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不同的数据流，不仅来自固定的交通检测器，还来自许多新兴的有源移动的交通传感器，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。这种新一代敏捷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、不准确且不可靠。本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，数据和运输管理系统之间的互连。在这项研究中开发的方法将有助于交通运输机构有效地部署和集成有限的预算和资源的传感器，确定最薄弱的传感器覆盖的交通网络中的链接/节点/区域，并产生基于可观测性措施的缓解策略。使用主动移动的传感器的现场测试将证明该系统的可行性。研究成果将通过各种渠道融入教学，包括课程开发和教学导向的软件工具，可以有助于未来交通工程师的培训。 这项研究的目的是开发严格的数学基础和创新的算法，以准确地量化动态交通状态的空间可观测性，优化活动的移动的传感器位置，并从众包数据源挖掘信息。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分析时空分布，并进一步开发面向时间地理的优化，以量化动态网络的空间可观测性。研究一类新的泛在传感器网络设计问题的交通状态估计阶段，以及融合的众包数据与优化的固定和活跃的移动的传感器数据的集成研究在不同水平的活动/渗透率。利用底层动态交通网络的结构，本研究旨在开发计算效率高的优化算法，以创建一个分布式和可扩展的计算框架，可以解决主动移动的传感器的联合调度和路由问题，以提高覆盖率和准确性。该研究小组将开发空间网络可观测性的通用措施，为地震影响检测、地下水污染源识别和关键基础设施监测等一般土木工程系统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.