Yufei Yuan

Real-Time Lagrangian Traffic State Estimator for Freeways

Freeway traffic state estimation and prediction are central components in real-time traffic management and information applications. Model-based traffic state estimators consist of a dynamic model for the state variables (e.g., a first- or second-order macroscopic traffic flow model), a set of observation equations relating sensor observations to the system state (e.g., the fundamental diagrams), and a data-assimilation technique to combine the model predictions with the sensor observations [e.g., the extended Kalman filter (EKF)]. Commonly, both process and observation models are formulated in Eulerian (space-time) coordinates. Recent studies have shown that this model can be formulated and solved more efficiently and accurately in Lagrangian (vehicle number-time) coordinates. In this paper, we propose a new model-based state estimator based on the EKF technique, in which the discretized Lagrangian Lighthill-Whitham and Richards (LWR) model is used as the process equation, and in which observation models for both Eulerian and Lagrangian sensor data (from loop detectors and vehicle trajectories, respectively) are incorporated. This Lagrangian state estimator is validated and compared with a Eulerian state estimator based on the same LWR model using an empirical microscopic traffic data set from the U.K. The results indicate that the Lagrangian estimator is significantly more accurate and offers computational and theoretical benefits over the Eulerian approach.

Network-Wide Traffic State Estimation Using Loop Detector and Floating Car Data

In real-time traffic management and intelligent transportation systems (ITS) applications, an accurate picture of the prevailing traffic state in terms of speeds and densities is critical, for which traffic state estimation methods are needed. The most popular and effective techniques used are so-called model-based traffic state estimators, which consist of a dynamic traffic flow model to predict the evolution of the state variables; a set of observation equations relating sensor observations to the system state; and data-assimilation techniques to combine the model predictions with the sensor observations. Commonly, both process and observation models are formulated in Eulerian (space–time) coordinates. However, recent studies show that (first-order) macroscopic traffic flow models can be formulated and solved more efficiently and accurately in Lagrangian (vehicle number–time) coordinates (which move with traffic stream) than in Eulerian coordinates (which are fixed in space). In this article such a Lagrangian system model for state estimation is used. The approach uses the extended Kalman filtering technique, in which the discretized Lagrangian kinematic wave model with an extension (node models) for network discontinuities is used as the process equation and the average relation between vehicle spacing and speed (the fundamental diagram) is used as the observation equation. The Lagrangian state estimator is validated and compared with its Eulerian counterpart based on ground-truth data from a microscopic simulation environment. The results demonstrate that network-wide Lagrangian state estimation is possible and provide evidence that the Lagrangian estimator outperforms the Eulerian approach.

Freeway traffic state estimation using extended Kalman filter for first-order traffic model in Lagrangian coordinates

Freeway traffic state estimation is one of the central components in real-time traffic management and information applications. Recent studies show that the classic kinematic wave model can be formulated and solved more efficiently and accurately in Lagrangian (vehicle number-time) coordinates. This paper investigates the opportunities of the Lagrangian form for state estimation. The main advantage for state estimation is that in Lagrangian coordinates, the numerical solution scheme is reduced to an upwind scheme. We propose a new model-based extended Kalman filter (EKF) state estimator where the discretized Lagrangian model is used as the model equation. This state estimator is applied to freeway traffic state estimation and validated using synthetic data. Different filter design specifications with respect to measurement aspects are considered. The achieved results are very promising for subsequent studies.

Automatic speed-bias correction with flow-density relationships

Accurate and reliable speeds measured with local traffic sensors are critically important for many applications, such as travel time estimation, state estimation and prediction and dynamic traffic control. Many local sensors (e.g. the dual induction loops used in The Netherlands, UK and in Germany) calculate average speed by arithmetic time averaging, which leads to strongly biased estimates. This paper proposes a speed-bias correction algorithm based on notions from first order traffic flow theory and empirical flow-density relationships, using only a limited number of parameters. On the basis of a microscopic simulation study, it is demonstrated that this algorithm is able to correct the speed bias due to time averaging considerably. A more extensive version of this paper can be found at http://dux.ict.tbm.tudelft.nl/Research/SpeedCorr.pdf

Coordination Concepts for Ramp Metering Control in a Freeway Network

Abstract The steadily increasing numbers and lengths of traffic jams on freeways have led to the application of Dynamic Traffic Management (DTM) measures all over the world. Ramp metering control has proven to be one of the most efficient means to reduce freeway congestion. Currently, it is expected that integrated and coordinated application of DTM measures will further improve its impact. This paper studies a coordinated ramp metering control algorithm called HERO/RWS. This algorithm has been developed for the current Dutch ramp metering systems and it will be applied on the Amsterdam A10 freeway network in the near future. The aim of this algorithm is to postpone congestion on freeways by effectively using ramp storage space from upstream on-ramps. VISSIM-based microscopic simulation results show that the HERO/RWS coordinated control outperforms non-coordinated ramp metering control. Parameter settings have been optimized for the specific A10-west network through a robustness study. In addition, the concept of coordination between ramp meter and upstream intersection traffic controllers is developed. The feasibility of this idea has been proven by a simulation study.

