【摘要】 为了全面、准确地分析路段行程时间的时空分布,将路段的时间序列和空间关联关系纳入两个邻近路段的行程时间可靠性预测过程.在时间维度上,通过广泛使用的卡尔曼滤波预测行程时间;在空间维度上,根据离散马尔科夫链构建上下游路段行程时间的关联模型.进而构建了时空贝叶斯模型(ST-BM),将时间维度和空间维度的行程时间分布进行融合,从而预测路段行程时间可靠性.实例分析结果表明,相比于先验分布数据,文中模型将两个实测邻近路段的可靠性预测误差分别降低了45.7%和29.2%,验证了ST-BM模型的有效性.更多还原

【Abstract】 In order to fully and accurately analyze the spatio-temporal distribution of link travel time,this paper incorporates the time sequence and the spatial relationship between two links into two adjacent links for travel time reliability prediction. From the temporal dimension,the widely-used Kalman filtering is adopted to predict the travel time; while from the spatial dimension,the correlation model describing the travel time between downstream and upstream links is built based on the discrete Markov chain model. Then,a spatio-temporal Bayesian model( STBM) is constructed to fuse the temporal and spatial travel time distributions for predicting the travel time reliability of road links. Case study results show that,as compared with the prior distributions,the proposed model decreases the reliability prediction error by 45. 7% and 29. 2% respectively for the two measured adjacent links,which verifies the effectiveness of the ST-BM model.更多还原