Abstract 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.


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