There are many traffic information services that provide realtime road speed for road users to inquire, informing them whether the traffic is in congested status or not. However, it is not enough to provide only current information in many circumstances. We believe that the convenience of traffic information for road users can be improved by providing prediction results. Our research aims at collecting and analyzing traffic data through statistical method and raising architectures for congestion detection and prediction. With the history trend of each road, it is possible to foresee appearances and durations of incoming congestions. Validated by historical data, the measurement of accuracy shows that over 94% of congestion can be predicted properly. It also revealed that the MAE(Mean Absolute Error) and MAPE(Mean Absolute Percentage Error) of proposed method outperform the results generated by simple average method.
Traffic Congestion Traffic Prediction Incident Detection Recurrent Congestion Speed Trend