Big, transport-related datasets are nowadays publicly available, which makes
data-driven mobility analysis possible. Trips with their origins, destinations
and travel times are collected in publicly available big databases, which
allows for a deeper and richer understanding of mobility patterns. This paper
proposes a low dimensional approach to combine these data sources with weather
data in order to forecast the daily demand for Bike Sharing Systems (BSS). The
core of this approach lies in the proposed clustering technique, which reduces
the dimension of the problem and, differently from other machine learning
techniques, requires limited assumptions on the model or its parameters. The
proposed clustering technique synthesizes mobility data quantitatively (number
of trips) and spatially (mean trip origin and destination). This allows
identifying recursive mobility patterns that - when combined with weather data
- provide accurate predictions of the demand. The method is tested with
real-world data from New York City. We synthesize more than four million trips
into vectors of movement, which are then combined with weather data to forecast
the daily demand at a city-level. Results show that, already with a
one-parameters model, the proposed approach provides accurate predictions.Comment: 2019 IEEE. Personal use of this material is permitted. Permission
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