Contextual Data Integration for Bike-sharing Demand Prediction with Graph Neural Networks in Degraded Weather Conditions
This work addresses the problem of accurate bike-sharing demand forecasting for urban planners and operators, particularly in adverse weather, but it is incremental as it builds on existing graph neural network methods.
The study tackled predicting bike-sharing demand in degraded weather conditions by integrating contextual data like weather, time embedding, and road traffic flow, resulting in a 20% reduction in prediction error compared to a baseline model and outperforming state-of-the-art results with time embedding.
Demand for bike sharing is impacted by various factors, such as weather conditions, events, and the availability of other transportation modes. This impact remains elusive due to the complex interdependence of these factors or locationrelated user behavior variations. It is also not clear which factor is additional information which are not already contained in the historical demand. Intermodal dependencies between bike-sharing and other modes are also underexplored, and the value of this information has not been studied in degraded situations. The proposed study analyzes the impact of adding contextual data, such as weather, time embedding, and road traffic flow, to predict bike-sharing Origin-Destination (OD) flows in atypical weather situations Our study highlights a mild relationship between prediction quality of bike-sharing demand and road traffic flow, while the introduced time embedding allows outperforming state-of-the-art results, particularly in the case of degraded weather conditions. Including weather data as an additional input further improves our model with respect to the basic ST-ED-RMGC prediction model by reducing of more than 20% the prediction error in degraded weather condition.