摘要
根据时间序列中的自相关函数法,判断交通流量、时间占有率与平均速度的时间序列的平稳性。根据混沌分析中的G-P算法,将非平稳的交通流参数时间序列转化为平稳的交通流参数时间序列。引入了互相关系数的概念,在阻塞流状态下,计算了上游断面对观测断面以及观测断面对下游断面的互相关系数,并应用K-S检验判断阻塞流状态下城市快速路进出口匝道的车辆到达特性。研究结果表明:交通流量和时间占有率属于非平稳时间序列,平均速度属于平稳时间序列;当时间延迟分别取2、3、5min时,在阻塞流状态下,重构的交通流量相空间嵌入维数为4;观测断面的交通流参数不仅受相邻上游断面交通流参数传递的影响,而且也受相邻下游断面交通流参数回溯的影响;在阻塞流状态下,城市快速路进出口匝道车辆到达特性符合负二项分布。
Based on the auto-correlation function method of time sequence, the stationarities of time sequences for traffic flow, time occupancy and average speed were judged. Based on the G-P algorithm of chaos analysis, the non-stationary time sequence of traffic flow parameter was transformed to the stationary time sequence of traffic Flow parameter. The concept of cross- correlation coefficient was introduced. Under jam flow condition, the cross-correlation coefficients of upstream section on observation section and observation section on downstream section were calculated, and K-S test were used to determine the characteristics of vehicle arrival at import and export ramps on urban expressway. Research result shows that traffic flow and time occupancy belong to non-stationary time sequence, but average speed belongs to stationary time sequence. When time lags are 2, 3 and 5 min respectively, the embedding dimension of reconstruction phase space is 4 under jam flow condition. The traffic flow parameters of observation section is not only influenced by the traffic flow parameters transmission of adjacent upstream section, but also influenced by the traffic flow parameters backtrack of adjacent downstream section. Under jam flow condition, the characteristics of vehicle arrival at import and export ramps on urban expressway are accordance with the negative binomial distribution. 4 tabs, 13 figs, 13 refs.
出处
《交通运输工程学报》
EI
CSCD
北大核心
2012年第3期73-79,共7页
Journal of Traffic and Transportation Engineering
基金
国家自然科学基金项目(51178032)
北京交通大学优秀博士生科技创新基金项目(141082522)
中央高校基本科研业务费专项资金项目(CHD2011JC112
CHD2011JC156
CHD2012JC062)
关键词
交通工程
城市快速路
阻塞流
时间特性
空间特性
自相关函数
互相关系数
G-P算法
traffic engineering
urban expressway
jam flow
temporal characteristic
spatialcharacteristic
auto-correlation function
cross-correlation coefficient
G-P algorithm