Analyzing Last Mile Delivery Operations in Barcelona’s Urban Freight Transport Network

  • Burcu Kolbay
  • Petar Mrazovic
  • Josep Llus Larriba-Pey
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 189)


Barcelona has recently started a new strategy to control and understand Last Mile Delivery, AreaDUM. The strategy is to provide freight delivery vehicle drivers with a mobile app that has to be used every time their vehicle is parked in one of the designated AreaDUM surface parking spaces in the streets of the city. This provides a significant amount of data about the activity of the freight delivery vehicles, their patterns, the occupancy of the spaces, etc.

In this paper, we provide a preliminary set of analytics preceded by the procedures employed for the cleansing of the dataset. During the analysis we show that some data blur the results and using a simple strategy to detect when a vehicle parks repeatedly in close-by parking slots, we are able to obtain different, yet more reliable results. In our paper, we show that this behavior is common among users with \(80\%\) prevalence. We conclude that we need to analyse and understand the user behaviors further with the purpose of providing predictive algorithms to find parking lots and smart routing algorithms to minimize traffic.


Urban freight Clustering Partitioning Around Medoids User behavior Smart City AreaDUM 


  1. 1.
    New York Edges Out London as the World’s “Smartest” City.
  2. 2.
    Ajuntament de Barcelona.
  3. 3.
    Barcelona Serveis de Municipals (B:SM).
  4. 4.
  5. 5.
    Hwang, T., Ouyang, Y.: Urban freight truck routing under stochastic congestion and emission considerations. Sustainability 7(6), 6610–6625 (2015)CrossRefGoogle Scholar
  6. 6.
    Reisman, A., Chase, M.: Strategies for Reducing the Impacts of Last-Mile Freight in Urban Business Districts. UT Planning (2011)Google Scholar
  7. 7.
    Yannis, G., Golias, J., Antoniou, C.: Effects of urban delivery restrictions on traffic movements. Transp. Plan. Technol. 29(4), 295–311 (2006)CrossRefGoogle Scholar
  8. 8.
    Quak, H., de Koster, R.: The impacts of time access restrictions and vehicle weight restrictions on food retailers and the environment. Eur. J. Transp. Infrastruct. Res. (Print) 131–150 (2006)Google Scholar
  9. 9.
    Banerjee, A., Dave, R.N.: Validating clusters using the Hopkins statistic. In: Proceedings of the IEEE International Conference on Fuzzy Systems, vol. 1, pp. 149–153 (2004)Google Scholar
  10. 10.
    Kaufman, L., Rousseeuw, P.J.: Clustering by means of medoids. In: Dodge, Y. (ed.) Statistical Data Analysis Based on L1 Norm, pp. 405–416 (1987)Google Scholar
  11. 11.
    Shumaker, B.P., Sinnott, R.W.: Astronomical computing: 1. Computing under the open sky. 2. Virtues of the haversine. Sky Telesc. 68, 158–159 (1984)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  • Burcu Kolbay
    • 1
  • Petar Mrazovic
    • 2
  • Josep Llus Larriba-Pey
    • 1
  1. 1.DAMA-UPC Data ManagementUniversitat Politecnica de CatalunyaBarcelonaSpain
  2. 2.Department of Software and Computer SystemsRoyal Institute of TechnologyStockholmSweden

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