Data Analysis of Urban Intersection Based on Historical Traffic

Intersections are the key node in the urban road network, so reasonable channelization at intersections is key for improving traffic efficiency of the entire urban network. However, traffic data of intersections that has been collected so far has great volatility and abnormality, which cannot provide an accurate data basis for further intersection optimization. This paper is based on historical traffic data of intersections for data processing and short-term traffic forecasting. First, the historical data is preprocessed by a time series method and short-term traffic prediction method to recover the missing data. We then performed short-term traffic forecasting based on SPSS and used an expert modeling method and ARIMA forecasting method to predict short-term traffic. After pretreatment, we performed time division of traffic data using the K-means clustering algorithm. Through the above methods, traffic data can be improved to provide accurate data support for intersection optimization.