摘要
从交通流扩散的特点和人的先验知识出发,提出采用Kriging插值法对路网中无检测器路段进行交通数据插补。基于交通数据空间相关性的特征,对交通数据进行空间建模,从而以空间距离作为度量基准对未知路段交通数据进行估计。利用南昌市浮动车系统中提取的路段行程速度作为试验数据,进行了试验验证。研究结果表明：在城市交通中各个典型时段行程速度的插补值标准差可以控制在8 km·h-1以内;在针对路网形态差异较大的中心区和湖区分别进行的试验中,行程速度的平均绝对误差都保持在2-5 km·h-1之间。可见,该方法具有良好的时态和区域移植性。
From the diffused characteristic of traffic flow and prior knowledge,Kriging interpolation was adopted to interpolate the traffic data of non-detection road link.Based on the spatial correlation of traffic data,a spatial model of traffic data was built.The spatial distance was adopted as metric to estimate the unsampled traffic data of road link. The road link travel speeds of Nanchang＇s road network were used as experiment data,which were collected from urban floating car system,and the method was verified.Experiment result shows that the standard errors of speed interpolations are always lower than 8 km·h-1 in different urban traffic time periods.Downtown zone and lake zone have different road network structures,and their mean absolute errors of speed interpolations are 2-5 km·h-1.So the method has good temporal and regional portabilities.3 tabs,7 figs,13 refs.
出处
《交通运输工程学报》
EI
CSCD
北大核心
2011年第3期118-126,共9页
Journal of Traffic and Transportation Engineering
基金
国家自然科学基金项目(40830530
60872132)
香港研究资助局项目(754109)
微软亚洲研究院开放基金项目(FY10-RES-THEME-019)
测绘遥感信息工程国家重点实验室开放基金项目
关键词
智能交通系统
交通地理信息系统
交通数据分析
浮动车
KRIGING插值
空间相关性
intelligence transportation system
geographic information system for transportation
traffic data analysis
floating car
Kriging interpolation
spatial correlation
