account socio-economic, spatial and logistics intensity variables to support urban logistics planning and management decision making. The methodology is centered around comparing two dimension reduction algorithms (PCA and UMAP) and four clustering algorithms (-means, affinity propagation, HDBSCAN, and SOM). This comparison is based on combinations of dimension reduction and clustering techniques, assessing the results for geographic coherence, patterns that are captured and the statistical validity of the clustering results. The variables used in the clustering are defined from socio-economic, geographic and demographic data issued from standard sources, and a logistics intensity estimation via freight trip generation (FTG models). Within its application to Lima, Peru, results show that the choice of the FTG model, the main logistics intensity variable, has a strong impact on the final composition of the logistics profile and also on ensuring a geographical sense of the clustering results. Finally, research, policy, and practical implications are discussed, as well as future research stemming from these results.
Idioma originalInglés
Número de artículo103312
PublicaciónTransportation Research, Part E: Logistics and Transportation Review
Fecha en línea anticipada18 oct. 2023
EstadoPublicada - nov. 2023

Nota bibliográfica

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© 2023 Elsevier Ltd

Palabras clave

  • Logística urbana
  • Zonificación
  • Modelos de generación de viajes de carga
  • Análisis de agrupación
  • Perú
  • Lima


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