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Analysis of Vehicle-Following Behavior in Mixed Traffic Conditions using Vehicle Trajectory Data
Transportation Research Record: Journal of the Transportation Research Board ( IF 1.6 ) Pub Date : 2020-09-16 , DOI: 10.1177/0361198120949874
Madhuri Kashyap N. R. 1 , Bhargava Rama Chilukuri 2 , Karthik K. Srinivasan 2 , Gowri Asaithambi 1
Affiliation  

In mixed traffic streams without lane discipline, driving behaviors are complex and difficult to model. However, limited attempts have been made to study the characteristics of these maneuvers using trajectory data. This paper proposes a novel use of vehicle trajectory data to identify car–car and auto–car pairs in the following regime and the regime duration, classify pairs as strict and staggered following, and investigate the factors influencing the following vehicle’s speed under different regimes in mixed traffic. Oblique trajectories and relative speed hysteresis plots are used to identify vehicle pairs in the steady-state following regime. Two new variables, oblique spacing (R) and the angle between the leader and the follower (θ), are proposed. Multiple linear regression models for the follower speed in strict and staggered following regimes are developed. The results show that cars exhibit following behavior more often than other vehicles. Also, while car–car pairs display both left and right staggered following, auto–car pairs predominantly demonstrate left staggered following. Regression analysis shows that the relationship between R and the speed of the following vehicle differs significantly when θ is close to 90° than when it deviates from 90°. The speed of followers is affected by leader and relative speeds. However, the relative speed has a smaller influence in both right and left staggered cases than strict follower cases. Finally, this study provides empirical evidence of qualitative and quantitative differences among following behaviors that can help in developing better microscopic traffic flow models for mixed traffic conditions.



中文翻译:

利用车辆轨迹数据分析混合交通条件下的跟车行为

在没有车道约束的混合交通流中,驾驶行为很复杂并且很难建模。然而,已经进行了有限的尝试来使用轨迹数据来研究这些动作的特性。本文提出了一种新颖的运用车辆轨迹数据来识别后续方案和方案持续时间中的汽车对和汽车对,将对分为严格和交错跟随的车辆,并研究在不同方案下影响后续车速的因素。混合流量。倾斜轨迹和相对速度磁滞曲线用于识别稳态跟随状态下的车辆对。提出了两个新变量,即斜距(R)和引导者与跟随者之间的角度(θ)。针对严格和交错跟随状态下的跟随者速度,建立了多个线性回归模型。结果表明,与其他车辆相比,汽车更容易表现出跟随行为。此外,虽然汽车对显示左右交错的跟随,但汽车对主要显示左交错的跟随。回归分析表明,当θ接近90°时,R与跟随车辆的速度之间的关系与偏离90°时的关系显着不同。跟随者的速度受领导者和相对速度的影响。但是,在左右交错的情况下,相对速度的影响比严格的跟随者情况小。最后,

更新日期:2020-09-16
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