Intelligent Traffic Big Data Mining and Prediction Model Considering Spatiotemporal Characteristics of Traffic Flow
TL;DR
This study identifies and quantifies traffic flow spatiotemporal characteristics to establish a targeted quantitative indicator system and designs a full-process big data mining framework to complete multi-source data preprocessing and spatiotemporal correlation feature extraction, constructing a traffic flow prediction model that integrates spatiotemporal characteristics.
Abstract
: Intelligent transportation is the core solution to alleviating urban traffic congestion and enhancing traffic governance efficiency. Accurately capturing the spatiotemporal coupling characteristics of traffic flow is key to achieving precise traffic flow prediction. Addressing current issues such as the neglect of spatiotemporal correlations in traffic flow prediction models, low efficiency in intelligent transportation big data mining, insufficient prediction accuracy, and weak generalization capabilities, this study focuses on the core spatiotemporal characteristics of traffic flow—time periodicity, volatility, spatial correlation, and aggregation—while incorporating the "5V" features of multi-source heterogeneous big data in intelligent transportation. It conducts research on big data mining and prediction models. First, it identifies and quantifies traffic flow spatiotemporal characteristics to establish a targeted quantitative indicator system. Second, it designs a full-process big data mining framework to complete multi-source data preprocessing and spatiotemporal correlation feature extraction. Building on this, it improves and optimizes the model by introducing a spatiotemporal attention mechanism based on LSTM and GCN algorithms, constructing a traffic flow prediction model that integrates spatiotemporal characteristics. Finally, the model's performance is validated through case studies. The results demonstrate that the proposed model significantly outperforms traditional models, with a minimum MAPE of 4.87%, a single-prediction time of 0.32 seconds, and strong real-time adaptability and scenario compatibility. This study enriches the theory of spatiotemporal analysis and big data integration in traffic flow, providing scientific decision-making support for intelligent transportation management and road network optimization, with important theoretical and practical application value.