Research of Short-Term Traffic Volume Prediction Based on Kalman Filtering

Yan, Gao; Zhang, Yi

doi:10.1109/icinis.2013.32

Cited by 16 publications

(10 citation statements)

References 8 publications

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“…KF-based traffic prediction research was proposed because of its recursive solid ability. Gong et al [13] suggested a KF-based model for the prediction of short-term traffic volume, and they used the Internet of Things for data collection. Ojeda et al [14] adopted the adaptive scheme to build a multi-step KF-based method for traffic flow prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

Traffic Flow Online Prediction Based on a Generative Adversarial Network with Multi-Source Data

Sun

Jiang

et al. 2021

Sustainability

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Traffic prediction is essential for advanced traffic planning, design, management, and network sustainability. Current prediction methods are mostly offline, which fail to capture the real-time variation of traffic flows. This paper establishes a sustainable online generative adversarial network (GAN) by combining bidirectional long short-term memory (BiLSTM) and a convolutional neural network (CNN) as the generative model and discriminative model, respectively, to keep learning with continuous feedback. BiLSTM constantly generates temporal candidate flows based on valuable memory units, and CNN screens out the best spatial prediction by returning the feedback gradient to BiLSTM. Multi-dimensional indicators are selected to map the multi-view fusion local trend for accurate prediction. To balance computing efficiency and accuracy, different batch sizes are pre-tested and allocated to different lanes. The models are trained with rectified adaptive moment estimation (RAdam) by dividing the dataset into the training and testing sets with a rolling time-domain scheme. In comparison with the autoregressive integrated moving average (ARIMA), BiLSTM, generating adversarial network for traffic flow (GAN-TF), and generating adversarial network for non-signal traffic (GAN-NST), the proposed improved generating adversarial network for traffic flow (IGAN-TF) successfully generates more accurate and stable flows and performs better.

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Section: Literature Reviewmentioning

confidence: 99%

Traffic Flow Online Prediction Based on a Generative Adversarial Network with Multi-Source Data

Sun

Jiang

et al. 2021

Sustainability

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show abstract

“…According to the strong recursive ability of KF, KF‐based traffic predictions were proposed. Based on the KF predictor, Gong et al established a short‐term traffic volume model, using the internet of things for data acquisition [18]. Ojeda et al used the adaptive scheme to establish a multi‐step KF traffic flow prediction model [19], and Guo et al proposed that the adaptive KF short‐term traffic prediction model does not possess a high flow rate.…”

Section: Literature Reviewmentioning

confidence: 99%

Urban traffic flow online prediction based on multi‐component attention mechanism

Sun

Zhang

et al. 2020

IET intell. transp. syst

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“…Chein and Kuchipudi used Kalman filter to predict travel time using real‐time data and historic data. Gong and Zhang proposed a combination of Kalman filter and Grey relational entropy methods to forecast traffic volume using the historic database. Okutani and Stephanedes and Wang and Papageorgiou used Kalman filter and extended Kalman filter techniques respectively to predict traffic volume.…”

Section: Literature Reviewmentioning

confidence: 99%

Short‐term prediction of traffic parameters—performance comparison of a data‐driven and less‐data‐required approaches

Badhrudeen

Raj

Vanajakshi

2016

J of Advced Transportation

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Summary The travel decisions made by road users are more affected by the traffic conditions when they travel than the current conditions. Thus, accurate prediction of traffic parameters for giving reliable information about the future state of traffic conditions is very important. Mainly, this is an essential component of many advanced traveller information systems coming under the intelligent transportation systems umbrella. In India, the automated traffic data collection is in the beginning stage, with many of the cities still struggling with database generation and processing, and hence, a less‐data‐demanding approach will be attractive for such applications, if it is not going to reduce the prediction accuracy to a great extent. The present study explores this area and tries to answer this question using automated data collected from field. A data‐driven technique, namely, artificial neural networks (ANN), which is shown to be a good tool for prediction problems, is taken as an example for data‐driven approach. Grey model, GM(1,1), which is also reported as a good prediction tool, is selected as the less‐data‐demanding approach. Volume, classified volume, average speed and classified speed at a particular location were selected for the prediction. The results showed comparable performance by both the methods. However, ANN required around seven times data compared with GM for comparable performance. Thus, considering the comparatively lesser input requirement of GM, it can be considered over ANN in situations where the historic database is limited. Copyright © 2016 John Wiley & Sons, Ltd.

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Research of Short-Term Traffic Volume Prediction Based on Kalman Filtering

Cited by 16 publications

References 8 publications

Traffic Flow Online Prediction Based on a Generative Adversarial Network with Multi-Source Data

Traffic Flow Online Prediction Based on a Generative Adversarial Network with Multi-Source Data

Urban traffic flow online prediction based on multi‐component attention mechanism

Short‐term prediction of traffic parameters—performance comparison of a data‐driven and less‐data‐required approaches

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