Observability based data-fusion cascading filtering for urban network flow estimation

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In this work we extend our previously proposed cascading Kalman filtering technique, applied to the problem of urban network flow estimation, to adopt heterogeneous traf- fic data sources. Both static infrastructure detection (double induction loops) and Floating Car Data are collected from a given transportation network, and employed within separate stages of the cascading technique. The proposed approach relies upon notions of traffic flow inference (observability) to both i) determine the optimal set of locations in which sensors should be installed and ii) provide enhanced covariance information within the estimation technique. The impact of both penetration rates of Floating Car Data and sensor selection procedure is evaluated empirically, through a microscopic simulation software (SUMO) generating experimental data on a simple grid-like network.
Test results showcase that the proposed extension to the cascading framework is indeed beneficial in reducing the overall estimation error on network segments where static infrastructure is unavailable. Furthermore, the importance of observability- based sensor locations is clearly demonstrated.