In recent years, as an important means to improve urban operation efficiency, intelligent transportation systems have been gradually surpassing traditional traffic management models. With the advancement of artificial intelligence and big data technologies, intelligent transportation realizes traffic flow prediction and management through intelligent traffic signal lights, real-time vehicle monitoring, and data analysis, demonstrating strong adaptability and intelligent characteristics. This study constructs a smart city transportation planning decision model based on the decision tree algorithm, building a dynamic decision tree structure by integrating multi-source traffic data (flow, weather, events, etc.). Targeting the static limitations of traditional planning strategies, an intelligent decision framework with real-time perception and dynamic optimization capabilities is proposed. Experimental results indicate that this model improves traffic flow prediction accuracy by 18%, enhances intersection efficiency by 23% through dynamic signal light timing optimization, and shortens accident emergency response time by 40%. The research provides a data-driven decision support paradigm for smart city transportation systems, effectively balancing the supply and demand of road resources and promoting the intelligent transformation of urban traffic management.
Research on smart city transportation planning strategy based on decision tree algorithm
Second International Conference on Intelligent Transportation and Smart Cities (ICITSC 2025) ; 2025 ; Luoyang, China
Proc. SPIE ; 13682 ; 1368203
2025-06-18
Conference paper
Electronic Resource
English
Transportation and city planning
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