Nonlinear moving horizon estimation for large-scale urban road networks

Sirmatel, I. I., & Geroliminis, N. (2019). Nonlinear moving horizon estimation for large-scale urban road networks. IEEE Transactions on Intelligent Transportation Systems, 21(12), 4983-4994. https://doi.org/10.1109/TITS.2019.2946324 (full text pdf)

Abstract: Perimeter control schemes proposed to alleviate congestion in large-scale urban networks usually assume perfect knowledge of the accumulation state together with current and future inflow demands, requiring information about the origins and destinations (OD) of drivers. Such assumptions are problematic for practice due to: (i) Measurement noise, (ii) difficulty of measuring OD-based accumulation states and inflow demands. To address these, we propose a nonlinear moving horizon estimation (MHE) scheme for large-scale urban road networks with dynamics described via macroscopic fundamental diagram. Furthermore, we consider various measurement configurations likely to be encountered in practice, such as measurements on regional accumulations and transfer flows without OD information, and provide results of their observability tests. Simulation studies, considering joint operation of the MHE with a model predictive perimeter control scheme, indicate substantial potential towards practical implementation of MFD-based perimeter control.