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In this study, we investigated an alternative technique to automatically classify road traffic congestion with high accuracy aligning with travelers' opinions. The method utilized an intelligent traffic camera system orchestrated with a web survey system to collect the traffic conditions and travelers' opinions. A large number of human perceptions were used to train the artificial neural network (ANN) model that classify velocity and traffic flow into three congestion levels: light, heavy, and jam. The learning parameters were heuristically optimized to gain highest prediction accuracy. The outcomes were a practical method and the model achieving as high as 94.99% accuracy. The model was then compared to the Occupancy Ratio (OR) technique, currently in service in the Bangkok Metropolitan Area. The comparison indicated that our model could determine the traffic congestion levels 12.15% more accurately than the existing system. The analysis revealed that the derived model classified congestion levels based mainly on the vehicle velocity, suggesting that the model could be modified and broadly used with various types of traffic sensors. The methodology, though conceived for use in Bangkok, is a general Intelligent Transportation System (ITS) practice that can be applied to any part of the world. [ABSTRACT FROM AUTHOR]

We proposed a technique to identify road traffic congestion levels from velocity of mobile sensors with high accuracy and consistent with motorists' judgments. The data collection utilized a GPS device, a webcam, and an opinion survey. Human perceptions were used to rate the traffic congestion levels into three levels: light, heavy, and jam. Then the ratings and velocity were fed into a decision tree learning model (J48). We successfully extracted vehicle movement patterns to feed into the learning model using a sliding windows technique. The model achieved accuracy as high as 91.29%. By implementing the model on the existing traffic report systems, the reports will cover on comprehensive areas. The proposed method can be applied to any parts of the world. [ABSTRACT FROM AUTHOR]