【摘要】 解决城市交通问题的关键在于优化调整交通出行结构以实现交通供需平衡,准确的进行交通需求分析是平衡供需矛盾的基础。本文通过分析城市经济结构发展与交通方式选择的相互关系,从横向和纵向两个层面提取交通方式选择的关键因素,构建描述城市交通出行方式选择行为的小波神经网络预测模型,利用关键因素之间的交互关系实现对出行方式的预测。在实际算例中,以北京市居民出行数据进行模型参数标定,验证了利用影响出行方式的关键因素及交互关系建立小波神经网络预测方法的可行性和精度。更多还原

【Abstract】 The balance between traffic demand and supply derived from optimizing traffic modal split is crucial to tackle urban traffic problems.To strike such a balance,traffic demand analysis plays an important role.Aiming at predicting travel modal split in urban traffic system,the interaction between urban economic structure and travel characteristics was analyzed and the key factors in such interaction were extracted from the horizontal and longitudinal dimensions respectively.With the interaction and the influential factors,taken into account a prediction model based on wavelet neural networks was proposed.The new model was calibrated and validated by using empirical data from the travel survey of Beijing city.The results show that the wavelet neural networks can give a reliable prediction of traffic modal split.3tabs,4figs,10 refs.更多还原