关键词: 交通工程, 多源数据融合, 短时交通流预测, 遗传算法, 小波神经网络

Abstract: Traffic data acquired by different types of traffic detectors contain different traffic information and traffic flow prediction plays an important role in traffic management and control. According to this, multi-source traffic data dynamic weighted fusion and short-term traffic flow forecasting were carried out. On the premise of synthetically analyzing the characteristics of multi-source data and the superiority of data fusion, combining with the advantages of global search of genetic algorithm and adaptive learning of wavelet neural network, a short-term traffic flow forecasting model based on multi-source data fusion and genetic wavelet neural network (GA-WNN) was put forward. The results of case study show that: the traffic data fusion method based on GA-WNN has more advantages than others, and the prediction accuracy of multi-source data fusion is better than that of single data source short-term traffic flow prediction sequence, which provides more precise and comprehensive traffic information for the decision-making of traffic managers and the choice of path for traffic travelers.

Key words: traffic engineering, multi-source data fusion, short-term traffic flow forecasting, genetic algorithm, wavelet neural network