CAREER: Using Mobile Sensors for Traffic Knowledge Extraction and Dynamic Network Management
职业:使用移动传感器进行交通知识提取和动态网络管理
基本信息
- 批准号:1055555
- 负责人:
- 金额:$ 40万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Continuing Grant
- 财政年份:2011
- 资助国家:美国
- 起止时间:2011-05-01 至 2017-02-28
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
The objective of the Faculty Early Career Development (CAREER) program award is to develop the foundations for a new area of transportation science that takes advantage of, and addresses the challenges of using mobile traffic sensors for traffic knowledge extraction and dynamic system management. This research will study the fundamental questions of how to use widely-deployed mobile sensors for (i) estimating/predicting traffic states on arterial networks; (ii) discovering new traffic knowledge and behaviors under normal and post-disaster situations; and (iii) developing path-based dynamic network management strategies utilizing the knowledge obtained from mobile sensors. This research will develop a mobile-data-based modeling framework that will enrich current fixed-location-sensor-based modeling techniques. It will also develop theories, models, and algorithms for path-based dynamic network management strategies, with explicit consideration of multiple equilibria and real time information from mobile sensors, which will bridge various fields such as optimization, network modeling, and control theory.This research project is expected to transform current traffic data collection and modeling from a fixed-location-sensor-based to a mobile-sensor-based paradigm and will help policy makers and industry make informed decisions on how mobile data can be best used for knowledge extraction and traffic system management. This project will develop new educational tools by integrating project-based learning in the civil engineering curriculum and will develop short courses and research seminars that will foster the communication among academia, government agencies, and industry. This work will conduct outreach activities by disseminating research and education outcomes to a wide audience that includes students in universities and K-12 schools, and in particular students in minority universities. This project will also use an external advisory panel with members from multidisciplinary backgrounds so that the research can address a broad set of questions and apply the results to other fields beyond transportation.
教师早期职业发展(Career)计划奖的目标是为交通科学的新领域奠定基础,该领域利用并解决使用移动交通传感器进行交通知识提取和动态系统管理的挑战。本研究将研究如何使用广泛部署的移动传感器的基本问题:(i)估计/预测主干道网络上的交通状态;(ii)在正常和灾后情况下发现新的交通知识和行为;(iii)利用从移动传感器获得的知识开发基于路径的动态网络管理策略。这项研究将开发一个基于移动数据的建模框架,这将丰富当前基于固定位置传感器的建模技术。它还将开发基于路径的动态网络管理策略的理论、模型和算法,明确考虑多个均衡和来自移动传感器的实时信息,这将连接各个领域,如优化、网络建模和控制理论。该研究项目有望将当前的交通数据收集和建模模式从基于固定位置传感器的模式转变为基于移动传感器的模式,并将帮助政策制定者和行业就如何将移动数据最佳地用于知识提取和交通系统管理做出明智的决策。该项目将开发新的教育工具,将基于项目的学习整合到土木工程课程中,并将开发短期课程和研究研讨会,以促进学术界、政府机构和工业界之间的交流。这项工作将开展外联活动,向包括大学和K-12学校的学生,特别是少数民族大学的学生在内的广泛受众传播研究和教育成果。该项目还将使用一个由多学科背景的成员组成的外部咨询小组,以便研究能够解决广泛的问题,并将研究结果应用于交通运输以外的其他领域。
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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Xuegang Ban其他文献
