A constrained filtering algorithm for freeway traffic state estimation

Abstract

A real-time traffic state estimation algorithm is developed and applied to a freeway. The evolution of the traffic is defined by a second order macroscopic model which computes, for each section of the freeway, the density and the mean speed according to several non linear equations. Different extensions of the Kalman method were already applied to this model, though none of them considers the natural constraints in the state variables. In this work, a new method that incorporates those natural constraints, is applied to the macroscopic model obtaining better results. To validate the proposed method, a simulation over a freeway section was made using two different tools: the macroscopic simulator called METANET and the microscopic simulator called SUMO. Promising results were obtained using both approaches.

An adaptive framework for real-time freeway traffic estimation in the presence of CAVs

“…A similar in the best case or inferior in the worst, performance for the EKF is implied as well, see for example Seo et al (2017), Mihaylova et al (2007), Risso et al (2020), Trinh et al (2021. Additionally, the original UKF and EnKF methods discussed in the literature often do not account for bounding traffic state outputs inside a physically plausible domain.…”

“…The sole exception to the above situation is a large field study demonstrating the EKF framework's performance on a large corridor between Salerno and Naples in Italy; see Wang et al (2011). However, with the improvement of microsimulation software, there is an emergence of works that use such software for more realistic measurement data generation for small networks, see for example Work et al (2008), Wang et al (2017), Risso et al (2020), Trinh et al (2021. Similarly, the proposed study uses a microscopic representation of the ring road of Antwerp (44 [km]), calibrated on traffic counts during peak hours (Mattas et al, 2018;Makridis et al, 2018) for measurement data generation.…”

“…On the other hand, real-time traffic estimation is sometimes noted in the literature as a short-term prediction. Many methodologies have been proposed for traffic state estimation in various approaches such as time-series analysis, see Ahmed and Cook (1979), data assimilation with Kalman filtering techniques, see Mihaylova et al (2007), Wang and Papageorgiou (2005), Risso et al (2020), fuzzy logic as in Stathopoulos et al (2010), neural networks as in Ma et al (2015), statistical approaches, see Crawford et al (2017), and others. Additionally, some studies exploit parametric traffic models such as CTM proposed by Daganzo (1994) and METANET proposed by Papageorgiou et al (1990), non-parametric proposed by Ma et al (2015), or hybrid ones, see Boto-Giralda et al (2010).…”

Automatic Identification of Near Stationary Traffic States Using Changepoint Detection Method

“…In Kidando et al ( 8, 9 ), the definition of traffic congestion was established by the Bayesian changepoint detection method, and a probabilistic framework was developed to evaluate the dynamic evolution process from free-flow state to traffic congestion state. Based on the traffic flow data, some scholars used the Gaussian mixed distribution model to divide the congested and non-congested traffic states ( 10, 11 ), and some scholars have estimated such traffic states ( 12–15 ).…”

A fast Broyden's approximation‐based weighted least square state estimation for power systems

“…16 A weighted least square (WLS)-based SE technique for detecting the bad data by following the variations in subsequent measurements and evaluating the distance indices between adjacent steps was proposed by Chaojun et al 17 A two-step SE method for large distribution systems was presented by Muscas et al, 18 wherein the system was partitioned into sub-networks based on the availability of measurements and on geographical constraints, and then a local SE was performed at each sub-network to obtain the state of the entire system. A hybrid SE algorithm integrating the "unscented" Kalman filter and the WLS was suggested by Risso et al 19 A dynamic SE involving both the extended Kalman filter and load forecasting, which predicts the missing measurements, was proposed by Gu and Jirutitijaroen with a view of enhancing the accuracy. 20 A PMU-based method for performing SE that adjusts the weights depending on the biggest disturbance from the PMUs with a view of enhancing the robustness under different operating conditions, was presented by Zhao et al 21 The WLS estimator was studied on standard power systems and investigated how the number and the type of measurements affect the efficiency of the estimator by Meriem et al 22 A mean squared error estimator was suggested by Rana et al 23 for investigating the filtering problem for dynamic SE of micro grids considering packet losses.…”