Improving traffic prediction using congestion propagation patterns in smart cities

Nagy, Attila Matyas; Simon, Vilmos

doi:10.1016/j.aei.2021.101343

Cited by 28 publications

(23 citation statements)

References 29 publications

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“…Traffic revitalization index forecasting falls within the field of traffic forecasting [7] , [8] , [9] , [10] , which has been extensively researched and developed in the past decades. Assuming the traffic is static, the existing time series models, such as linear regression [11] and autoregressive integrated moving average models (ARIMA [12] ), are based on linear models to capture the temporal dependence.…”

Section: Introductionmentioning

confidence: 99%

A deep spatio-temporal meta-learning model for urban traffic revitalization index prediction in the COVID-19 pandemic

Wang

Sheng

et al. 2022

Advanced Engineering Informatics

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Section: Introductionmentioning

confidence: 99%

A deep spatio-temporal meta-learning model for urban traffic revitalization index prediction in the COVID-19 pandemic

Wang

Sheng

et al. 2022

Advanced Engineering Informatics

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“…Popular feature mining methods include basic statistics (BS) [24], fast Fourier transform (FFT) [25], short-time Fourier transform (STFT) [26], continuous wavelet transform (CWT) [27], DWT [28], and auto-regressive moving average (ARMA) [29]. Popular machine learning models include SVM [30], k-nearest neighbor (KNN) [31,32], gradient boosting decision tree (GBDT) [33,34], Bayesian network (BN) [35], decision tree (DT) [36], random forest (RF) [37], and CNN [38].…”

Section: Introductionmentioning

confidence: 99%

A Novel Air-Door Opening and Closing Identification Algorithm Using a Single Wind-Velocity Sensor

Shang

Deng

Liu

2022

Sensors

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The air-door is an important device for adjusting the air flow in a mine. It opens and closes within a short time owing to transportation and other factors. Although the switching sensor alone can identify the air-door opening and closing, it cannot relate it to abnormal fluctuations in the wind speed. Large fluctuations in the wind-velocity sensor data during this time can lead to false alarms. To overcome this problem, we propose a method for identifying air-door opening and closing using a single wind-velocity sensor. A multi-scale sliding window (MSSW) is employed to divide the samples. Then, the data global features and fluctuation features are extracted using statistics and the discrete wavelet transform (DWT). In addition, a machine learning model is adopted to classify each sample. Further, the identification results are selected by merging the classification results using the non-maximum suppression method. Finally, considering the safety accidents caused by the air-door opening and closing in an actual production mine, a large number of experiments were carried out to verify the effect of the algorithm using a simulated tunnel model. The results show that the proposed algorithm exhibits superior performance when the gradient boosting decision tree (GBDT) is selected for classification. In the data set composed of air-door opening and closing experimental data, the accuracy, precision, and recall rates of the air-door opening and closing identification are 91.89%, 93.07%, and 91.07%, respectively. In the data set composed of air-door opening and closing and other mine production activity experimental data, the accuracy, precision, and recall rates of the air-door opening and closing identification are 89.61%, 90.31%, and 88.39%, respectively.

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“…But, on many occasions, these methodologies are unprepared to find out the convoluted congestion propagation patterns. This lack of preparation results in erroneous predictions under severe conditions, although exact predictions are required during these important times [6,7]. Such serious conditions occur through multiple factors such as events, traffic threats, extreme weather conditions, and so on.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Slime Mould Optimization with Deep Learning Enabled Traffic Prediction in Smart Cities

Hamza¹,

Alsolai²,

Alamgeer³

et al. 2022

Computers, Materials &Amp; Continua

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Intelligent Transportation System (ITS) is one of the revolutionary technologies in smart cities that helps in reducing traffic congestion and enhancing traffic quality. With the help of big data and communication technologies, ITS offers real-time investigation and highly-effective traffic management. Traffic Flow Prediction (TFP) is a vital element in smart city management and is used to forecast the upcoming traffic conditions on transportation network based on past data. Neural Network (NN) and Machine Learning (ML) models are widely utilized in resolving real-time issues since these methods are capable of dealing with adaptive data over a period of time. Deep Learning (DL) is a kind of ML technique which yields effective performance on data classification and prediction tasks. With this motivation, the current study introduces a novel Slime Mould Optimization (SMO) model with Bidirectional Gated Recurrent Unit (BiGRU) model for Traffic Prediction (SMOBGRU-TP) in smart cities. Initially, data preprocessing is performed to normalize the input data in the range of [0, 1] using minmax normalization approach. Besides, BiGRU model is employed for effective forecasting of traffic in smart cities. Moreover, the novelty of the work lies in using SMO algorithm to effectively adjust the hyperparameters of BiGRU method. The proposed SMOBGRU-TP model was experimentally validated and the simulation results established the model's superior performance in terms of prediction compared to existing techniques.

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Improving traffic prediction using congestion propagation patterns in smart cities

Cited by 28 publications

References 29 publications

A deep spatio-temporal meta-learning model for urban traffic revitalization index prediction in the COVID-19 pandemic

A deep spatio-temporal meta-learning model for urban traffic revitalization index prediction in the COVID-19 pandemic

A Novel Air-Door Opening and Closing Identification Algorithm Using a Single Wind-Velocity Sensor

Intelligent Slime Mould Optimization with Deep Learning Enabled Traffic Prediction in Smart Cities

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