基于模糊C均值聚类和随机森林的短时交通状态预测方法

Short-term Traffic State Prediction Approach Based on FCM and Random Forest

交通拥堵长期以来是城市面临的主要问题之一,解决交通拥堵瓶颈刻不容缓。准确的短时交通状态预测有利于市民预知交通出行信息,及时采取措施避免陷入拥堵困境。该文提出一种基于模糊C均值聚类(FCM)和随机森林的短时交通状态预测方法。首先,利用一种新颖的融合时空信息的自适应多核支持向量机(AMSVM)来预测短时交通流参数,包括流量、速度和占有率。其次,基于FCM算法分析历史交通流,获取历史交通状态信息。最后,利用随机森林算法分析所预测的短时交通流参数,得到最终预测的短时交通状态。该方法在融合时空信息的同时采用随机森林算法应用于短时交通状态预测这一全新的研究领域。实验结果表明,FCM对历史交通状态的评估方式适用于不同的高速路和城市道路场景。其次,随机森林比其它常见的机器学习方法具有更高的预测精度,从而提供实时可靠的短时交通出行信息。

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.