Paper Infomation
Forecast of Short-term Traffic Flow in Chengdu City Based on Decision Tree Model
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Author: Jingdian Yang
Abstract: With the rapid increase in the population in the central area of Chengdu, congestion in a certain period has become one of the stubborn diseases that restrict the development of the city. By accurately predicting the short-term road traffic information in the future, it can effectively improve the traffic efficiency of vehicles in a specific period. In this paper, data training is carried out according to the collected relevant indicators, and a decision tree classification model is established. The model can be used to predict the traffic flow on the road in a short period of time based on the time of the road, weather, average speed, and other indicators, and then the relevant regulatory authorities can use the advanced new media today to push, so that the citizens can plan their trips reasonably.
Keywords: Traffic Jam, Machine Learning, Decision Tree, Prediction
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