Unscented Kalman Filter Method for Speed Estimation Using Single Loop Detector Data

Ye, Zhirui; Zhang, Yunlong; Middleton, Dan

doi:10.3141/1968-14

Cited by 25 publications

(13 citation statements)

References 9 publications

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“…The estimation results under congested conditions are also presented in the table. It can be seen that the UPF has the best estimation accuracy with the lowest MAEs and smallest variances of estimation errors in all cases; the UKF outperforms the EKF, which confirms the results from the earlier study by Ye et al (2006).…”

Section: Applicationssupporting

confidence: 87%

“…A constant MEVL was used for each data set. Refer to the study by Ye et al (2006) for the determination of MEVL. Two Measures of Effectiveness (MOEs) were used for result evaluation.…”

Section: Applicationsmentioning

confidence: 99%

“…Examples of these methods include g‐estimator (Athol, 1965; Coifman et al, 2003; Lin et al, 2004), log‐linear regression method (Wang and Nihan, 2000), exponential smoothing method (Hellinga, 2002), catastrophe theory method (Hall, 1987; Pushkar et al, 1994), sequence method (Neelisetty and Coifman, 2004), Kalman filters (Dailey, 1999; Ye et al, 2006), Bayesian approach (Li, 2009), and distribution method (Coifman and Kim, 2009). The Kalman filter methods reported good results compared with other existing methods (Ye et al, 2006), but they have an inherent assumption of Gaussian distribution on speed that may not be always valid. Studies have found that the normal distribution may describe speed distribution accurately only for homogenous traffic streams with light to moderate traffic volume.…”

Section: Introductionmentioning

confidence: 99%

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Speed Estimation from Single Loop Data Using an Unscented Particle Filter

Zhang

2010

Computer-Aided Civil and Infrastructure Engineering

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This article presents a hybrid method, the Unscented Particle Filter (UPF), for traffic flow speed estimation using single loop outputs. The Kalman filters used in past speed estimation studies employ a Gaussian assumption that is hardly satisfied. The hybrid method that combines a parametric filter (Unscented Kalman Filter) and a nonparametric filter (Particle Filter) is thus proposed to overcome the limitations of the existing methods. To illustrate the advantage of the proposed approach, two data sets collected from field detectors along with a simulated data set are utilized for performance evaluation and comparison with the Extended Kalman Filter and the Unscented Kalman Filter. It is found that the proposed method outperforms the evaluated Kalman filter methods. The UPF method produces accurate speed estimation even for congested flow conditions in which many other methods have significant accuracy problems.

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Section: Applicationssupporting

confidence: 87%

“…A constant MEVL was used for each data set. Refer to the study by Ye et al (2006) for the determination of MEVL. Two Measures of Effectiveness (MOEs) were used for result evaluation.…”

Section: Applicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Speed Estimation from Single Loop Data Using an Unscented Particle Filter

Zhang

2010

Computer-Aided Civil and Infrastructure Engineering

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“…The data driven model is the main method of short-term prediction and can be divided into linear, nonlinear, and hybrid forecasting methods. The linear forecasting method mainly includes time series model [7][8][9] and Kalman filtering model [10][11][12]. The nonlinear forecasting method includes nonparametric regression [13,14], neural network algorithm [15][16][17], support vector machine [18][19][20], and Gaussian maximum likelihood model [21].…”

Section: Introductionmentioning

confidence: 99%

Forecasting of Short-Term Metro Ridership with Support Vector Machine Online Model

Wang¹,

Zhang

et al. 2018

Journal of Advanced Transportation

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Forecasting for short-term ridership is the foundation of metro operation and management. A prediction model is necessary to seize the weekly periodicity and nonlinearity characteristics of short-term ridership in real-time. First, this research captures the inherent periodicity of ridership via seasonal autoregressive integrated moving average model (SARIMA) and proposes a support vector machine overall online model (SVMOOL) which insets the weekly periodic characteristics and trains the updated data day by day. Then, this research captures the nonlinear characteristics of the ridership via successive ridership value inputs and proposes a support vector machine partial online model (SVMPOL) which insets the nonlinear characteristics and trains the updated data of the predicted day by time interval (such as 5-min). Afterwards, to avoid the drawbacks and to take advantages of the strengths of the two individual online models, this research takes the average predicted values of two models as the final predicted values, which are called support vector machine combined online model (SVMCOL). Finally, this research uses the 5-min ridership at Zhujianglu and Sanshanjie Stations of Nanjing Metro to compare the SVMCOL model with three well-known prediction models including SARIMA, back-propagation neural network (BPNN), and SVM models. The resultant performance comparisons suggest that SARIMA is superior for the stable weekday ridership to other models. Yet the SVMCOL model is the best performer for the unstable weekend ridership and holiday ridership. It shows that for metro operation manager that gear toward timely response to real-world unstable and abnormal situations, the SVMCOL may be a better tool than the three well-known models.

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“…Common short-term traffic flow forecasting includes methods based on linear theory, non-linear theory, mixture theory and other methods (Lu, 2009;JIN, 2011). Among them, method based on linear theory mainly includes history average model, time-series model and state space model (Nadhir, 2002;Ahmaed, 1979;Ye, 2006). History average model is earlier been studied, whose method is simple and easily applied.…”

Section: Introductionmentioning

confidence: 99%

An Improved k-NN Nonparametric Regression-Based Short-Term Traffic Flow Forecasting Model for Urban Expressways

Zhang¹,

Rao²,

Yang³

et al. 2013

Icte 2013

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In domestic major cities, the development of Urban Expressway is network-oriented. The traffic flow forecasting system is the important prerequisite and foundation of realizing real-time traffic management and control. However, the traffic flow forecasting research is mainly based on highways. Research and application of short-term traffic forecasting for urban expressway is severely insufficient. Therefore, the study of urban expressway flow forecasting is discussed and a short-term traffic flow forecasting system for urban expressway based on k-NN nonparametric regression model is proposed in this study. First, the study analyze the characteristics and needs of the urban expressway traffic flow, introduce k-NN nonparametric regression model and design the short-term traffic flow forecasting system based on k-NN overall. Then, the short-term urban expressway flow forecasting system based on k-NN is established in three aspects: the historical database, the search mechanism and algorithm parameters, and the forecasting plan. At last, a short-term traffic forecasting for urban expressway based on k-NN nonparametric regression model is developed in the VS2010 VC++ platform. Utilizing the Shanghai urban expressway section measured traffic flow data, the comparison of average and weighted k-NN nonparametric regression model is discussed and the reliability of the forecasting result is analyzed. Results show that the accuracy of the proposed method, under the 5-minute interval, is over 90 percent, which best proves the reasonableness of the proposed forecasting model based on k-NN nonparametric model.

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Unscented Kalman Filter Method for Speed Estimation Using Single Loop Detector Data

Cited by 25 publications

References 9 publications

Speed Estimation from Single Loop Data Using an Unscented Particle Filter

Speed Estimation from Single Loop Data Using an Unscented Particle Filter

Forecasting of Short-Term Metro Ridership with Support Vector Machine Online Model

An Improved k-NN Nonparametric Regression-Based Short-Term Traffic Flow Forecasting Model for Urban Expressways

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