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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.

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As it emerges from the literature, electricity access in rural contexts is deeply intertwined with socioeconomic dynamics. However, the advent of a reliable and sufficient source of electricity is not the sole driver that might contribute to local development. Indeed, complementary activities might have a crucial role in sustaining the development of rural communities as well as the electricity access. The current research addresses the lack of counterfactual scenarios in which the impact of complementary activities on electrification projects can be investigated. The authors introduce the case study of Matembwe village, a rural community in the Njombe region of Tanzania. The data collection includes the electricity consumption, number of electricity connections, and number of income-generating activities in a timespan ranging from 1989 to 2015. The analysis is based on system dynamics. The study considers different scenarios representing the dynamics related to the following complementary actions: access to market measures, access to credit measures, and access to usable skills. On the one hand, the study reveals that the effectiveness of the considered complementary actions is limited except from the access to microcredit which fosters an increase in electricity connections by 17%. On the other hand, both access to microcredit and the starting up of a local cooperative by CEFA Onlus that reinvests its profits in the local market impact the socio-economic dimension by 69% and 22%, respectively.

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