CAREER: Dynamic State Transitions in Vehicular Traffic and the Effects of Driver Behavior
职业:车辆交通的动态状态转换和驾驶员行为的影响
基本信息
- 批准号:1439795
- 负责人:
- 金额:$ 30.66万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2013
- 资助国家:美国
- 起止时间:2013-09-01 至 2018-02-28
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
The objective of this Faculty Early Career Development (CAREER) program award is to investigate the effects of driver behaviors, such as car-following and lane-changing, on traffic state transitions (i.e., changes in traffic conditions such as speed). This research focuses on transitions near freeway choke points (i.e., bottlenecks), merges, and ends of queues (where vehicles encounter congestion). Transitions around these regions can lead to disorderly traffic conditions, such as stop-and-go driving, due to sharp changes in speed and frequent lane-changing maneuvers. This research consists of four primary tasks of (i) collecting new individual vehicle data, (ii) analyzing general features (e.g. changes in traffic flow, density, and speed) of traffic transitions, (iii) analyzing driver behaviors and linking them to the general features, and (iv) extending/developing models of driver behaviors capable of reproducing the observed transition phenomena.If successful, this research will uncover the physical mechanisms of transitions; i.e., how individual driver behaviors (e.g., car-following, lane-changing) govern changes in flow, density, and speed in time and space. Understanding these mechanisms has important implications for mobility and safety. Transitions around freeway bottlenecks can determine bottleneck throughput and hence the delays on freeway networks. Further, vehicles in transition regions are more prone to crashes due to sudden changes in speed and frequent lane-changing maneuvers. Therefore, the results from this research will potentially advance traffic control strategies to manage congestion more effectively and improve safety. Educational and outreach activities involve the development of a project website, a smart-phone application to collect probe data, web-based modules for hands-on learning of data analysis methods and modeling. Using these means, this project will engage undergraduate and graduate students, STEM (science, technology, engineering, and mathematics) high school teachers to reach out to high-school students in the long term, researchers in other disciplines, and industry stakeholders.
这个教师早期职业发展(CAREER)计划奖的目的是调查驾驶员行为的影响,如汽车跟随和变道,对交通状态转换(即,交通状况(如速度)的变化。本研究的重点是高速公路阻塞点附近的过渡(即,瓶颈)、汇合点和队列末端(车辆遇到拥堵的地方)。这些区域周围的过渡可能会导致无序的交通状况,如走走停停的驾驶,由于速度的急剧变化和频繁的变道机动。本研究包含四个主要任务:(i)收集新的个体车辆数据,(ii)分析总体特征(例如交通流量、密度和速度的变化),(iii)分析驾驶员行为并将其与一般特征联系起来,以及(iv)扩展/开发能够再现所观察到的过渡现象的驾驶员行为模型。如果成功,该研究将揭示转变的物理机制;即,个体驾驶员行为(例如,车辆跟随、车道变换)控制流量、密度和速度在时间和空间上的变化。 了解这些机制对机动性和安全性具有重要意义。围绕高速公路瓶颈的过渡可以确定瓶颈吞吐量,从而确定高速公路网络上的延误。此外,由于速度的突然变化和频繁的变道操纵,过渡区域中的车辆更容易发生碰撞。因此,这项研究的结果将有可能推进交通控制策略,以更有效地管理拥堵,提高安全性。教育和外联活动包括开发一个项目网站、一个收集探测数据的智能手机应用程序、用于实际学习数据分析方法和建模的网络模块。利用这些手段,该项目将吸引本科生和研究生,STEM(科学,技术,工程和数学)高中教师长期接触高中生,其他学科的研究人员和行业利益相关者。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Soyoung Ahn其他文献
A Deep Long Short-Term Memory Network Embedded Model Predictive Control Strategies for Car-Following Control of Connected Automated Vehicles in Mixed Traffic
混合交通中联网自动车辆跟车控制的深度长短期记忆网络嵌入式模型预测控制策略
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Yang Zhou;Zhen Zhang;Fan Ding;Soyoung Ahn;Keshu Wu;Bin Ran - 通讯作者:
Bin Ran
Electric bicycles sharing: opportunities and environmental impacts
电动自行车共享:机遇和环境影响
- DOI:
- 发表时间:
2022 - 期刊:
- 影响因子:0
- 作者:
Wissam Kontar;Soyoung Ahn;A. Hicks - 通讯作者:
A. Hicks
Human-automated vehicle interactions: Voluntary driver intervention in car-following
人机交互车辆:在跟车过程中驾驶员的自愿干预
- DOI:
10.1016/j.trc.2024.104969 - 发表时间:
2025-02-01 - 期刊:
- 影响因子:7.900
- 作者:
Xinzhi Zhong;Yang Zhou;Amudha Varshini Kamaraj;Zhenhao Zhou;Wissam Kontar;Dan Negrut;John D. Lee;Soyoung Ahn - 通讯作者:
Soyoung Ahn
Evaluating the Benefits of a System-Wide Adaptive Ramp-Metering Strategy in Portland , Oregon
评估俄勒冈州波特兰市全系统自适应斜坡计量策略的效益
- DOI:
- 发表时间:
2002 - 期刊:
- 影响因子:0
- 作者:
Soyoung Ahn - 通讯作者:
Soyoung Ahn
A Generic Stochastic Hybrid Car-following Model Based on Approximate Bayesian Computation
基于近似贝叶斯计算的通用随机混合跟车模型
- DOI:
10.48550/arxiv.2312.10042 - 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Jiwan Jiang;Yang Zhou;Xin Wang;Soyoung Ahn - 通讯作者:
Soyoung Ahn
Soyoung Ahn的其他文献
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{{ truncateString('Soyoung Ahn', 18)}}的其他基金
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 - 财政年份:2022
- 资助金额:
$ 30.66万 - 项目类别:
Standard Grant
CPS: TTP Option: Medium: Identifying, Characterizing, and Shaping Multi-Scale Cyber-Human Interactions in Mixed Autonomous/Conventional Vehicle Traffic
CPS:TTP 选项:中:识别、表征和塑造混合自主/传统车辆交通中的多尺度网络人机交互
- 批准号:
1739869 - 财政年份:2019
- 资助金额:
$ 30.66万 - 项目类别:
Continuing Grant
Collaborative Research: Mixed Traffic Dynamics Under Disturbances: Impact of Multi-Class Connected and Automated Vehicles
合作研究:干扰下的混合交通动态:多类互联和自动驾驶车辆的影响
- 批准号:
1932932 - 财政年份:2019
- 资助金额:
$ 30.66万 - 项目类别:
Standard Grant
Vehicular Traffic Modeling and Control in Mixed Manual and Automated Environments
混合手动和自动环境中的车辆交通建模和控制
- 批准号:
1536599 - 财政年份:2015
- 资助金额:
$ 30.66万 - 项目类别:
Standard Grant
CAREER: Dynamic State Transitions in Vehicular Traffic and the Effects of Driver Behavior
职业:车辆交通的动态状态转换和驾驶员行为的影响
- 批准号:
1150137 - 财政年份:2012
- 资助金额:
$ 30.66万 - 项目类别:
Standard Grant
Collaborative Research: Analysis and Modeling of Traffic Instabilities in Congested Traffic
协作研究:拥堵交通中的交通不稳定分析与建模
- 批准号:
0856699 - 财政年份:2009
- 资助金额:
$ 30.66万 - 项目类别:
Standard Grant
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