2018/12/05 by Sebastian A. Nugroho, Nugroho, Sebastian A., Ahmad F. Taha +3 · 1 citation
Engineering · #FOS: Electrical engineering #FOS: Mathematics #Optimization and Control (math.OC) #Systems and Control (eess.SY) #Traffic Prediction and Management Techniques #Traffic control and management #Vehicle emissions and performance #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1812.02128
openalex publication_date 2018/12/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Monitoring and control of traffic networks represent alternative, inexpensive\nstrategies to minimize traffic congestion. As the number of traffic sensors is\nnaturally constrained by budgetary requirements, real-time estimation of\ntraffic flow in road segments that are not equipped with sensors is of\nsignificant importance---thereby providing situational awareness and guiding\nreal-time feedback control strategies. To that end, firstly we build a\ngeneralized traffic flow model for stretched highways with arbitrary number of\nramp flows based on the Lighthill Whitham Richards (LWR) flow model. Secondly,\nwe characterize the function set corresponding to the nonlinearities present in\nthe LWR model, and use this characterization to design real-time and robust\nstate estimators (SE) for stretched highway segments. Specifically, we show\nthat the nonlinearities from the derived models are locally Lipschitz\ncontinuous by providing the analytical Lipschitz constants. Thirdly, the\nanalytical derivation is then incorporated through a robust SE method given a\nlimited number of traffic sensors, under the impact of process and measurement\ndisturbances and unknown inputs. The estimator is based on deriving a convex\nsemidefinite optimization problem. Finally, numerical tests are given\nshowcasing the applicability, scalability, and robustness of the proposed\nestimator for large systems under high magnitude disturbances, parametric\nuncertainty, and unknown inputs.\n