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Analysis of Headway and Speed Based on Driver Characteristics and Work Zone Configurations Using Naturalistic Driving Study Data
Transportation Research Record: Journal of the Transportation Research Board ( IF 1.6 ) Pub Date : 2021-09-14 , DOI: 10.1177/03611981211015261
Dan Xu 1 , Chennan Xue 1 , Huaguo Zhou 1
Affiliation  

The objective of this paper is to analyze headway and speed distribution based on driver characteristics and work zone (WZ) configurations by utilizing Naturalistic Driving Study (NDS) data. The NDS database provides a unique opportunity to study car-following behaviors for different driver types in various WZ configurations, which cannot be achieved from traditional field data collection. The complete NDS WZ trip data of 200 traversals and 103 individuals, including time-series data, forward-view videos, radar data, and driver characteristics, was collected at four WZ configurations, which encompasses nearly 1,100 vehicle miles traveled, 19 vehicle hours driven, and over 675,000 data points at 0.1 s intervals. First, the time headway selections were analyzed with driver characteristics such as the driver’s gender, age group, and risk perceptions to develop the headway selection table. Further, the speed profiles for different WZ configurations were established to explore the speed distribution and speed change. The best-fitted curves of time headway and speed distributions were estimated by the generalized additive model (GAM). The change point detection method was used to identify where significant changes in mean and variance of speeds occur. The results concluded that NDS data can be used to improve car-following models at WZs that have been implemented in current WZ planning and simulation tools by considering different headway distributions based on driver characteristics and their speed profiles while traversing the entire WZ.



中文翻译:

使用自然驾驶研究数据基于驾驶员特征和工作区配置分析车头时距和速度

本文的目的是利用自然驾驶研究 (NDS) 数据分析基于驾驶员特征和工作区 (WZ) 配置的车头时距和速度分布。NDS 数据库提供了一个独特的机会来研究各种 WZ 配置下不同驾驶员类型的跟车行为,这是传统的现场数据收集无法实现的。在四种 WZ 配置下收集了 200 次遍历和 103 个人的完整 NDS WZ 行程数据,包括时间序列数据、前视视频、雷达数据和驾驶员特征,包括近 1,100 车辆英里行驶,19 车辆小时驾驶,以及超过 675,000 个数据点,间隔为 0.1 秒。首先,根据驾驶员的性别、年龄组、和风险认知来制定进度选择表。此外,建立了不同 WZ 配置的速度曲线以探索速度分布和速度变化。车头时距和速度分布的最佳拟合曲线由广义加性模型 (GAM) 估计。变化点检测方法用于识别速度均值和方差发生显着变化的位置。结果得出结论,NDS 数据可用于改进 WZ 的跟驰模型,这些模型已在当前 WZ 规划和仿真工具中实施,通过考虑基于驾驶员特征及其速度分布的不同车头时距分布,同时穿越整个 WZ。建立了不同 WZ 配置的速度曲线,以探索速度分布和速度变化。车头时距和速度分布的最佳拟合曲线由广义加性模型 (GAM) 估计。变化点检测方法用于识别速度均值和方差发生显着变化的位置。结果得出结论,NDS 数据可用于改进 WZ 的跟驰模型,这些模型已在当前 WZ 规划和仿真工具中实施,通过考虑基于驾驶员特征及其速度分布的不同车头时距分布,同时穿越整个 WZ。建立了不同 WZ 配置的速度曲线,以探索速度分布和速度变化。车头时距和速度分布的最佳拟合曲线由广义加性模型 (GAM) 估计。变化点检测方法用于识别速度均值和方差发生显着变化的位置。结果得出结论,NDS 数据可用于改进 WZ 的跟驰模型,这些模型已在当前 WZ 规划和仿真工具中实施,通过考虑基于驾驶员特征及其速度分布的不同车头时距分布,同时穿越整个 WZ。变化点检测方法用于识别速度均值和方差发生显着变化的位置。结果得出结论,NDS 数据可用于改进 WZ 的跟驰模型,这些模型已在当前 WZ 规划和仿真工具中实施,通过考虑基于驾驶员特征及其速度分布的不同车头时距分布,同时穿越整个 WZ。变化点检测方法用于识别速度均值和方差发生显着变化的位置。结果得出结论,NDS 数据可用于改进 WZ 的跟车模型,这些模型已在当前 WZ 规划和仿真工具中实施,通过考虑基于驾驶员特征及其速度分布的不同车头时距分布,同时穿越整个 WZ。

更新日期:2021-09-14
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