Collaborative Research: Improving Spatial Observability of Dynamic Traffic Systems through Active Mobile Sensor Networks and Crowdsourced Data

为了提供有效的交通拥堵缓解策略，交通运输机构需要有效地设计传感器网络，以可靠地估计和预测大型交通网络的交通状况。下一代交通传感器网络将提供大量、多样化的数据流，不仅来自固定交通探测器，还来自许多新兴的主动移动交通传感器，如无人机、自动驾驶汽车，以及来自社交传感器和交通网络公司的众包数据源。这种新一代的灵敏传感器可以提供更丰富但也越来越复杂的交通数据环境。此外，众包数据通常不受控制、不准确和不可靠。这项研究的重点是新的传感器设计/控制应用，以改变旅行者，传感器，数据和交通管理系统之间的互连。本研究开发的方法将帮助运输机构在预算和资源有限的情况下有效地部署和整合传感器，确定传感器覆盖范围最弱的运输网络中的链路/节点/区域，并根据可观察性措施制定缓解策略。使用主动移动传感器的现场测试将证明该系统的可行性。研究成果将透过课程开发和教学导向的软件工具等多种渠道，融入教学，为未来的交通工程师培训作出贡献。与一所历史悠久的黑人学院和大学的合作将有助于扩大代表性不足的学生群体的参与。本研究的目的是建立严格的数学基础和创新算法，以准确量化动态交通状态的空间可观测性，优化主动移动传感器位置，并从众包数据源中挖掘信息。研究团队将首先在宏观和微观尺度上表征不同交通状态的解析时空分布，并进一步发展面向时间地理的动态网络空间可观测性量化优化。在流量状态估计阶段研究了一类新的泛在传感器网络设计问题，并在不同活动/渗透率水平下，研究了融合良好的众包数据与优化的固定和主动移动传感器数据的集成问题。本研究利用底层动态交通网络结构，开发计算效率高的优化算法，创建分布式、可扩展的计算框架，解决有源移动传感器联合调度和路由问题，提高覆盖和精度。该研究小组将开发空间网络可观测性的通用测量方法，为一般土木工程系统（如地震影响检测、地下水污染源识别和关键基础设施监测）提供额外的理论发现。

To provide effective traffic congestion mitigation strategies, transportation agencies need to effectively design sensor networks to reliably estimate and predict traffic conditions across large transportation networks. The next generation traffic sensor network will offer large, diverse data streams not only from fixed traffic detectors, but also from many emerging active mobile traffic sensors such as Unmanned Aerial Vehicles, self-driving cars, and crowdsourced data sources from social sensors and transportation network companies. This new generation of agile sensors can provide a much richer but also increasingly complex traffic data environment. Moreover, crowdsourced data is generally uncontrolled, inaccurate and unreliable. This research focuses on new sensor design/control applications to transform the interconnection between travelers, sensors, data and transportation management systems. Methodologies developed in this research will help transportation agencies to efficiently deploy and integrate sensors with limited budgets and resources, identify links/nodes/areas in transportation networks with the weakest sensor coverage, and generate mitigation strategies based on observability measures. Field tests using active mobile sensors will demonstrate the feasibility of the system. Research outcomes will be integrated into teaching through various channels including curriculum development and teaching-oriented software tools that can contribute to the training of future transportation engineers. The collaboration with a Historically Black College and University will help broaden participation of underrepresented student groups.The objective of this research is to develop rigorous mathematical foundations and innovative algorithms to accurately quantify spatial observability of dynamic traffic states, optimize active mobile sensor locations, and mine information from crowdsourced data sources. The research team will first characterize analytical space-time distributions of different traffic states at both macroscopic and microscopic scales, and further develop time-geography-oriented optimization for quantifying spatial observability for dynamic networks. A new class of ubiquitous sensor network design problems is studied for the traffic state estimation stage, and the integration of the well-fused crowdsourced data with optimized fixed and active mobile sensor data is investigated under different levels of activity/penetration rates. Utilizing the structure of underlying dynamic transportation networks, this research aims to develop computationally efficient optimization algorithms to create a distributed and scalable computing framework, which can solve joint scheduling and routing problems of active mobile sensors to increase coverage and accuracy. The research team will develop generic measures of spatial network observability that can provide additional theoretical findings for general civil engineering systems such as earthquake impact detection, ground water pollution source identification, and critical infrastructure monitoring.