考虑车辆特征的分流区跟驰模型研究

为研究混合人工驾驶车辆(human-driven vehicle,HV)与网联自动驾驶车辆(connected and autonomous vehicle,CAV)交通流环境下不同特征车辆混入下高速路分流区的通行效率与行车安全性的提升问题[1],深入分析不同特征车辆混入造成的影响十分必要。基于此,文章以L3层级自动驾驶货车为研究对象,选取3组特征车辆,在考虑混分流(L0~L5)的影响下,选定并设置高速路匝道入口作为研究场景,引入车路协同系统[2],纳入交通参与者行为差异的多样性并加以改进。在所建立的混合交通流微观仿真模型基础上,联合SUMO(Simulation of Urban Mobility)与Python搭建仿真平台,实现驾驶行为模型的仿真验证与高速路中包含基本路段和分流区的混合交通流场景下的仿真试验[3],基于试验数据从交通效率与安全角度分析不同车辆特征对交通流特性的影响及改进跟驰模型的效果。

Measuring speed consistency for freeway diverge areas using factor analysis

Although either absolute speed or speed difference can be considered as a measure for speed consistency, few researches consider both in practice. The factor analysis method was introduced to extract an optimal number of factors from numerous original measures. The freeway diverging zone was divided into four elements, namely the upstream, the diverge area, the downstream and the exit ramp. Operating speeds together with individual vehicle speeds were collected at each element with radar guns. Following the factor analysis procedure, two factors, which explain 96.722% of the variance in the original data, were retained from the initial seven speed measures. According to the loadings after Varimax rotation, the two factors are clearly classified into two categories. The first category is named "speed scale" reflecting the absolute speed, and the other one is named "speed dispersion" interpreting speed discreteness. Then, the weighted score of speed consistency for each diverge area is given in terms of linear combination of the two retained factors. To facilitate the level classification of speed consistency, the weighted scores are normalized in the range of (0, 1.0). The criterion for speed consistency classification is given as 0≤F N <0.30, good consistency; 0.30≤F N <0.60, fair consistency; 0.60≤ F N ≤1.00, poor consistency. The validation by comparing with previously developed measures shows that the proposed measure is acceptable in evaluating speed consistency.