Incident-aware Resilient Traffic Management for Urban Road Networks (InTURN)

想象一下，你正在以尽可能快和可靠的速度通过城市交通网络，同时避免所有拥堵和干扰。再想象一下，您在这样做的时候，没有连续且无处不在地向大型数据驱动型企业发送位置和上下文信息，从而蒸发了您所有的个人隐私信息。在这项提案中，我们将研究如何将这样的系统实现为自适应和自组织(SASO)系统。对于SASO系统来说，城市交通是一个具有挑战性的试验台：巨大的交通量与潜在的动态和时变行为相结合，以及事件和负面环境影响等干扰，需要新的综合控制和管理策略。在过去的十年里，已经提出了几种用于交通灯控制、渐进信号系统和路线诱导的方法，其中包括我们自己的初步工作：有机交通控制(OTC)。然而，现有的系统通常仅限于对观察到的交通状况做出反应，而不考虑诸如事故(例如，车祸、建筑工程或卡车卸载)之类的干扰。在这项提议中，我们希望通过智能机制来克服这些限制，以增加交通控制和管理解决方案的弹性。我们使用机器学习方法检测异常交通状况并识别事件。这些事故根据其估计持续时间、预期严重程度以及对其他交叉口和道路要素的预期影响自动进行分类。我们进一步利用交通控制的互连特性，开发了对检测到的事件进行协作验证的技术-这也允许检测受干扰的传感器。事件分类受制于自主学习机制，该机制在运行时自我改进和完善决策。为了最终利用确定的事件信息，我们研究了一个基于OTC的集成和健壮的交通管理系统，该系统1)适应和自我改进交通灯信号策略，2)建立和维护交通响应渐进信号系统，3)动态引导驾驶员通过底层道路网络。结果将在出行时间、红绿灯前停车次数和减排方面优于现有解决方案。

Imagine that you are passing an urban traffic network as fast and reliable as possible while simultaneously avoiding all congestions and disturbances. Imagine also that you are doing this without continuously and ubiquitously sending location and context information to large data-driven enterprises evaporating all your personal privacy information. In this proposal, we will investigate how such a system can be realised as a self-adaptive and self-organising (SASO) system. Urban traffic is a challenging testbed for SASO systems: Massive traffic volumes in combination with the underlying dynamics and time-variant behaviour as well as disturbances such as incidents and negative environmental effects demand for novel integrated control and management strategies. Within the last decade, several approaches for traffic light control, progressive signal systems, and route guidance have been presented, including our own preliminary work: Organic Traffic Control (OTC). Existing systems, however, are typically limited to only reacting to observed traffic conditions and they do not consider disturbances such as incidents (e.g. car accidents, construction work, or un/loading of lorries). In this proposal, we want to overcome these limitations by means of intelligent mechanisms to increase the resilience of traffic control and management solutions. We detect abnormal traffic conditions and identify incidents using machine learning approaches. These incidents are automatically classified by means of their estimated duration, their anticipated severity, and the expected influence on other intersections and road elements. We further take advantage of the interconnected character of traffic control by developing techniques for a cooperative validation of detected incidents – which also allows for detecting disturbed sensors. The incident classification is subject to an autonomous learning mechanism that self-improves and refines the decisions at runtime. In order to finally take advantage of the determined incident information, we investigate an integrated and robust traffic management system based on OTC that 1) adapts and self-improves the traffic light signalisation strategy, 2) establishes and maintains traffic-response progressive signal systems, and 3) dynamically guides drivers through the underlying road network. The result will outperform existing solutions in terms of travel times, number of stops in front of red traffic lights, and emission reductions.