The transport sector is currently facing a significant transition, with strong drivers includingdecarbonization and digitalization trends, especially in urban passenger transport. The availability ofmonitoring data is at the basis of the development of optimization models supporting an enhancedurban mobility, with multiple benefits including lower pollutants and CO2 emissions, lower energyconsumption, better transport management and land space use. This paper presents two datasetsthat represent time series with a high temporal resolution (five-minute time step) both for vehiclesand bike sharing use in the city of Turin, located in Northern Italy. These high-resolution profileshave been obtained by the collection and elaboration of available online resources providing liveinformation on traffic monitoring and bike sharing docking stations. The data are provided for theentire year 2018, and they represent an interesting basis for the evaluation of seasonal and dailyvariability patterns in urban mobility. These data may be used for different applications, rangingfrom the chronological distribution of mobility demand, to the estimation of passenger transportflows for the development of transport models in urban contexts. Moreover, traffic profiles are at thebasis for the modeling of electric vehicles charging strategies and their interaction with the powergrid.
Toward Sustainability: Bike-Sharing Systems Design, Simulation and Management
