Investigating mobility patterns through crowd-sourced activity data

Investigating trip purposes represents an important phase of travel demand modeling which allows to correctly infer mobility patterns and to better understand travel behavior. Until now, researchers collected information on the motivation for carrying out a trip mainly through travel surveys. However, traditional methods of acquiring this type of information are challenging and expensive to implement; therefore, they are typically performed infrequently and with low sampling rates. These two occurrences do not always allow for adequate representation of the heterogeneity of trip purposes. This paper aims to investigate trip purposes through the joint processing of GPS-based data, such as Floating Car Data (FCD), and aggregated activity data available through open-source platforms, such as Google Popular Times (GPT), which provide information on the daily distribution of visits in a certain activity venue as well as the average visit duration based on aggregated data obtained from users who share their mobile phones geo-traces. Through the application of clustering techniques on a FCD dataset containing trips carried out between September and November 2020 in the EUR district of Rome, Italy, we classify the trips as Home-Work trips and Not Home-Work trips, obtaining a total of 96 Origin-Destination matrices (one for every 15 minute time interval). Not Home-Work trips are further examined, exploiting 6 patterns obtained through the clustering of GPT activity data, and classified according to the arrival time at destination and the duration of their stopover. The obtained Origin-Destination matrices have been compared to values found in literature in terms of spatial and temporal flexibility to validate the results.