An adaptive observation site selection strategy for road traffic data assimilation

Smartphones and other vehicular sensors equipped with GPS and wireless networking capabilities, are becoming ubiquitous in transportation systems. They provide us with opportunities to gather timely information about road traffic conditions, fuse (assimilate) it with traffic flow models to improve upon the accuracy of these models, and hence supply valuable information for real-time transportation decision making. Macroscopic traffic flow models are described by systems of partial differential equations (PDEs), which are usually only solved numerically. Adaptive moving mesh methods have shown promise in handling high variability of the spatio-temporal features (e.g. shocks and discontinuities) in model's solutions. We propose a novel low-overhead strategy to adaptively select observation sites in real time, by relying on information from the adaptive moving mesh of the numerical solver of the underlying PDEs. The idea is to place more of the limited observational resources to locations of higher variability in the numerical solution. We incorporate our strategy into a particle-filter based data assimilation framework, and compare it with the strategy of gathering and assimilating measurements from evenly spaced observation sites. We experimentally show that our strategy reduces the relative error by up to 53% in estimating vehicle density on a road during phantom jams and traffic jams due to bottlenecks.