Abstract
Urban mobility is directly linked to the demand for communication resources and, clearly, its understanding is useful for better planning of urban and communication systems. However, getting data about urban mobility is still a challenge. In many cases, only a few companies have access to accurate and updated data. In most cases, these data are also privacy sensitive. It is thus important to generate models that can help to understand mobility patterns. We here characterize the demands of a two-way car-sharing system. We explore data of the public API of Modo, a car-sharing system that operates in Vancouver (Canada) and nearby regions. Our study uncovers patterns of users’ habits and demands in the service, which can be explored for urban and communication planning.
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Notes
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The standard deviation of idle and busy curves are the light red and gray curves, respectively.
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Cumulative distribution function (CDF) of the Weibull distribution: \(F(x;\alpha ,\beta ) = 1 - e^{-(x/\beta )^\alpha }\).
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Cumulative distribution function (CDF) of the Pareto distribution: \(F(x)=1-(\frac{\beta }{x})^\alpha \).
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Rooke, F., Aquiles, V., Vieira, A.B., Almeida, J.M., Drago, I. (2019). Characterizing Usage Patterns and Service Demand of a Two-Way Car-Sharing System. In: Oliveira, J., Farias, C., Pacitti, E., Fortino, G. (eds) Big Social Data and Urban Computing. BiDU 2018. Communications in Computer and Information Science, vol 926. Springer, Cham. https://doi.org/10.1007/978-3-030-11238-7_1
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