dc.description.abstract
Designing and generating wiser policies for urban systems and infrastructures is a
challenge of paramount importance. Today, the cities that present the most successful
transport strategies are prioritising the movement of people, giving residents
and visitors a wider variety of attractive transport options while creating effective
ways to switch from private to public transport means. Understanding the use and
the impact of public infrastructures that facilitate mobility is crucial.
We consider a dataset of one year of activity in the form of car park occupancy in
the province of Barcelona. The data comprises ten different parking facilities located
close to train stations. We propose and analyze different and intuitive prediction
models based on statistical and mathematical approximations.
First, we analyze the occupancy recordings in different parking locations and show
that the activity is strongly coupled with the circadian rhythm, following a 24-hours
cyclic pattern. Second, we implement a predictive model to provide the occupancy of
a particular parking for an entire future day. We show that for both, statistical and
mathematical approximations it performs quite accurately. Third, we implement a
predictive model to guess the occupancy of the remaining hours of the day given the
occupancy of the previous hours. Finally, a qualitative and quantitative analysis of
the parking occupancy during the Covid-19 pandemic has been performed in order
to understand how the global situation has influenced the parking usage.
Our results show that, despite the apparent complexity associated to public mobility
and use of car parks, very simple models motivated in intuitive principles are sufficient
to understand and predict this dynamics. Overall, our results can facilitate
the design of public policies to facilitate the mobility within Barcelona and its surroundings,
by providing a better understanding of how the citizens switch between
private cars and public trains.
