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

Arizona State University, Scottsdale AZ

Investigators

Abstract

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.