Naïve Bayes Classifier Based Traffic Prediction System on Cloud Infrastructure

As traffic congestion is becoming an everyday facing problem in urban region, traffic prediction and detection systems are playing an important role in city life. The road network sensors were popular in the previous systems. However, these technologies addressed to solve the installation and maintenance cost. Fortunately, the dramatic technology innovation is carrying many crucial solution for transportation agency to provide the relative services efficiently. This paper mainly emphasizes on detecting traffic condition by analyzing the behavior of vehicle primarily based on GPS mobile phone and history data. The system is built into two parts: Client and Cloud Server. On the Client side, the system distinguishes whether a phone carrier is taking a vehicle or walking. To analysis this situation, the Average Moving Filtering method are applied. On the Server side, it detects the traffic status based on checking vehicle's behavior based on the Client's result by applying Bayes Classifier.