Spatiotemporal Data Analytics for Optimizing Public Transportation Networks in Urban Environments

Abstract

Urban transportation systems are increasingly challenged by rapid urbanization, congestion, and environmental concerns. Leveraging spatiotemporal data analytics has emerged as a promising approach to optimize public transportation networks by understanding mobility patterns, temporal usage trends, and spatial demand distributions. This paper explores methodologies that integrate spatiotemporal analytics with urban transport planning to improve system efficiency, reduce delays, and enhance commuter satisfaction. We contextualize this study within the state of the art as of discuss relevant techniques, tools, and findings. Furthermore, we review existing literature a synthesized framework that aligns big data analytics with public transport policy design.