机构地区: 吉林大学
出 处: 《吉林大学学报(工学版)》 2013年第6期1459-1464,共6页
摘 要: 在分析非常态事件对道路交通影响的基础上,设计了适用于各类非常态事件的通用行程时间估计方法,并研究该方法实现的关键技术,包括GPS数据行程时间估计样本量判定;样本充足和不足情况下的GPS数据行程时间估计方法以及BP神经网络的行程时间融合方法。最后通过模拟重大交通事故和大雾两种非常态情况对本文方法进行验证,验证结果表明该方法可以更精确地对各类非常态下路段行程时间进行估计。 The influence of abnormal states on road traffic is analyzed. Then an estimation method of travel time feasible to different abnormal states is proposed. Some key technologies in this method are studied, including determination of sampling size based on GPS data, estimation of travel time with sufficient or insufficient samples based on GPS data, and BP neural network based travel time fusion method. Finally, the proposed method is verified by simulations under two abnormal states, one is serious traffic accident and the other one is dense fog. Simulation results show the proposed method can more accurately estimate the travel time under different abnormal states.