Collaborative Research: Stochastic Sensing Control Models for Safe and Efficient Traffic Signal Strategies

合作研究：安全高效交通信号策略的随机传感控制模型

• 批准号: 0528225
• 负责人: Srinivas Peeta
• 金额: $ 26.5万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-08-01 至 2008-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0528225&HistoricalAwards=false
• 关键词: Collaborative; Research; Stochastic; Sensing; Control

Abstract for CMS-0528225 and CMS-0528143Over the past decade, the ability to obtain detailed, reliable and time-dependent traffic flow data has tended to mostly emphasize strategic level control algorithms for large systems of controllers in the real-time traffic control context. These algorithms are typically targeted at large urban areas and depend on rather extensive and costly detector placements. They do not cost-effectively address isolated intersections in semi-urban or rural areas, where either extensive system-wide detector installation cannot be economically justified, or signalized intersections are spatially sparse precluding a systems perspective. The investigator and his colleagues develop a range of tactical stochastic sensing control models that enhance the safety and efficiency at signalized intersections. They use a probabilistic paradigm that is consistent with state-of-the-art technological capabilities of existing signals, is sensitive to user requirements, and captures the time-dependency and randomness. This new paradigm is incorporated in a probabilistic control layer that works within the existing traffic control framework. It explicitly acknowledges that the arrival process of vehicles to a traffic intersection is stochastic in several respects, representing a shift from the current standard practice of signal control logic where this randomness is implicitly incorporated to some extent through actuated control. However, the current actuated logic is methodologically limited in capturing the vagaries of vehicle headways under multiple lanes, the influence of weather, the effect of vehicle arrivals on the competing phases, as well as in exploiting the rich array of readily-available historical data. It also lacks a robust mechanism to safely terminate the green by ensuring dilemma zone protection. The proposed methodology for signal control logic: (i) is probabilistic to reflect the multiple facets of randomness associated with the operation of signalized intersections, (ii) can exploit valuable historical data in addition to the current conditions reflected by real-time data, (iii) can enhance operational robustness through strategic sensor placements, (iv) can incorporate a holistic view of the intersection rather than focusing on just the intersection approaches corresponding to the current phase, (v) is technology-neutral to accommodate a variety of sensing technologies, and (vi) can react to inclement weather conditions and special events. The study leverages existing state-of-the-art traffic signal laboratory facilities (Purdue University instrumented intersections in West Lafayette and Noblesville, IN as well as the Traffic Operations Laboratory at the University of Tennessee). Rather than just use sensor data passively, the study incorporates data from the instrumented intersections and prototype models and algorithms into actual laboratory-based closed loop signal control systems. This represents a significant technological paradigm for the next generation of sensor-based methodologies that are less tolerant of performance inefficiencies and safety drawbacks for traffic systems. Our society is continuing to demand safer and more efficient roadways. Traditional traffic signal control uses deterministic algorithms which are reliable, but frequently inefficient in their allocation of green time. As traffic congestion increases, it is necessary to obtain more efficiency out of existing systems. This study reduces delay and improves safety at the widely prevalent rural and suburban isolated intersections by developing new probabilistic paradigms that exploit advances in information and sensor technologies. The proposed approaches incorporate a new control layer that is compatible with control infrastructure, thereby allowing direct implementation of this research without large and expensive upgrades to signal system infrastructure. The study reduces dilemma zone exposure and increases operational efficiency. Reduced dilemma zone exposure reduces human factors related crashes. More efficient operations reduce fossil fuel consumption and vehicle emissions. The proposed solutions are a paradigm shift compared to methodological constructs adopted for the past four decades, and are synergistically enabled by the rich array of data afforded by advances in sensor and information technologies. The study also contributes to current efforts on developing a national network of traffic signal control laboratories that leverage capabilities at geographically distributed universities.

摘要CMS-0528225和CMS-0528143在过去的十年中，获得详细的，可靠的和时间相关的交通流数据的能力往往主要强调在实时交通控制上下文中的大型控制器系统的战略级控制算法。这些算法通常针对大城市地区，并且依赖于相当广泛和昂贵的检测器放置。他们不符合成本效益地解决孤立的交叉口在半城市或农村地区，广泛的系统范围内的检测器安装不能经济合理，或信号交叉口空间稀疏排除系统的观点。研究人员和他的同事开发了一系列战术随机传感控制模型，提高了信号交叉口的安全性和效率。它们使用的概率范式与现有信号的最先进技术能力一致，对用户需求敏感，并捕获时间依赖性和随机性。这种新的范例被纳入一个概率控制层，在现有的交通控制框架内工作。它明确承认，车辆到达交通路口的过程是随机的，在几个方面，代表了从目前的信号控制逻辑的标准做法，这种随机性是隐含地纳入到某种程度上通过致动控制的转变。然而，目前的驱动逻辑在方法上是有限的，在捕捉变幻莫测的车辆车头时距下多车道，天气的影响，车辆到达的影响，对竞争阶段，以及在利用丰富的数组随时可用的历史数据。它还缺乏一个强大的机制，以安全地终止绿色，确保困境区保护。建议的信号控制逻辑方法：（i）是概率性的，以反映与信号交叉口的操作相关联的随机性的多个方面，（ii）除了由实时数据反映的当前状况之外，还可以利用有价值的历史数据，（iii）可以通过战略传感器布置来增强操作鲁棒性，（iv）可以结合交叉口的整体视图，而不是仅仅关注对应于当前相位的交叉口引道，（v）是技术中立的以适应各种感测技术，及（vi）可对恶劣天气状况及特殊事件作出反应。这项研究利用了现有的最先进的交通信号实验室设施（普渡大学在西拉斐特和诺布尔斯维尔的仪器化交叉口，以及田纳西大学的交通运营实验室）。该研究不仅仅被动地使用传感器数据，而是将来自仪表化交叉口的数据以及原型模型和算法整合到实际的基于实验室的闭环信号控制系统中。这代表了下一代基于传感器的方法的重要技术范例，该方法对交通系统的性能低效率和安全缺陷的容忍度较低。我们的社会继续要求更安全、更高效的道路。传统的交通信号控制使用确定性算法，这些算法是可靠的，但在分配绿色时间时常常效率低下。随着交通拥堵的加剧，有必要提高现有系统的效率。本研究通过开发利用信息和传感器技术进步的新的概率范例，减少了延误，提高了广泛流行的农村和郊区孤立的交叉口的安全性。所提出的方法包括一个新的控制层，是与控制基础设施兼容，从而允许直接实施这项研究，而无需大规模和昂贵的升级信号系统基础设施。这项研究减少了困境区的暴露，提高了运营效率。减少困境区暴露减少人为因素相关的崩溃。更高效的运营减少了化石燃料消耗和车辆排放。与过去四十年采用的方法论结构相比，拟议的解决方案是一种范式转变，并通过传感器和信息技术进步提供的丰富数据协同实现。这项研究还有助于目前的努力，发展一个国家网络的交通信号控制实验室，利用地理分布的大学的能力。