Sriram, A.Ananthanarayana, V.S.2026-02-062023Proceedings of the AISB Convention 2023, 2023, Vol., , p. 57-63https://doi.org/https://idr.nitk.ac.in/handle/123456789/29408This paper presents a comprehensive study on the usage of Uber in different markets, with a focus on understanding the impact of demographic factors, public transit proximity, weather and extreme events on the demand for Uber ride-hailing services. This study involves application of Explainable AI techniques for feature selection among multiple data sources to model external factors on the Uber ride usage. Furthermore, factors such as weather and local events are used for ride usage forecasting using spatiotemporal aspects and extreme event analysis. The results of this study showed that certain factors like demography, proximity of public transit play a role in shaping the usage patterns of Uber. Also, extreme events, such as weather conditions and local events, were found to have a significant impact on the demand for Uber services. This study provides valuable insights for Uber, similar ride-hailing services and policymakers for optimal resource allocation, and lays the foundation for further research on the relationship between transportation services and various contextual factors. © AISB Convention 2023.All rights reserved.Contextual DependencyExplainable AI Extreme event analysisFeature selectionRegression analysisRide forecastingRide-hailing servicesSpatiotemporal analysis