基于特性和影响因素分析的短时交通流预测

Short-term Traffic Flow Forecasting Based on Analysis of Characteristics and Impact Factors

可靠的短时交通流预测是智能交通系统的重要基础。为了提高短时交通流预测的预测精度和对于不同交通状态的适应性,在分析了交通流特性以及时空二维影响因素的基础上,提出了一种组合预测模型,使其能够综合反映这些特性和影响因素。该组合预测模型包括时间序列模块、空间相关模块和组合预测模块三个子模块。单项预测模型包括自适应单指数平滑模型和RBF神经网络模型,组合系数是以两个单项预测子模块的平滑百分比相对误差作为输入,以神经网络作为学习算法自适应地得到。最后通过平峰和高峰时段实测的交通流量数据来验证模型的有效性和可靠性,结果表明:该组合预测模型的预测精度高于单项预测模型各自单独使用时的精度,且对于不同的交通流状况具有较好的适应性。

Reliable short-term traffic flow forecasting is an important foundation for the intelligent transportation system. In order to improve the accuracy of the short-term traffic flow forecasting and increase its adaptability in different traffic states,a combination forecasting model based on the analysis of traffic flow characteristics and space-time two-dimensional impact factors is presented to reflect the characteris- tics and influencing factors. The model has three sub-models.time-series model,space-related model and combination forecasting model. The single forecast models includes single adaptive exponential smooth- ing model and RBF neural network model. The combination coefficient is obtained adaptively based on the smoothing percentage relative error of the two single forecast sub-modules as input by using the neu- ral network as a learning algorithm. Finally,the traffic flow data are measured respectively in flat peak and peak hours to verify the validity and reliability of the model. The results show that the combination model can produce more precise forecasting than that of two individual models and adapt to different traffic states better.