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

摘要：CMS-0528225和cms -0528143在过去的十年中，在实时交通控制环境下，获取详细、可靠和随时间变化的交通流数据的能力往往主要强调大型控制器系统的策略级控制算法。这些算法通常针对大城市地区，并依赖于相当广泛和昂贵的探测器放置。它们不能经济有效地解决半城市或农村地区孤立的十字路口，在这些地方，广泛的全系统探测器安装在经济上不合理，或者信号交叉口在空间上稀疏，妨碍了系统的观点。研究者和他的同事们开发了一系列战术随机传感控制模型，以提高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新技术能力相一致，对用户需求敏感，并捕获时间依赖性和随机性。这个新范例被合并到一个概率控制层中，该层在现有的流量控制框架中工作。它明确承认车辆到达交通路口的过程在几个方面是随机的，这代表了当前信号控制逻辑标准实践的转变，这种随机性在某种程度上通过驱动控制隐含地结合在一起。然而，目前的驱动逻辑在方法上受到限制，无法捕捉多车道下车辆行驶的变幻性、天气的影响、车辆到达对竞争阶段的影响，以及无法利用丰富的现成历史数据。它也缺乏一个强大的机制，以确保困境区保护安全终止绿色。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运行相关的随机性的多个方面；（ii）除了实时数据反映的当前条件外，还可以利用有价值的历史数据；（iii）可以通过战略性传感器放置来增强操作稳健性；（iv）可以整合交叉口的整体视图，而不仅仅是关注当前阶段对应的交叉口方法。(v)是技术中立的，以适应各种传感技术，以及（vi）可以对恶劣天气条件和特殊事件作出反应。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十字路口安装了仪器，以及田纳西大学的交通操作实验室）。该研究不是被动地使用传感器数据，而是将来自仪表化路口的数据、原型模型和算法纳入实际的基于实验室的闭环信号控制系统。这代表了下一代基于传感器的方法的重要技术范例，这些方法对交通系统的性能低下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采用确定性算法，虽然可靠，但在绿灯时间的分配上往往效率低下。随着交通拥堵的增加，有必要从现有系统中获得更高的效率。本研究通过开发利用信息和传感器技术进步的新概率范式，减少了广泛存在的农村和郊区孤立十字路口的延误并提高了安全性。所提出的方法包含一个与控制基础设施兼容的新控制层，从而允许直接实施本研究，而无需对信号系统基础设施进行大规模和昂贵的升级。该研究减少了困境区暴露，提高了运营效率。减少两难区暴露可减少与人为因素相关的撞车事故。更高效的操作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结构相比，提出的解决方案是一种范式转变，并且由传感器和信息技术的进步提供的丰富数据阵列协同实现。该研究还有助于目前开发国家交通信号控制实验室网络的努力，以利用地理分布的大学的能力。

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.