CAREER: Modeling and Estimation Methods for Complex Traffic

这个教师早期职业发展（CAREER）计划奖的目的是调查复杂交通的动态。复杂交通的特征在于不同的车辆类型（例如自行车和汽车），这些车辆在尺寸和性能特征上不同，但共享相同的基础设施，并且通常由人类控制。这些特征在美国因特殊事件而产生的极端拥堵中越来越常见，并且在全球新兴经济体中普遍存在。这项研究假设，数学模型的进步，通过大量的交通数据提供信息和验证，是解开复杂交通的全面理解的关键因素。本研究的重点是（一）异构交通的数学模型的发展，（二）人为导向的交通建模和分析，（三）快速，准确的估计算法的发展，将数据集成到城市规模的模型。通过一种新开发的交通传感技术获得数据来验证模型和估计算法。如果成功，这项工作将支持下一代交通监控和管理系统的开发。最终，这将有助于减少美国特殊活动期间每年数十亿美元的拥堵成本。开展教育和推广活动，使学生具备设计下一代计算机增强型基础设施所需的计算能力。这是通过为本科生和研究生提供新的教育计划来实现的，这些计划强调应用于土木工程问题的编程和计算技能。在交通运输领域内可重复的计算研究计划将有助于最大限度地发挥研究的潜在影响，并增加从业人员采用的可能性。通过外联和开放式课件活动，使更广泛的社区参与计算在交通运输中的应用。

The objective of this Faculty Early Career Development (CAREER) program award is to investigate the dynamics of complex traffic. Complex traffic is characterized by heterogeneous vehicle types (e.g. bikes and cars) that vary in size and performance characteristics but share the same infrastructure, and is often controlled by humans. These features are increasingly common in the US during extreme congestion generated by special events, and are pervasive in emerging economies worldwide. This research postulates that advances in mathematical models, informed by and validated with large volumes of traffic data, are key elements to unlock the full understanding of complex traffic. This research focuses on (i) the development of mathematical models of heterogeneous traffic, (ii) modeling and analysis of human-directed traffic and (iii) the development of fast and accurate estimation algorithms to integrate data into city-scale models. Data to validate the models and estimation algorithms are obtained through a newly developed traffic sensing technology.If successful, this work will support the development of next generation traffic monitoring and management systems. Ultimately, this will help reduce the multibillion-dollar annual cost of congestion during special events in the US. Educational and outreach activities are executed to prepare students with the computing competencies needed to engineer the next generation of computer enhanced infrastructure. This is achieved through new educational initiatives for undergraduate and graduate students that emphasize programming and computational skills applied to problems in civil engineering. Reproducible computational research initiatives within the transportation community will help maximize the potential impact of the research and increase likelihood of adoption by practitioners. Engagement of the broader community on applications of computing in transportation is achieved through outreach and open courseware activities.