Speed Estimation from Single Loop Data Using an Unscented Particle Filter

Ye, Zhirui; Zhang, Yunlong

doi:10.1111/j.1467-8667.2009.00634.x

Cited by 11 publications

(4 citation statements)

References 45 publications

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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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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

Self Cite

View full text Add to dashboard Cite

show abstract

“…The calibration and validation of the model is another critical task prior to the deployment of the model to real‐road networks. Another research direction would be to investigate the impact of online information provision (e.g., using loop detectors and variable message signs, Liu and Danczyk, 2009; Larsson et al, 2010; Ye and Zhang, 2010) on the traffic patterns in a road network. The bounds derived in were based on a particular set of inequalities, that is, traffic flow patterns might change if one is able to obtain other, tighter bounds.…”

Section: Discussionmentioning

confidence: 99%

A Dynamic Route Choice Model Considering Uncertain Capacities

Waller

2011

Computer aided Civil Eng

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The standard assumption in (dynamic) traffic assignment models is that route choice is solely determined by a (perceived) deterministic travel time. However, recently, there is a growing interest in (dynamic) equilibrium route choice models in which travelers not only select their paths based on an estimated deterministic travel time, but also based on travel time reliability, in this article defined as the probability that the actual travel time deviates from the anticipated value. We extend the linear programming cell transmission model‐based dynamic traffic assignment (LP CTM‐DTA) model to account for travelers’ consideration of uncertainty regarding saturation flow rates (in this article referred to as capacities). It is shown that these reliability considerations can be accounted for by simply reducing the road capacities appearing in the constraint set of the classical LP CTM‐DTA model. More importantly, we provide results on the amount of capacity reduction necessary to ensure a certain reliability level. Although in the proposed model any probability distribution can be used to model the uncertainty, the selection of a specific probability distribution can potentially be burdensome for the modeler. To this end, we also present results on the class of symmetric probability distributions that has been particularly popular in the robust optimization literature. Properties for this broad class of distributions will be derived within the context of the introduced model. In numerical case studies, the model predicts that travel patterns can be significantly different when accounting for travelers’ reliability considerations.

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“…It relies on a Bayesian inference procedure to estimate the latent state for nonlinear problems based on observed data. Further, the likelihood is sequentially estimated to conduct a convenient inference procedure (Kantas, Doucet, Singh, & Maciejowski, 2009;Särkkä, 2013;Ye & Zhang, 2010). Thus, the observed deterioration model is formulated through the HMM, so the stochastic particle filtering procedure is performed to provide an estimate of the hidden state from the measured data.…”

Section: Introductionmentioning

confidence: 99%

A Perturbed Markovian process with state‐dependent increments and measurement uncertainty in degradation modeling

Oumouni

Schoefs

2021

Computer aided Civil Eng

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In structural reliability, the Markovian cumulative damage approaches such as Gamma process seem promising to model a nonreversible deterioration that involves gradually over time with small time increments. However, in many degradation phenomena, its evolution depends on the level of the degradation rather than the increments of time. Further, the measured data are usually collected with random error whose dispersion depends on the current degradation level. Therefore, the deterioration model should take in consideration such dependency and uncertainty for more accurate prediction. In this paper, a new statistical, data-driven state-dependent and perturbed model is proposed to model the state dependence (hidden level-degradation) both in temporal increments and measurement uncertainty. The construction of the degradation model will be discussed within an application to the pitting corrosion and synthetic data. Numerical experiments will later be conducted to identify preliminary properties of the model in terms of statistical inferences. Algorithm and estimates are proposed to compute the parameters of the model, the hidden state, and failure probabilities.

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Speed Estimation from Single Loop Data Using an Unscented Particle Filter

Cited by 11 publications

References 45 publications

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

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

A Dynamic Route Choice Model Considering Uncertain Capacities

A Perturbed Markovian process with state‐dependent increments and measurement uncertainty in degradation modeling

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