Collaborative Research: New Methods for Measuring, Evaluating and Predicting the Safety Impact of Road Infrastructure Systems on Driver Behavior

合作研究：测量、评估和预测道路基础设施系统对驾驶员行为的安全影响的新方法

• 批准号: 0927138
• 负责人: Samer Hamdar
• 金额: $ 25万
• 依托单位: George Washington University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0927138&HistoricalAwards=false
• 关键词: Collaborative; Research; New; Methods; Measuring

In this collaborative research project, the investigators study the impact of roadway infrastructure on driver behavior and its implications on vehicle to vehicle interactions as well as on assessing macroscopic transportation network performance. Focusing on the geometric characteristics of highways and freeways and trying to understand how the road surrounding environment affect the aggressive driving behavior from a traffic flow theory perspective, better insight is given on the geometric effects on traffic operations, geometric effects on safety and effects of operations on safety. For that, the investigators adopt a unified traffic flow framework where the developed microscopic acceleration model and the macroscopic two-fluid model are based on utility maximization (choice) under risk. Explicit incorporation of risk attitudes and perception parameters in the corresponding models while understanding the influence of non-traffic related stimulus allows quantifying the safety implications of different roadway sections on driver behavior. In order to calibrate the traffic flow models that explain risk attitudes and perception based on road features, extensive data collection is undertaken. The investigators focus on three geographically diverse locations: Washington D.C. metropolitan area and the states of LA and CA. Crash data as well as highway infrastructure characteristics of the data collection sites are used for verifying the new approaches with the SEM (Structural Equation Method) modeling approach. The different research findings are used to develop surrogate safety measures on freeways and highways and to create a prototype microscopic simulation model capturing the impact of external non-traffic related characteristics on the different model parameters.From a broader perspective, transportation problems are characterized by finding the most efficient methods to move people/goods from an origin to a destination in a fast and safe manner. In vehicular traffic, these problems translate into avoiding road congestion and reducing human and physical losses due to traffic incidents. The major "players" in these problems are the driver/vehicle units (driver socio-demographic characteristics, vehicle properties, etc) and the driving environment (environmental conditions, traffic conditions, transportation infrastructure system characteristics, etc) surrounding such units. In the past years, understanding the impact of civil infrastructure systems on driver behavior, in particular the corresponding safety implications, have been limited to identifying "black-spots" and assessing qualitatively or statistically the safety measures that can be taken to avoid or respond to such incident scenarios. Nowadays, with the rising number of vehicles-miles travelled and the associated incidents, the need for substantial research in this area is no less important but with more generalized models helping answering important questions: what are the driver cognitive properties that are influenced by the road geometric features? Which socio-demographic driver characteristics and environmental/weather features play the major roles in such influence? What are the relationships between drivers? behavior and collective traffic patterns including congestion dynamics? How can weather-related and infrastructure-related parameters be incorporated in traffic flow models? These fundamental questions need to be answered in order to develop good infrastructure design strategies and engineering solutions for safer and efficient transportation systems. By improving traffic management control systems and considering risk-taking behavior, the resulting traffic flow models have tremendous value in evacuation management where risk associated to extreme and hazardous conditions affect driving behavior.

在这个合作研究项目中，研究人员研究了道路基础设施对驾驶员行为的影响及其对车与车之间相互作用的影响，以及对宏观交通网络性能评估的影响。着眼于高速公路和高速公路的几何特征，试图从交通流理论的角度理解道路周围环境是如何影响攻击性驾驶行为的，从而更好地了解交通运营的几何效应、安全的几何效应以及运营对安全的影响。为此，研究人员采用统一的交通流框架，其中建立的微观加速度模型和宏观双流体模型基于风险下的效用最大化（选择）。在理解非交通相关刺激的影响的同时，将风险态度和感知参数明确地纳入相应的模型，可以量化不同路段对驾驶员行为的安全影响。为了校准解释基于道路特征的风险态度和感知的交通流模型，进行了广泛的数据收集。调查人员将重点放在三个地理位置不同的地点：华盛顿特区大都会区以及洛杉矶和加利福尼亚州。碰撞数据以及数据收集地点的公路基础设施特征用于验证SEM（结构方程法）建模方法的新方法。不同的研究结果被用于制定高速公路和高速公路的替代安全措施，并创建一个原型微观模拟模型，以捕获外部非交通相关特征对不同模型参数的影响。从更广泛的角度来看，运输问题的特点是找到最有效的方法，以快速和安全的方式将人员/货物从出发地运送到目的地。在车辆交通中，这些问题转化为避免道路拥堵和减少交通事故造成的人员和物质损失。这些问题的主要“参与者”是驾驶员/车辆单元（驾驶员社会人口特征、车辆属性等）以及这些单元周围的驾驶环境（环境条件、交通条件、交通基础设施系统特征等）。在过去的几年里，了解民用基础设施系统对驾驶员行为的影响，特别是相应的安全影响，仅限于识别“黑点”，并定性或统计地评估可以采取的安全措施，以避免或应对此类事故情景。如今，随着车辆行驶里程和相关事故的不断增加，对这一领域进行大量研究的必要性同样重要，但需要更广义的模型来帮助回答一些重要问题：受道路几何特征影响的驾驶员认知特性是什么？哪些社会人口驱动特征和环境/天气特征在这种影响中起主要作用？司机之间的关系是什么？行为和集体交通模式包括拥塞动态？如何将与天气有关的参数和与基础设施有关的参数纳入交通流量模型？这些基本问题需要得到回答，以便为更安全和高效的交通系统制定良好的基础设施设计策略和工程解决方案。通过改进交通管理控制系统并考虑冒险行为，得出的交通流模型在极端危险条件下的风险影响驾驶行为的疏散管理中具有巨大的价值。