This article demonstrates the use of traffic density observations collected dynamically in the vicinity of probe vehicles. Fixed position sensors cannot capture the longitudinal evolution of local traffic density in the corridor. In this research, dynamic traffic density observations were collected in a naturalistic driving setting that was free of any controlled experiment biases. Speed from global positioning system and space headway from a light detection and ranging module was collected on one arterial and one freeway segment, 2 and 4 mi long, respectively. The combined data frequency was approximately 3 Hz. Space headway was used to estimate the local density and consequently to identify the density of a specific location in a corridor. Besides, driver behavior was characterized using the relationship between instantaneous speed and local density under different regimes of the Wiedemann car-following model. Macroscopic traffic stream models were used to investigate the relationship between dynamically collected instantaneous speed and local density. Using the longitudinal evolution of density, precise local density across the corridor can be obtained along with the leader and follower trajectories. A method to identify driver behavior across density ranges was developed for different facility types using a microscopic relationship between instantaneous speed and local density. Overall driving behavior on the freeway segment can be represented by translating the instantaneous speed and local density relationship to macroscopic stream models.

本文演示了在探测车附近动态收集的交通密度观测值的使用。固定位置传感器无法捕获走廊中局部交通密度的纵向演变。在这项研究中，动态交通密度观测是在没有任何受控实验偏差的自然驾驶环境中收集的。来自全球定位系统的速度和来自光探测与测距模块的空间前进距离分别收集在一个动脉段和一个高速公路段上，分别长2英里和4英里。组合数据频率约为3 Hz。使用空间间距估计局部密度，从而确定走廊中特定位置的密度。除了，通过在Wiedemann汽车跟随模型的不同状态下的瞬时速度与局部密度之间的关系来表征驾驶员的行为。宏观交通流模型用于研究动态收集的瞬时速度和局部密度之间的关系。使用密度的纵向演变，可以获得整个走廊的精确局部密度以及引导者和跟随者的轨迹。利用瞬时速度和局部密度之间的微观关系，针对不同的设施类型，开发了一种在不同密度范围内识别驾驶员行为的方法。可以通过将瞬时速度和局部密度关系转换为宏观流模型来表示高速公路路段上的总体驾驶行为。