Application of Data Mining Techniques for Classification of Traffic Affecting Environments
Keywords:
Traffic, Data Mining, Decision Tree, K Nearest Neighbor, Support Vector MachineAbstract
This research aims to explore data mining techniques that is appropriate to classify traffic volume data and factors influencing on traffic. The traffic volume data was 31,147 records from the westbound traffic volume of MN DoT ATR station 301, roughly midway between Minneapolis and St Paul, MN. The data was retrieved from UCI Machine Learning Repository from 2014 to 2018. According to the experiment, Decision Tree (DT) is the technique that provide the highest accuracy of data classification of 79.57 percent, following with k Nearest Neighbors (k-NN) , accuracy of data classification of 73.27 percent with k=1 and Support Vector Machine (SVM) has the accuracy of data classification of 59.41 percent. Additionally, DT can identify that time is the most essential factor considering the traffic volume
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