摘要
为在先验数据有限情况下较精确地预测道路换道次数,提出基于神经网络与马尔可夫链的组合预测模型。采集路段区间平均车速和车流密度,采用BP神经网络训练初步拟合模型;运用马尔可夫链方法,进一步给出换道次数在表示高估、正常、低估的3组区间内的分布及概率,改善BP神经网络误差。运用组合预测模型对西安市某道路的换道次数进行了预测分析,结果表明,实际换道次数均在模型给出的较大概率的预测区间内,表明模型能够根据路段区间平均车速和车流密度较好地预测换道次数。
In order to predict more accurately the frequencies of lane changes with the limited data, a combined forecasting model based on neural network and Markov chain was proposed. The average speed and density of road sections were collected, and the BP neural network model was used to train the preliminary fitting model. By using Markov chain method, the distribution and probability of lane changing frequencies in three groups of intervals representing overestimation, normal and underestimation were given to decrease BP neural network error. The combination forecasting model was adopted to predict and analyze the frequency of lane-changing of a road in Xi'an. The results show that the actual frequencies of lane changing are all within the prediction range of the maximum probability given by the model, which indicates that the model can predict the frequency of lane changing according to the average speed and traffic density of the section.
作者
洪维伟
王元庆
Hong Weiwei;Wang Yuanqing(School of Highway, Chang’an University, Xi’an 710064, China)
出处
《华东交通大学学报》
2019年第2期92-98,104,共8页
Journal of East China Jiaotong University
基金
国家自然科学基金项目(51878062)
关键词
换道行为
换道次数
神经网络
马尔可夫链
lane changing behavior
lane changing frequency
neural network
Markov chain
