Probe vehicle-based traffic state estimation method with spacing information and conservation law

Seo, Toru; Kusakabe, Takahiko

doi:10.1016/j.trc.2015.05.019

Cited by 72 publications

(32 citation statements)

References 16 publications

(20 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In general, methods for traffic state estimation can be categorized into model-driven and data-driven. In model-driven traffic state estimation, statistical state estimators such as Particle Filter [19]- [21], Kalman Filter [22], [23], Extended Kalman Filter (EKF) [24]- [27], Unscented Kalman Filter (UKF) [28], [29], and Ensemble Kalman Filter (EnKF) [11], [20], [30], [31] are among of the most extensively used methods-see [32, Tables 1 and 2] for a list of state estimators used in the recent literature. To mention a few, traffic density estimation has been studied based on a switching-mode scheme of cell transmission model (CTM) [33].…”

mentioning

confidence: 99%

A Control-Theoretic Approach for Scalable and Robust Traffic Density Estimation Using Convex Optimization

Nugroho

Taha

Claudel

2021

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

Monitoring and control of traffic networks represent alternative, inexpensive strategies to minimize traffic congestion. As the number of traffic sensors is naturally constrained by budgetary requirements, real-time estimation of traffic flow in road segments that are not equipped with sensors is of significant importance-thereby providing situational awareness and guiding real-time feedback control strategies. To that end, firstly we build a generalized traffic flow model for stretched highways with arbitrary number of ramp flows based on the Lighthill Whitham Richards (LWR) flow model. Secondly, we characterize the function set corresponding to the nonlinearities present in the LWR model, and use this characterization to design real-time and robust state estimators (SE) for stretched highway segments. Specifically, we show that the nonlinearities from the derived models are locally Lipschitz continuous by providing the analytical Lipschitz constants. Thirdly, the analytical derivation is then incorporated through a robust SE method given a limited number of traffic sensors, under the impact of process and measurement disturbances and unknown inputs. The estimator is based on deriving a convex semidefinite optimization problem. Finally, numerical tests are given showcasing the applicability, scalability, and robustness of the proposed estimator for large systems under high magnitude disturbances, parametric uncertainty, and unknown inputs.

show abstract

mentioning

confidence: 99%

A Control-Theoretic Approach for Scalable and Robust Traffic Density Estimation Using Convex Optimization

Nugroho

Taha

Claudel

2021

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

show abstract

“…Previous studies showed that the performance of probe-based traffic estimation models can be affected by the probe penetration rate. Moreover, network links coverage and link neighbor coverage are significantly impacted by the probe penetration rate in urban network [10,11,18]. Therefore, this paper also extends the proposed methodology of travel time estimation using RF and ANN to evaluate the proposed models performance at different probe penetration rates.…”

Section: Literature Reviewmentioning

confidence: 97%

Applying Machine Learning and Statistical Approaches for Travel Time Estimation in Partial Network Coverage

2019

View full text Add to dashboard Cite

The objective of this study is to estimate the real time travel times on urban networks that are partially covered by moving sensors. The study proposes two machine learning approaches; the random forest (RF) model and the multi-layer feed forward neural network (MFFN) to estimate travel times on urban networks which are partially covered by moving sensors. A MFFN network with three hidden layers was developed and trained using the back-propagation learning algorithm, and the neural weights were optimized using the Levenberg–Marquardt optimization technique. A case study of an urban network with 100 links is considered in this study. The performance of the proposed models was compared to a statistical model, which uses the empirical Bayes (EB) method and the spatial correlation between travel times. The models’ performances were evaluated using data generated from VISSIM microsimulation model. Results show that the machine learning algorithms, e.g., RF and ANN, achieve average improvements of about 4.1% and 2.9% compared with the statistical approach. The RF, MFFN, and the statistical approach models correctly predict real time travel times with estimation accuracies reaching 90.7%, 89.5%, and 86.6% respectively. Moreover, results show that at low moving sensor penetration rate, the RF and MFFN achieve higher estimation accuracy compared with the statistical approach. At probe penetration rate of 1%, the RF, MFFN, and the statistical approach models correctly predict real time travel times with estimation accuracy of 85.6%, 84.4%, and 80.9% respectively. Furthermore, the study investigated the impact of the probe penetration rate on real time neighbor links coverage. Results show that at probe penetration rates of 1%, 3%, and 5%, the models cover the estimation of real time travel times on 73.8%, 94.8%, and 97.2% of the estimation intervals.

show abstract

“…This method requires a numerical model of the relationship between the traffic states to describe the changes in speed, flow, and density. Finally, Seo and Kusakabe [25] propose a method based on the number of vehicles located in between two connected vehicles to estimate the traffic conditions on the road. For methods using only connected vehicle data, the measurements used to estimate density are local, only including the connected vehicle and its surroundings, which might not necessarily reflect the density on a larger section of the road.…”

Section: Traffic State Estimation Using Connected Vehiclesmentioning

confidence: 99%

Traffic State Estimation Using Connected Vehicles and Stationary Detectors

Grumert

Tapani

2018

Journal of Advanced Transportation

View full text Add to dashboard Cite

Real-time traffic state estimation is of importance for efficient traffic management. This is especially the case for traffic management systems that require fast detection of changes in the traffic conditions in order to apply an effective control measure. In this paper, we propose a method for estimating the traffic state and speed and density, by using connected vehicles combined with stationary detectors. The aim is to allow fast and accurate estimation of changes in the traffic conditions. The proposed method does only require information about the speed and the position of connected vehicles and can make use of sparsely located stationary detectors to limit the dependence on the infrastructure equipment. An evaluation of the proposed method is carried out by microscopic traffic simulation. The traffic state estimated using the proposed method is compared to the true simulated traffic state. Further, the density estimates are compared to density estimates from one detector-based method, one combined method, and one connected-vehiclebased method. The results of the study show that the proposed method is a promising alternative for estimating the traffic state in traffic management applications.

show abstract

Probe vehicle-based traffic state estimation method with spacing information and conservation law

Cited by 72 publications

References 16 publications

A Control-Theoretic Approach for Scalable and Robust Traffic Density Estimation Using Convex Optimization

A Control-Theoretic Approach for Scalable and Robust Traffic Density Estimation Using Convex Optimization

Applying Machine Learning and Statistical Approaches for Travel Time Estimation in Partial Network Coverage

Traffic State Estimation Using Connected Vehicles and Stationary Detectors

Contact Info

Product

Resources

About