基于季节模型及Kalman滤波的道路行程时间  

Route travel time estimation based on seasonal model and Kalman filtering algorithm

作　　者：孙健[1] 张纯[1,2] 陈书恺 薛睿[2] 彭仲仁[1,3] 

机构地区：[1]上海交通大学船舶海洋与建筑工程学院海洋工程国家重点实验室,上海200240 [2]上海交通大学船舶海洋与建筑工程学院交通研究中心,上海200240 [3]佛罗里达大学城市与区域规划系,佛罗里达州盖恩斯维尔32601

出　　处：《长安大学学报（自然科学版）》2014年第6期145-151,共7页Journal of Chang’an University(Natural Science Edition)

基　　金：国家自然科学基金青年科学基金项目(71101109);长沙理工大学公路工程教育部重点实验室开放基金项目(kfj120108)

摘　　要：道路行程时间是影响城市交通出行行为的重要因素。当前大多数出行时间研究基于路段进行,假设驾驶人沿着理想最短路径或最快路径行驶,难以对交叉口排队延误等相关时间参数进行精确估计。针对城市任意OD间的出行时间进行分析,采用Kalman滤波方法,利用历史数据对总行程时间进行有效预测。鉴于总行程时间分布存在比较明显的周期性特点,单一Kalman滤波算法难以反映出这种周期性,引入基于季节模型的Kalman滤波算法进行建模和优化。最后,利用深圳浮动车2011年12月连续3d的数据进行实证。研究结果表明:相对于传统的SARIMA模型及普通Kalman滤波算法,优化模型同时考虑总行程时间分布的周期性和时变性,具有较小误差及更好的拟合度;所得预测时间的平均绝对误差(MAE)分别在传统SARIMA模型及普通Kalman滤波算法结果基础上降低了37%和52%,其余误差指标,如均方根误差(RMSE)及最大相对误差(MRE)均有较大下降,从而证明了研究模型的有效性。Travel time is a key factor affecting driving behavior in urban area. The majority of existing re- search focused on the link or section based travel time estimation and assumed that drivers generally choose the ideal shortest or fastest path. Hence, it＇s difficult to acquire accurate time estimation such as delays oc- curred in signalized intersections. This paper analyzes the travel time between OD pairs and estimates the total travel time based on historical data by using the Kalman filtering algorithm. Considering an ordinary Kalman filtering algorithm is not enough to capture the characteristic of periodicity, a seasonal Kalman filte- ring algorithm is proposed for the further modeling and optimization. Finally, the floating car data from three continuous days in December. 2011 （Shenzhen, China） were obtained for an empirical study. The re-sults indicate that in comparison to the traditional SARIMA time series model and the ordinary Kalman filte- ring algorithm, the proposed mode[ captures the periodicity and time variations of total travel time, and thus has higher accuracy and fitness. Compared to the results from SARIMA and the ordinary Kalman algo- rithms, the mean absolute errors （MAE） of the total travel time from the ordinary Kalman filtering predic- tions decrease 37~//oo and 52~//00, respectively. The other two error related indexes, namely, the root mean square error （RMSE） and the maximum relative error （MIRE） both decrease significantly, which conse- quently indicates the effectiveness of the proposed method, and further verifies the modelling capability of the seasonal Kalman filtering algorithm. 4 tabs, 5 figs, 15 refs.

关 键 词：交通工程 城市交通 总行程时间预测 季节时间序列 Kalman滤波算法 浮动车数据 

分 类 号：U491.1[交通运输工程—交通运输规划与管理]