Research on Smart City Traffic Management System Based on Big Data Fusion

doi:10.19301/j.cnki.zncs.2025.06.011

Abstract：The article designs a smart city traffic management system based on big data fusion. Real-time traffic data is collected through the Gaode API and Python crawler, then cleaned and converted into a unified format. Hive is used to store the data and support SQL queries. The MapReduce processing framework is adopted for distributed computing, and the Echarts visualization library is combined to achieve dynamic display of multi-dimensional data such as traffic conditions, vehicle distribution, and weather impact. The system not only provides real-time traffic information but also supports signal light timing optimization and road condition prediction functions. The results show that the system can efficiently integrate multi-source data, accurately analyze traffic situations, and significantly improve the level of intelligent traffic management. Through the visual interface, traffic managers can intuitively understand road conditions and make scientific decisions; the public can also obtain real-time information and plan their travel reasonably. System testing verifies its advantages in terms of functional completeness, data processing efficiency, and stability.