摘要
针对大规模复杂路网条件下基于浮动车数据的交通状态估计精度评价,开发了基于交通仿真软件VISSIM的实时仿真分析方法.以上海市陆家嘴地区的微观仿真路网为例,通过30组仿真实验分析了浮动车比例和数据采样频率对路网覆盖率和平均行程车速估计精度的影响.结果表明:随浮动车比例和采样频率的增加,平均行程车速估计精度与路网覆盖率逐渐提高,当浮动车比例为8%和采样频率为1/10s-1时达到最优.
This study developed a simulation-based approach to comprehensively evaluate the joint effects of penetration rate and uploading frequency of floating car data on traffic state estimation accuracy for the large-scale complex road networks. A case study based on the simulation model of Lujiazui Region in Shanghai was conducted to demonstrate the applicability of the developed approach and explore the optimal penetration rate and uploading frequency in terms of travel speed estimation accuracy and network coverage. Totally, 30 combinations of the two parameters were tested in the calibrated simulation model. Results show that the accuracy of travel speed estimation and network coverage rise as the penetration rate and uploading frequency increase. The optimal penetration rate and uploading frequency are found to be 8% and 1/10 s-1 respectively.
出处
《同济大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2014年第9期1347-1351,1407,共6页
Journal of Tongji University:Natural Science
基金
国家自然科学基金(51208380)
上海市浦江人才计划(12PJ1408500)
关键词
复杂路网
浮动车
交通状态估计
交通仿真
large-scale complex road networks
floating cardata
traffic state estimation
traffic simulation
