CAREER: Traffic congestion on freeways: using probe vehicle data to understand bottlenecks and mitigate the resulting problems

职业：高速公路交通拥堵：使用探测车辆数据了解瓶颈并缓解由此产生的问题

• 批准号: 0133278
• 负责人: Benjamin Coifman
• 金额: --
• 依托单位: Ohio State University Research Foundation -DO NOT USE
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-10-01 至 2008-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0133278&HistoricalAwards=false
• 关键词: CAREER; Traffic; congestion; freeways; using

Freeway traffic congestion impacts the movement of most people and goods in the United States. This congestion is due to a small number of points on the network where demand peaks or capacity drops. Although these bottlenecks typically occupy a short distance of roadway, the resulting queues can extend for several miles. Most freeway queues are characterized by slow and stop waves propagating upstream from a bottleneck. The changing speeds give rise to an increased probability of accidents and the frequent accelerations increase vehicle emissions. Although the propagation of these waves can be predicted once they form, it is not known how they originate and debate continues as to whether they impact capacity. To date, these issues have been studied with point detectors, which are capable of monitoring traffic at fixed locations.The proposed work will equip two probe vehicles with GPS receivers and distance sensors to monitor adjacent vehicles. The probe vehicles will collect data throughout bottleneck regions and observe important signals that may not propagate all the way to point detectors. These probes will be used to identify which factors influence bottleneck capacity, and identify how the signals and waves form in freeway queues. These efforts should lead to additional benefits including improved car following and traffic flow models. The work is interdisciplinary, drawing on electrical engineering for sensor technologies, and civil engineering for traffic flow theory. This proposal forms the cornerstone for a new course, cross-listed between the two academic departments and taught with a teamwork environment that is found rarely in coursework. In this integrated environment, the students will push the boundaries of knowledge by the end of each quarter.

高速公路交通拥堵影响了美国大部分人员和货物的流动。这种拥塞是由于网络上需求达到峰值或容量下降的少量点造成的。虽然这些瓶颈通常占用一小段道路，但由此产生的队列可能会延伸几英里。大多数高速公路排队的特点是缓慢和停止波传播上游的瓶颈。不断变化的速度增加了发生事故的可能性，频繁的加速增加了车辆的排放。虽然这些波一旦形成就可以预测其传播，但不知道它们是如何起源的，关于它们是否影响容量的辩论仍在继续。到目前为止，这些问题已经研究了点探测器，它能够监测交通在固定位置。拟议的工作将配备两个探测车与全球定位系统接收器和距离传感器，以监测相邻的车辆。探测车将在整个瓶颈区域收集数据，并观察可能不会一直传播到点探测器的重要信号。这些探测器将被用来确定哪些因素影响瓶颈容量，并确定如何形成的信号和波在高速公路排队。这些努力应该会带来额外的好处，包括改善车辆跟踪和交通流模型。这项工作是跨学科的，利用电气工程的传感器技术，和土木工程的交通流理论。这一提议构成了一门新课程的基石，在两个学术部门之间交叉列出，并在课程作业中很少发现的团队合作环境中授课。在这种综合环境中，学生将在每个季度末推动知识的边界。