Free-Floating Car Sharing (FFCS) services are currently available in tens of cities and countries spread all over the worlds. Depending on citizens’ habits, service policies, and road conditions, car usage profiles are rather variable and often hardly predictable. Even within the same city, different usage trends emerge in different districts and in various time slots and weekdays. Therefore, modeling car availability in FFCS systems is particularly challenging. For these reasons, the research community has started to investigate the applicability of Machine Learning models to analyze FFCS usage data. This paper addresses the problem of predicting the short-term level of availability of the FFCS service in the short term. Specifically, it investigates the applicability of Machine Learning models to forecast the number of available car within a restricted urban area. It seeks the spatial and temporal contexts in which nonlinear ML models, trained on past usage data, are necessary to accurately predict car availability. Leveraging ML has shown to be particularly effective while considering highly dynamic urban contexts, where FFCS service usage is likely to suddenly and unexpectedly change. To tailor predictive models to the real FFCS data, we study also the influence of ML algorithm, prediction horizon, and characteristics of the neighborhood of the target area. The empirical outcomes allow us to provide system managers with practical guidelines to setup and tune ML models.
Location-Based Social Networks can be profitably exploited to characterize citizens' activities in urban environments. However, collecting LBSN is potentially challenging due to privacy concerns, connectivity issues, and potential imbalances in LBSN service usage. We propose to complement LBSN data with mobility data in the analysis of citizens' activities in urban areas. Unlike the explicit insights provided by LBSN users, mobility data give implicit feedback on citizens' habits. This paper explores the spatial and temporal conditions under which user habits are coherent according to both sources and reports the most reliable common sequences of visited categories of Points-Of-Interests. To this aim, it relies on a multidimensional model in which recurrent citizens' activities are described by a new pattern type, namely the generalized activity pattern. It also detects the eventual presence of bias between LBSN and mobility user activities by customizing the established Statistical Parity metric. The motivations behind the detected bias are explained in terms of combinations of POI categories that are most likely to be the main causes. We evaluate the proposed approach on real-world data achieved from Foursquare check-ins, taxi service, and free-floating car sharing. The results highlight not only the complementarity of the data sources regarding specific POI categories, but also their interchangeability in many spatiotemporal conditions.
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