Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification

Guo, Jianhua; Huang, Wei; Williams, Billy M.

doi:10.1016/j.trc.2014.02.006

Cited by 533 publications

(231 citation statements)

References 54 publications

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“…sensor noise caused by poor illumination and/or high temperature, and/or transmission e.g. electronic circuit noise Speckle noise (multiplicative noise) can be caused by random values that added to video frames [14,15]. Images obtained from these surfaces by coherent imaging systems such as laser, SAR, and ultrasound suffer from a common phenomenon called speckle.…”

Section: Video Degradation and Suggested Filtering Techniquesmentioning

confidence: 99%

Traffic Video Enhancement based Vehicle Correct Tracked Methodology

Ata¹,

El-Darieby²,

Elnaby³

et al. 2017

IJIGSP

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Abstract-In this paper, an enhancement based traffic video has been proposed in the state of the art of computer vision. The main target is to develop a decision making criteria for removing the most probable video degradations. Such traffic video degradations would have an adverse impact on the transportation system. In order to establish the appropriate analysis, three types of video degradations have been added to the test video; salt and pepper noise, Gaussian noise, and speckle noise, we have simulated rainy, fog, and darkness conditions for the traffic video. First of all, back ground subtraction and Kalman filter techniques have been used for detecting and tracking vehicles respectively. By using such algorithms, it would be easily to estimate average number of assigned tracks which express the efficacy of correct detection and prediction of vehicles in each frame. Furthermore, video degradations would be applied in order to studying its effect on the average number of assigned tracks which would be deviated than noiseless video. Spatial filtering system has been applied to state the most suitable filter mask which satisfy the least deviation in the average number of assigned tracks. Experimental results show that median filter satisfies the least deviation in all cases of video degradations.

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Section: Video Degradation and Suggested Filtering Techniquesmentioning

confidence: 99%

Traffic Video Enhancement based Vehicle Correct Tracked Methodology

Ata¹,

El-Darieby²,

Elnaby³

et al. 2017

IJIGSP

View full text Add to dashboard Cite

show abstract

“…The first one focuses on time based rules and can be applied to short or long period forecast [4]. The second one mainly optimizes the indexes, when it comes to forecasting [5]. The third one depends more on newly updated data, and the last one can reflect the differences between the different emergency accidents and changing weights of other factors [6].…”

Section: Passenger Data Monitoring and Forecastingmentioning

confidence: 99%

Congestion Risk Evaluation and Precaution of Passenger Flow in Metro Stations

Lu¹,

Han²

2016

TOCIEJ

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Abstract:Given the security problems of the urban rail transportation, this paper proposes a methodology for congestion risk evaluation and precaution in order to guarantee the safety of passengers and improve the operation efficiency in metro stations. To begin with, we selected the retention rate of the platform, the waiting rate of the ticket gate and the transfer efficiency as the indexes for the station congestion precaution system and created models to calculate the threshold respectively. By reasonably combining these three indexes, a congestion warning level function was set up to evaluate the congestion risk and determine the warning level. The characteristics of the proposed model are its quick decision results and ease of handling. With the determined warning level, some strategies are suggested to relieve the pressure. Finally, a practical application in Beijing's GuoMao metro station was performed to verify the feasibility and practicability of the proposed congestion precaution model.

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“…Some of these methods employ statistical time series analysis methods like autoregressive integrated moving average (ARIMA) model [1][2], seasonal ARIMA (SARIMA) model [3], autoregressive conditional heteroscedasticity model [4], generalized autoregressive conditional heteroscedasticity (GARCH) model and their combination. For example, Guo et al [5] use SARIMA + GARCH to model traffic flow series and employ the adaptive Kalman filtering approach to implement this SARIMA + GARCH structure. Wang et al [6] propose a new Bayesian combination method to overcome the deficiency of the traditional model.…”

Section: Introductionmentioning

confidence: 99%

The Short-term Prediction of Vehicle Speed of Urban Freeway Based on RBF Neural Network

Gu¹,

Lu²,

Wang³

et al. 2017

dtcse

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Abstract. The short-term prediction of traffic flow is a significant way to help predicting and solving traffic congestion. In order to describe traffic state, speed is used as a parameter to find the prediction model. By analyzing the temporal and spatial relevance of speed, a speed prediction model is established based on temporal-spatial characteristics and radial basis function (RBF) neural network. With the simulation and analysis of microwave data of Second Ring in Beijing, the prediction result demonstrates that the prediction method based on temporal and spatial characteristics can effectively improve the prediction accuracy of speed compared with the prediction method based on a single time series, where the mean absolute relative error (MAPE) decreases from 7.45% to 3.61%.

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Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification

Cited by 533 publications

References 54 publications

Traffic Video Enhancement based Vehicle Correct Tracked Methodology

Traffic Video Enhancement based Vehicle Correct Tracked Methodology

Congestion Risk Evaluation and Precaution of Passenger Flow in Metro Stations

The Short-term Prediction of Vehicle Speed of Urban Freeway Based on RBF Neural Network

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