Predicting the Next State of Traffic by Data Mining Classification Techniques

Subject Areas : International Journal of Smart Electrical Engineering

S.Mehdi  Hashemi ¹ (Department of Mathematical and Computer Science, Amirkabir University of Technology, Tehran, Iran)
Mehrdad  Almasi ² (Department of Computer Engineering, Isfahan University of Technology, Isfahan, Iran.)
Roozbeh  Ebrazi ³ (Department of Mathematical and Computer Science, Amirkabir University of Technology, Tehran, Iran)
Mohsen  Jahanshahi ⁴ (Young Researchers and Elite club, Central Tehran Branch, Islamic Azad University, Tehran, Iran.)

Received: 2015-04-13 Accepted : 2015-04-13 Published : 2012-09-01

Keywords: traffic prediction, Level of Service Prediction, Data mining, Naïve Bayesian, Random forest, Classification tree, CN2,

Abstract :

Traffic prediction systems can play an essential role in intelligent transportation systems (ITS). Prediction and patterns comprehensibility of traffic characteristic parameters such as average speed, flow, and travel time could be beneficiary both in advanced traveler information systems (ATIS) and in ITS traffic control systems. However, due to their complex nonlinear patterns, these systems are burdensome. In this paper, we have applied some supervised data mining techniques (i.e. Classification Tree, Random Forest, Naïve Bayesian and CN2) to predict the next state of Traffic by a categorical traffic variable (level of service (LOS)) in different short-time intervals and also produce simple and easy handling if-then rules to reveal road facility characteristic. The analytical results show prediction accuracy of 80% on average by using methods

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