Spatiotemporal Data Analysis of Urban Traffic Systems: Understanding Operational Patterns and Inferring Future States

Abstract

Urban traffic flow data constitutes a complex dynamic system, exhibiting both temporal evolution and spatial dependence. In the context of high-density metropolitan areas such as the Greater Bay Area in China and the construction of emerging smart cities, extracting operational patterns from historical traffic observation data and inferring future traffic conditions has become a core issue in urban computing and intelligent transportation. This paper conducts a logic-oriented analysis of urban traffic spatiotemporal data. First, it examines three temporal evolution characteristics: periodicity, trend, and abrupt change. Then, it analyzes how road network topology affects the spatial dependence between road segments. Based on this, the paper explores the coupling mechanism between the temporal and spatial dimensions from the perspective of congestion propagation dynamics and extracts a logical framework for traffic condition prediction. This paper also discusses key challenges such as cross-regional multi-modal analysis, the integration of domain knowledge and data-driven methods, and the shift from open-loop prediction to closed-loop decision support, aiming to provide a structured analytical framework that contributes to understanding the intrinsic mechanisms of urban traffic system operation.