Scaling the Kalman filter for large-scale traffic estimation
For traffic engineers and researchers, this provides a scalable filtering algorithm that balances estimation accuracy and computational efficiency in large-scale networks.
This work introduces a distributed local Kalman consensus filter (DLKCF) for large-scale traffic estimation, which partitions the network into overlapping sections and uses consensus to promote inter-agent agreement. The filter achieves globally asymptotically stable mean error dynamics under observable modes and bounded errors otherwise, with numerical experiments showing improved estimation accuracy and reduced computational load compared to centralized approaches.
This work introduces a scalable filtering algorithm for multi-agent traffic estimation. Large-scale networks are spatially partitioned into overlapping road sections. The traffic dynamics of each section is given by the switching mode model (SMM) using a conservation principle, and the traffic state in each section is estimated by a local agent. In the proposed filter, a consensus term is applied to promote inter-agent agreement on overlapping sections. The new filter, termed a (spatially) distributed local Kalman consensus filter (DLKCF), is shown to maintain globally asymptotically stable (GAS) mean error dynamics when all sections switch among observable modes. When a section is unobservable, we show that the mean estimate of each state variable in the section is ultimately bounded, which is achieved by exploring the interaction between the properties of the traffic model and the measurement feedback of the filter. Based on the above results, the boundedness of the mean estimation error of the DLKCF under switching sequences with observable and unobservable modes is established to address the overall performance of the filter. Numerical experiments show the ability of the DLKCF to promote consensus, increase estimation accuracy compared to a local filter, and reduce the computational load compared to a centralized approach.