Large-Scale Road Network Congestion Pattern Analysis and Prediction Using Deep Convolutional Autoencoder

Ranjan, Navin; Bhandari, Sovit; Khan, Pervez; Hong, Youn-Sik; Kim, Hoon

doi:10.3390/su13095108

Cited by 19 publications

(12 citation statements)

References 49 publications

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“…In this study, we used the road traffic congestion data in the form of a roadmap, which was obtained from our previous work [47,48], as shown in Figure 1. The data was collected by taking snapshots of traffic congestion maps from the open-source online web service, Seoul Transportation Operation and Information Service (TOPIS).…”

Section: Data Sourcementioning

confidence: 99%

Large-Scale Road Network Traffic Congestion Prediction Based on Recurrent High-Resolution Network

et al. 2023

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Traffic congestion is a significant problem that adversely affects the economy, environment, and public health in urban areas worldwide. One promising solution is to forecast road-level congestion levels in the short-term and long-term, enabling commuters to avoid congested areas and allowing traffic agencies to take appropriate action. In this study, we propose a hybrid deep neural network algorithm based on High-Resolution Network (HRNet) and ConvLSTM decoder for 10, 30, and 60-min traffic congestion prediction. Our model utilizes the HRNet’s multi-scale feature extraction capability to capture rich spatial features from a sequence of past traffic input images. The ConvLSTM module learns temporal information from each HRNet multi-scale output and aggregates all feature maps to generate accurate traffic forecasts. Our experiments demonstrate that the proposed model can efficiently and effectively learn both spatial and temporal relationships for traffic congestion and outperforms four other state-of-the-art architectures (PredNet, UNet, ConvLSTM, and Autoencoder) in terms of accuracy, precision, and recall. A case study was conducted on the dataset from Seoul, South Korea.

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Section: Data Sourcementioning

confidence: 99%

Large-Scale Road Network Traffic Congestion Prediction Based on Recurrent High-Resolution Network

et al. 2023

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“…Here transpose (•) represent 2D transpose convolution with strides of 2 × 2 and filter kernel size of 2 × 2. After adding skip connection o 1 1:p from frame optical-flow Convolutional Residual Block to the transpose layer of 1 th decoder block, we get the output of transposed layer o 1 TO,1:p as in (15), and based on (4), we can compute the output of optical-flow decoder block 1, as in ( 16)…”

Section: Reconstruction Networkmentioning

confidence: 99%

“…Such predictive cognitive neural networks are often considered the essence of computer vision. They play a critical role in a variety of applications, such as abnormal event detection [1], autonomous driving [2][3][4], intention prediction in robotics [5,6], video coding [7,8], collision avoidance systems [9,10], activity and event prediction [11,12], and pedestrian and traffic prediction [13][14][15]. However, modeling future image content and object motion is challenging due to dynamic evolution and image complexity, such as occlusions, camera movements, and illumination.…”

Section: Introductionmentioning

confidence: 99%

Video Frame Prediction by Joint Optimization of Direct Frame Synthesis and Optical-Flow Estimation

Ranjan¹,

Bhandari²,

Kim³

et al. 2023

Computers, Materials &Amp; Continua

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Video prediction is the problem of generating future frames by exploiting the spatiotemporal correlation from the past frame sequence. It is one of the crucial issues in computer vision and has many real-world applications, mainly focused on predicting future scenarios to avoid undesirable outcomes. However, modeling future image content and object is challenging due to the dynamic evolution and complexity of the scene, such as occlusions, camera movements, delay and illumination. Direct frame synthesis or optical-flow estimation are common approaches used by researchers. However, researchers mainly focused on video prediction using one of the approaches. Both methods have limitations, such as direct frame synthesis, usually face blurry prediction due to complex pixel distributions in the scene, and optical-flow estimation, usually produce artifacts due to large object displacements or obstructions in the clip. In this paper, we constructed a deep neural network Frame Prediction Network (FPNet-OF) with multiplebranch inputs (optical flow and original frame) to predict the future video frame by adaptively fusing the future object-motion with the future frame generator. The key idea is to jointly optimize direct RGB frame synthesis and dense optical flow estimation to generate a superior video prediction network. Using various real-world datasets, we experimentally verify that our proposed framework can produce high-level video frame compared to other state-ofthe-art framework.

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“…CNNs are a type of ANNs that emerged from the study of the brain visual cortex for image recognition purposes. Those networks revolutionized pattern recognition for image-based problems such as medical imaging (Hameurlaine et al, 2019;Shen et al, 2017) or traffic prediction and autonomous vehicles (Ranjan et al, 2020(Ranjan et al, , 2021. A CNN is a collection of convolutional layers, fully connected layers, and pooling layers where convolutional layers are the most important blocks.…”

Section: Artificial and Convolutional Neural Networkmentioning

confidence: 99%

Fault classification using convolutional neural networks and color channels for time-frequency analysis of acoustic emissions

Nashed

Renno

Mohamed

2023

Journal of Vibration and Control

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We present a novel method for real-time fault classification using the time history of acoustic emissions (AEs) recorded from a lab-scale gas turbine operating under normal and faulty conditions across multiple turbine speeds. Time-frequency features are extracted using the continuous wavelet transform, and for each signal, the root mean square (RMS) and kurtosis are calculated. We employ a color mapping technique to combine the time-frequency and statistical features into a single red–green–blue (RGB) image. The red channel is mapped to the time-frequency data, whereas the green and blue channels are mapped to the RMS and kurtosis, respectively. Subsequently, a deep convolutional neural network is trained on the generated images to classify the gas turbine condition. We show that the proposed model can form an online monitoring system using AEs to classify multiple running conditions at various turbine speeds. The methodology not only achieves real-time classification of faults but also minimizes the human intervention in identifying these faults. The datasets and codes used in this paper will be openly available.

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Large-Scale Road Network Congestion Pattern Analysis and Prediction Using Deep Convolutional Autoencoder

Cited by 19 publications

References 49 publications

Large-Scale Road Network Traffic Congestion Prediction Based on Recurrent High-Resolution Network

Large-Scale Road Network Traffic Congestion Prediction Based on Recurrent High-Resolution Network

Video Frame Prediction by Joint Optimization of Direct Frame Synthesis and Optical-Flow Estimation

Fault classification using convolutional neural networks and color channels for time-frequency analysis of acoustic emissions

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