New Extended Discrete First-Order Model to Reproduce Propagation of Jam Waves

This paper proposes an extension of the discrete Lighthill–Whitham–Richards model of the cell transmission model type to reproduce capacity drop and the propagation of jam waves. Recent studies have tried to incorporate the capacity drop into discrete first-order traffic flow models for traffic optimization purposes. It was found that the inflow to a discharging cell predicted by these models might have been overestimated, and this overestimation influenced the propagation of a jam wave. An empirical analysis was carried out to confirm this assumption. It was found that the extent of the flow reduction depended on the state difference between the targeting cell and its upstream cell. On the basis of these findings, a new mathematical model formulation is given. Simulations with both a hypothetical freeway stretch and a real-life freeway stretch are performed to test the behavior of the proposed model. The previously mentioned models are also simulated for comparison. The simulation results indicate that the proposed model is better able to reproduce jam waves. In addition, the proposed model can be used in a linear model predictive control framework and formulated as a linear optimization problem, which may be beneficial for a real-life, real-time application.

Estimation of Multiclass and Multilane Counts from Aggregate Loop Detector Data

Lane utilization on the highway is affected subtly by dynamic traffic management systems such as speed controls and lane management. To optimize the operation of dynamic traffic management, a better understanding of lane utilization is required, in particular, of how the flows of different vehicle classes (e.g., passenger cars, lorries) vary across the carriageway. Most loop detector systems do not collect this multilane, multiclass count data. This study developed a procedure for estimating multilane, multiclass counts from a variety of standard aggregate loop data formats from around the world. The estimation procedure involved the inference of multilinear regression laws that relate multilane, multiclass data to standard aggregate formats. The regression laws were then trained with small samples of individual vehicle data on a site-by-site basis. Preliminary results showed that the estimation procedure worked rather well, even when the input data were minimal—the extreme case being that of (U.S.-style) single-loop data, for which only flow and occupancy were available on a by lane basis. An error analysis indicated that small amounts of individual vehicle data were sufficient to train the estimator, provided they contained a representative mix of the flow behaviors at the site in question. Further work is required for the practical development of the tool, but it appears to have a wide range of potential uses for both researchers and practitioners.

Coordination of agent-based control in a freeway network

The steadily increasing numbers and lengths of traffic jams on freeways have led to the use of Dynamic Traffic Management (DTM) measures all over the world. Ramp metering control has proven to be one of the most efficient means to reduce freeway congestion. It is expected that integrated and coordinated application of DTM measures will further improve its impact. This paper studies a new coordinated ramp metering control algorithm called HERO/RWS. This algorithm has been developed for the current Dutch ramp metering systems and it will be applied on the Amsterdam A10 freeway network in the near future. The aim of this algorithm is to postpone congestion on freeways by effectively using ramp storage space from upstream on-ramps. The control scheme is simple and real-time operable. VISSIM-based microscopic simulation results show that the HERO/RWS coordinated control outperforms non-coordinated control. This control algorithm turns out to provide less congestion, higher mean speeds and lower travel time spent on the freeway.

Efficient Traffic State Estimation and Prediction Based on the Ensemble Kalman Filter with a Fast Implementation and Localized Deterministic Scheme

Traffic state estimation and forecasting are central components in dynamic traffic management and information applications. This paper proposes a traffic state estimation approach based on an improved formulation of the traditional Ensemble Kalman filter (EnKF), including a fast implementation and a localized deterministic scheme. A reformulation of the EnKF equations leads to efficient computation. The deterministic scheme implies that we use the same observations for each of the ensembles instead of randomized observations. The use of a deterministic algorithm can reduce the impact of coincidental sampling and associated sampling errors. Localization is in contrast with a global method. In the global method, both the evolution of system states and the incorporation of observations are considered as an entity (within a global matrix). Here, the inclusion of localization has several potential advantages for large-scale applications: blocking spurious correlations, decreasing computation time due to smaller matrix inversions, increasing the accuracy by increasing the effective ensemble size. The proposed implementation of the EnKF for traffic state estimation and prediction is tested and validated in a realistic Dutch freeway network. The experiment studies deliver promising results for large-scale practical applications.

Linear Quadratic MPC for Integrated Route Guidance and Ramp Metering

Capacity drop is a common phenomenon that typically occurs at the downstream of freeway congestion area, which decreases traffic operation efficiency significantly. It can be avoided by some traffic control measures such as ramp metering. In this paper, a generic first-order traffic flow model is proposed, to reproduce the capacity drop at both on-ramp bottleneck and lane drop bottleneck. Based on this model, a linear quadratic model predictive control strategy for the integration of dynamic route guidance and ramp metering is presented. The objective is to minimize the total time spend of a traffic network. Due to the linearity of the optimization problem, the problem can be solved efficiently, which makes the problem suitable for real-time traffic control. A hypothetical traffic network containing both on-ramp bottleneck and lane drop bottleneck is utilized as a test bed, to demonstrate the effectiveness of the proposed control strategy.