Abstract：Traffic congestion is a problem faced by cities, and it is urgent for solving this issue. Accurate short-term traffic state prediction is benefit for citizens to know the traffic information in advance, and take the measures in time to avoid the congestion. In this paper, a short-term traffic state prediction approach is proposed based on Fuzzy C-Means (FCM) clustering and Random Forest. Firstly, a novel Adaptive Multi-kernel Support Vector Machine (AMSVM) which incorporates the spatial-temporal information is used to predict the short-term traffic parameters, including the volume, the speed and the occupancy. Secondly, the historical traffic data are analyzed based on FCM algorithm, and the historical traffic state information is got. Lastly, the Random Forest (RF) algorithm is utilized to analyze the predicted short-term traffic parameters, then the final predicted short-term traffic state is obtained. This method incorporates the spatial-temporal information as well as applying the Random Forest to a new research field of short-term traffic state prediction. The experimental results demonstrate that the evaluation method of historical traffic state based on FCM is suitable for both freeway and urban road scenarios. Besides, the Random Forest has higher prediction accuracy than other common machine learning methods, thus providing the short-term traffic information timely and reliably.
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