Analysis of Differences in ECG Characteristics for Different Types of Drivers under Anxiety
不同类型驾驶员焦虑状态心电图特征差异分析
- DOI:
10.1155/2021/6640527 - 发表时间:
2021-08 - 期刊:
- 影响因子:1.8
- 作者:
Yongqing Guo;Xiaoyuan Wang;Qing Xu;Quan Yuan;Chenglin Bai;Xuegang Ban - 通讯作者:
Xuegang Ban
Real-time route diversion control in a model predictive control framework with multiple objectives: Traffic efficiency, emission reduction and fuel economy
模型预测控制框架中的实时路线改道控制具有多个目标:交通效率、减排和燃油经济性
- DOI:
10.1016/j.trd.2016.08.013 - 发表时间:
2016-10 - 期刊:
- 影响因子:0
- 作者:
Lihua Luo;Ying-En Ge;Fangwei Zhang;Xuegang Ban - 通讯作者:
Xuegang Ban
Simulation of Carbon Emission for Heavy-Duty Vehicle Queuing Systems
重型车辆排队系统碳排放仿真
- DOI:
- 发表时间:
2017 - 期刊:
- 影响因子:0
- 作者:
Yao Yu;Jialin Zhai;Xuegang Ban;Jinxian Weng - 通讯作者:
Jinxian Weng
Correcting the Market Failure in Work Trips with Work Rescheduling: An Analysis Using Bi-level Models for the Firm-workers Interplay
通过工作重新安排来纠正工作旅行中的市场失灵:使用双层模型进行企业-工人相互作用的分析
- DOI:
10.1007/s11067-013-9213-7 - 发表时间:
2013 - 期刊:
- 影响因子:2.4
- 作者:
Wilfredo F. Yushimito;Xuegang Ban;J. Holguín - 通讯作者:
J. Holguín
The Emergence Characteristics of Driver’s Intentions Influenced by Different Emotions
不同情绪影响驾驶员意图的显现特征
- DOI:
10.3390/su132313292 - 发表时间:
2021-12 - 期刊:
- 影响因子:0
- 作者:
Xiaoyuan Wang;Yongqing Guo;Chenglin Bai;Quan Yuan;Shanliang Liu;Xuegang Ban - 通讯作者:
Xuegang Ban
Xuegang Ban的其他文献
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{{ truncateString('Xuegang Ban', 18)}}的其他基金
Collaborative Research: Data Poisoning Attacks and Infrastructure-Enabled Solutions for Traffic State Estimation and Prediction
合作研究:数据中毒攻击和基于基础设施的交通状态估计和预测解决方案
- 批准号:
2326340 - 财政年份:2023
- 资助金额:
$ 40万 - 项目类别:
Standard Grant
Collaborative Research: SaTC: CORE: Small: Privately Collecting and Analyzing V2X Data for Urban Traffic Modeling
合作研究:SaTC:核心:小型:私下收集和分析用于城市交通建模的 V2X 数据
- 批准号:
2034615 - 财政年份:2021
- 资助金额:
$ 40万 - 项目类别:
Standard Grant
Collaborative Research: Bias Modeling and Estimation of Networked Transportation Data
合作研究:网络交通数据的偏差建模和估计
- 批准号:
1825053 - 财政年份:2018
- 资助金额:
$ 40万 - 项目类别:
Standard Grant
CAREER: Using Mobile Sensors for Traffic Knowledge Extraction and Dynamic Network Management
职业:使用移动传感器进行交通知识提取和动态网络管理
- 批准号:
1719551 - 财政年份:2016
- 资助金额:
$ 40万 - 项目类别:
Continuing Grant
Collaborative Research: Transportation Network Identification: Information Fusion via Stochastic Optimization
合作研究:交通网络识别:通过随机优化进行信息融合
- 批准号:
1719548 - 财政年份:2016
- 资助金额:
$ 40万 - 项目类别:
Standard Grant
Collaborative Research: Transportation Network Identification: Information Fusion via Stochastic Optimization
合作研究:交通网络识别:通过随机优化进行信息融合
- 批准号:
1537700 - 财政年份:2015
- 资助金额:
$ 40万 - 项目类别:
Standard Grant
BECS Collaborative Research: Modeling the Dynamics of Traffic User Equilibria Using Differential Variational Inequalities
BECS 协作研究:使用微分变分不等式对交通用户均衡动态进行建模
- 批准号:
1024647 - 财政年份:2010
- 资助金额:
$ 40万 - 项目类别:
Standard Grant
Collaborative Research: Mobile Sensors as Traffic Probes - Addressing Transportation Modeling and Privacy Protection in an Integrated Framework
协作研究:移动传感器作为交通探针 - 在集成框架中解决交通建模和隐私保护问题
- 批准号:
1031452 - 财政年份:2010
- 资助金额:
$ 40万 - 项目类别:
Standard Grant
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