Understanding and Harnessing Traffic Fundamental Diagram in the Era of Connected Automated Vehicles

理解和利用互联自动驾驶汽车时代的交通基本图

• 批准号: 2129765
• 负责人: Soyoung Ahn
• 金额: $ 42.31万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2129765&HistoricalAwards=false
• 关键词: Understanding; Harnessing; Traffic; Fundamental; Diagram

The traffic ‘fundamental diagram’ describes fundamental relationships among the key traffic variables of flow, density, and speed. It is used to understand traffic phenomena and develop traffic management strategies. The adoption of connected automated vehicles is expected to bring systematic changes to traffic flow patterns and increase the complexity of traffic dynamics. This requires a deeper understanding of how stochastic and non-static traffic flow properties are manifested in the fundamental diagram. To this end, this project aims to develop a new theoretical framework to enhance the fundamental diagram with stochastic and non-static dimensions. The framework will be empirically grounded and developed based on trajectory data of human-driven vehicles and vehicles with automation features (adaptive cruise control). The framework will also utilize computational models to characterize the stochastic behavior of these vehicles, particularly when the exact algorithm of adaptive cruise control is unknown. The anticipated outcome of this project includes a better understanding of and models to describe stochastic and non-static features of highly heterogeneous traffic flow. This project will also engage a range of educational, outreach, and dissemination activities, including (1) industry and transportation agency outreach, (2) local K-12 school outreach, (3) Engineering Expo participation, (4) development of educational modules using the developed simulation tools, (5) promoting diversities by recruiting students from underrepresented groups.The traditional fundamental diagram is a time-invariant and deterministic description for traffic conditions in steady states. Further, it represents the collective behavior of vehicles, often missing a connection to vehicle-level behavior important for some dynamic traffic phenomena. Thus, the traditional fundamental diagram is not well equipped to describe complex traffic dynamics in highly heterogeneous traffic. This research will: (1) construct a generic stochastic calibration framework based on a Bayesian approach to describe car-following behavior of a wide range of vehicles (e.g., human-driven and automated vehicles); and (2) establish a theoretical approximation framework to extend the traditional fundamental diagram to a non-static and stochastic version, based on the calibrated stochastic car-following models. The fundamental knowledge, models, and simulation tool from this research can lead to better design of automated vehicle control strategies and smart traffic management that will ultimately improve traffic throughput, stability, and resilience.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

交通“基本图”描述了流量、密度和速度等关键交通变量之间的基本关系。它被用来理解交通现象和制定交通管理策略。互联自动驾驶汽车的采用预计会给交通流模式带来系统性的变化，并增加交通动态的复杂性。这需要更深入地理解随机和非静态交通流特性是如何在基本图中表现出来的。为此，本项目旨在建立一个新的理论框架，以增强具有随机和非静态维度的基本图。该框架将以经验为基础，并基于人类驾驶车辆和具有自动化功能（自适应巡航控制）的车辆的轨迹数据进行开发。该框架还将利用计算模型来描述这些车辆的随机行为，特别是在自适应巡航控制的确切算法未知的情况下。该项目的预期结果包括更好地理解和模型来描述高度异构交通流的随机和非静态特征。该项目还将开展一系列教育、外展和传播活动，包括(1)工业和运输机构外展，(2)当地K-12学校外展，(3)参与工程博览会，(4)使用开发的模拟工具开发教育模块，(5)通过招募代表性不足的群体的学生来促进多样性。传统的基本图是对稳态交通状况的定常和确定性描述。此外，它代表了车辆的集体行为，通常缺少与车辆级行为的联系，这对一些动态交通现象很重要。因此，传统的基本图不能很好地描述高度异构交通中的复杂交通动态。本研究将：(1)构建一个基于贝叶斯方法的通用随机校准框架来描述各种车辆（如人类驾驶和自动驾驶车辆）的跟车行为；(2)基于标定的随机跟车模型，建立理论近似框架，将传统的基本图扩展为非静态的随机版本。本研究的基础知识、模型和仿真工具可以更好地设计自动车辆控制策略和智能交通管理，最终提高交通吞吐量、稳定性和弹性。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。