Short‐Term Traffic Volume Forecasting Using Kalman Filter with Discrete Wavelet Decomposition

Zhang, Yunlong; Xie, Yuanchang; Ye, Zhirui

doi:10.1111/j.1467-8667.2007.00489.x

Cited by 279 publications

(146 citation statements)

References 28 publications

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“…Tremendous univariate prediction models have been proposed including parameter models such as time series models [1][2][3], Kalman filtering [4][5], support vector machine [6][7], and some non-parameter models such as nonparametric regressive model [8][9] and neural network model [10][11][12]. To further improve the short-term forecasting accuracy, some multivariate models was introduced to calibrate the relationships between different traffic flow variables at a traffic station or the same variable at different traffic stations.…”

Section: Advanced Traveler Information Systems (Atis) and Active Signmentioning

confidence: 99%

Comparative Evaluation of The Traffic Flow Volatility Forecasting Models

Lu¹,

Xue²,

Chen³

et al. 2017

Proceedings of the International Conference on Communication and Electronic Information Engineering (CEIE 2016)

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Section: Advanced Traveler Information Systems (Atis) and Active Signmentioning

confidence: 99%

Comparative Evaluation of The Traffic Flow Volatility Forecasting Models

Lu¹,

Xue²,

Chen³

et al. 2017

Proceedings of the International Conference on Communication and Electronic Information Engineering (CEIE 2016)

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“…For instance, Vlahogianni et al [36] proposed a modular neural predictor in short-term traffic volume forecasting and incorporating temporal and spatial volume characteristics to improve the prediction accuracy from multiple locations of an urban signalized arterial roadway. Xie et al [38] investigated the application of a Kalman filter with discrete wavelet analysis in short-term traffic volume forecasting. To improve the prediction accuracy, discrete wavelet decomposition and reconstruction analysis was used to divide the original data into several approximate and detailed data, and then the Kalman filter model was applied to the denoised data only.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Approach for Short-Term Freeway Traffic Prediction During Peak Periods

Qi¹,

Ishak²

2013

IEEE Trans. Intell. Transport. Syst.

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Using a stochastic approach, this paper explores and models the basic stochastic characteristics of freeway traffic behavior under a wide range of traffic conditions during peak periods and then applies the models to short-term traffic speed prediction. The speed transition probabilities are estimated from real-world 30-s speed data over a six-year period at three different locations along the 38-mi corridor of Interstate 4 (I-4) in Orlando, FL. The cumulative negative/positive transition probabilities and expected values are derived from the transition probabilities and fitted using logistic and exponential models, respectively. The expected values associated with the most likely transition of speed are then derived from the fitted models and used for predicting speed. Each predicted speed is also associated with a probability value, indicating the chance of observing the occurrence of such transition. The prediction performance was compared for three methods using the root mean square errors (RMSEs). The weighted average method was very close to the higher probability method in most cases. For the two probabilistic methods, the performance was slightly better for the morning peak periods than the evening peak period or all data combined. While the prediction performance of the probabilistic models was comparable with those of other methods found in the literature, the probabilistic approach based on the higher probability provides estimates of the associated probability with each prediction. This provides a measure of confidence in the predicted values before such information is disseminated to the public by traffic agencies.

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“…The Kalman filter is an algorithm that operates recursively on streams of noisy input data to produce a statistically optimal estimate of the underlying system state. They have been applied in many areas, including navigation (Yim et al, 2011), water demand prediction (Nasseri et al, 2011), and traffic volume forecasting (Xie et al, 2007). An introduction to Kalman filter theory is given by Haykin (2001).…”

Section: Kalman Filtermentioning

confidence: 99%

Short-term traffic safety forecasting using Gaussian mixture model and Kalman filter

Jin

Wang

Cheng

et al. 2013

J. Zhejiang Univ. Sci. A

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Abstract:In this paper, a prediction model is developed that combines a Gaussian mixture model (GMM) and a Kalman filter for online forecasting of traffic safety on expressways. Raw time-to-collision (TTC) samples are divided into two categories: those representing vehicles in risky situations and those in safe situations. Then, the GMM is used to model the bimodal distribution of the TTC samples, and the maximum likelihood (ML) estimation parameters of the TTC distribution are obtained using the expectation-maximization (EM) algorithm. We propose a new traffic safety indicator, named the proportion of exposure to traffic conflicts (PETTC), for assessing the risk and predicting the safety of expressway traffic. A Kalman filter is applied to forecast the short-term safety indicator, PETTC, and solves the online safety prediction problem. A dataset collected from four different expressway locations is used for performance estimation. The test results demonstrate the precision and robustness of the prediction model under different traffic conditions and using different datasets. These results could help decision-makers to improve their online traffic safety forecasting and enable the optimal operation of expressway traffic management systems.

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Short‐Term Traffic Volume Forecasting Using Kalman Filter with Discrete Wavelet Decomposition

Cited by 279 publications

References 28 publications

Comparative Evaluation of The Traffic Flow Volatility Forecasting Models

Comparative Evaluation of The Traffic Flow Volatility Forecasting Models

Stochastic Approach for Short-Term Freeway Traffic Prediction During Peak Periods

Short-term traffic safety forecasting using Gaussian mixture model and Kalman filter

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