Observation and Modeling of Variability in Car-Following Behavior

跟车行为变异性的观察和建模

• 批准号: 0201505
• 负责人: David Lovell
• 金额: $ 14.36万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-07-15 至 2005-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0201505&HistoricalAwards=false
• 关键词: Observation; Modeling; Variability; Car; Following

This study aims to observe and analyze the car-following behavior of subjects who do not know they are part of an experiment, and to develop a model that can explain the stochastic effects in car-following behavior across and within drivers, as well as those caused by variability of critical factors such as human characteristics, traffic and road characteristics, and environmental characteristics. This is a significant improvement over previous empirical studies, conducted sparingly by pairs of trained professional traffic engineers on closed test tracks. The results of this project should contribute significantly to the development of more realistic microscopic car-following laws that incorporate variation across and within individual drivers. A combination of inertial, GPS, infrared sensing, and video equipment will be used to collect the data. The lead (control) vehicle will be driven by an experimenter, but it is the behavior of the follower (random members of the driving population) that will be observed. Video will be used for manual confirmation of recorded events, as well as to record certain characteristics that might play a statistically relevant role, including human, traffic and road, and environmental parameters.

本研究的目的是观察和分析不知道自己是实验一部分的受试者的跟车行为，并开发一个模型来解释驾驶员之间和驾驶员内部的跟车行为的随机效应，以及由人的特征、交通和道路特征、环境特征等关键因素的变异性引起的跟随行为的随机效应。与之前的经验研究相比，这是一个显著的改进，以前的经验研究是由两对训练有素的专业交通工程师在封闭的测试赛道上进行的。这一项目的成果将大大有助于开发更现实的微观跟车规律，将不同司机之间和个体内部的差异纳入其中。将使用惯性、GPS、红外传感和视频设备的组合来收集数据。领头(控制)车辆将由实验者驾驶，但将观察追随者(驾驶人群中的随机成员)的行为。视频将用于手动确认记录的事件，以及记录可能起到统计相关作用的某些特征，包括人、交通和道路以及环境参数。