Research And Application Of Random Forest Algorithm In Traffic State Prediction

One of the main problems in the transportation field is traffic state prediction,which provides strong support for controlling urban traffic.With the rapid development of communication technology and the times,people have more and more ways to travel,but car travel is still the choice of most people.This also makes traffic state prediction particularly important in the transportation field.Among them,the problem of mining from a large amount of traffic flow data to information that is beneficial to traffic prediction has received widespread attention.By September 2020,my country’s car ownership has reached 275 million vehicles,and my country’s medium-sized cities generate more than hundreds of T traffic flow data every day.Faced with these data,it is necessary to find efficient data processing and rapid prediction of traffic conditions.An important step in traffic control.This article analyzes and predicts traffic data through the combination of Hadoop and random forest algorithm.Hadoop is a distributed system architecture.Its main design framework is HDFS and Map Reduce,which provide data storage and computing functions for this research.The advantage of the random forest algorithm is that it can process multidimensional data and has high classification accuracy.So,in this article,we will explain in detail how the random forest algorithm works,and optimizes and improves the algorithm in the process to improve the accuracy of the prediction.This article mainly studies the following:First of all,this article elaborates on the domestic and foreign research status of Hadoop platform and machine forest algorithm,as well as the current main forecasting methods of traffic flow forecasting.Then introduce the operating mechanism of Map Reduce and the combination of Map Reduce and random forest algorithm to run on the Hadoop platform for prediction.Finally,this combined calculation method is compared with the random forest algorithm.Secondly,there may be data redundancy or loss in the process of obtaining data,which will bring inaccuracy or even errors to the subsequent prediction.To solve this problem,this paper performs data preprocessing during the Map Reduce calculation to repair or simplify the data.Finally,study the calculation principle of the random forest algorithm,find the best split point when constructing the decision tree,and optimize the algorithm to achieve better traffic flow prediction results.