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Cantelmo Guido, Kucharski Rafał, Antoniou Constantinos
A low dimensional model for bike sharing demand forecasting
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 th...    »
Horizon 2020:
This research has been partially sponsored by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska- Curie grant agreement No 754462 and the H2020 project NOESIS - No769980
Journal title:
6th International Conference on Models and Technologies for Intelligent Transportation Systems
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