The transport sector is currently facing a significant transition, with strong drivers including decarbonization and digitalization trends, especially in urban passenger transport. The availability of monitoring data is at the basis of the development of optimization models supporting an enhanced urban mobility, with multiple benefits including lower pollutants and CO2
emissions, lower energy consumption, better transport management and land space use. This paper presents two datasets that represent time series with a high temporal resolution (five-minute time step) both for vehicles and bike sharing use in the city of Turin, located in Northern Italy. These high-resolution profiles have been obtained by the collection and elaboration of available online resources providing live information on traffic monitoring and bike sharing docking stations. The data are provided for the entire year 2018, and they represent an interesting basis for the evaluation of seasonal and daily variability patterns in urban mobility. These data may be used for different applications, ranging from the chronological distribution of mobility demand, to the estimation of passenger transport flows for the development of transport models in urban contexts. Moreover, traffic profiles are at the basis for the modeling of electric vehicles charging strategies and their interaction with the power grid.
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