Contextual Data Integration for Bike-sharing Demand Prediction with Graph Neural Networks in Degraded Weather Conditions

TL;DR

This paper introduces a graph neural network model that incorporates contextual data like weather and traffic to improve bike-sharing demand prediction, especially under degraded weather conditions, outperforming existing models.

Contribution

It demonstrates the effectiveness of integrating weather, time embedding, and traffic data into GNNs for more accurate bike-sharing demand forecasting in adverse weather.

Findings

Including weather data reduces prediction error by over 20% in degraded conditions.

Time embedding enhances model performance beyond state-of-the-art.

Road traffic flow has a mild but notable impact on demand prediction.

Abstract

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.