Traffic Mosaic: Traffic Data Imputation Method Inspired by Image Super Resolution

The loss of traffic state data is a common problem in intelligent transportation system. To improve the imputation accuracy and robustness of road traffic data, a novel generative adversarial network for the imputation of road network traffic data is proposed in this paper, i.e., GAE-GAN-LSTM. The spatiotemporal characteristics were extracted using a improved graph auto-encoder (GAE), followed by a generative adversarial network (GAN) to generate the complete spatiotemporal characteristics on the basis of the missing features. The internal structure of the generator was a long short-term memory network (LSTM), and the internal structure of the discriminator was a fully connected neural network (FCN). Finally, the traffic state data could be recovered by the decoder of GAE. The experimental results revealed that the performance of the proposed method was better than that of the other methods at any data loss ratio considered. The main innovations of the proposed method include two aspects. One, an improved GAE for the imputation of road network data was presented by redefining the loss function of GAE, which could effectively extract the potential spatiotemporal features of a road network. Two, the GAN was used to generate the spatiotemporal characteristics of the traffic state data by using the strong data generation ability of GAN.KeywordsData imputationGraph auto-encoderGenerative adversarial networkRoad traffic state data

Data missing remains a difficult and important problem in the transportation information system, which seriously restricts the application of the intelligent transportation system (ITS), dominatingly on traffic monitoring, e.g., traffic data collection, traffic state estimation, and traffic control. Numerous traffic data imputation methods had been proposed in the last decade. However, lacking of sufficient temporal variation characteristic analysis as well as spatial correlation measurements leads to limited completion precision, and poses a major challenge for an ITS. Leveraging the low-rank nature and the spatial-temporal correlation of traffic network data, this paper proposes a novel approach to reconstruct the missing traffic data based on low-rank matrix factorization, which elaborates the potential implications of the traffic matrix by decomposed factor matrices. To further exploit the temporal evolvement characteristics and the spatial similarity of road links, we design a time-series constraint and an adaptive Laplacian regularization spatial constraint to explore the local relationship with road links. The experimental results on six real-world traffic data sets show that our approach outperforms the other methods and can successfully reconstruct the road traffic data precisely for various structural loss modes.

Nowadays, satellite images are used in various governmental applications, such as urbanization and monitoring the environment. Spatial resolution is an element of crucial impact on the usage of remote sensing imagery. As such, increasing the spatial resolution of an image is an important pre-processing step that can improve the performance of various image processing tasks, such as segmentation. Once a satellite is launched, the more practical solution to improve the resolution of its captured images is to use Single Image Super Resolution (SISR) techniques. In the recent years, Deep Convolutional Neural Networks (DCNNs) have been recognized as a highly effective tool to reconstruct a High Resolution (HR) image from its Low Resolution (LR) counterpart, which is an open problem due to the inherent difficulty of estimating the missing high frequency components. The aim of this research paper is to design and implement a satellite image SISR algorithm by estimating high frequency details through training Deep Convolutional Neural Network (DCNNs) with respect to wavelet analysis. The goal is to improve the spatial resolution of multispectral remote sensing images captured by DubaiSat-2 satellite. The accuracy of the proposed algorithm is assessed using several metrics such as Peak Signal-to-Noise Ratio (PSNR), Wavelet-based Signal-to-Noise Ratio (WSNR) and Structural Similarity Index Measurement (SSIM).

Traffic data is a fundamental component for applications and researches in transportation systems. However, real traffic data collected from loop detectors or other channels often include missing data which affects the relative applications and researches. This paper proposes an approach based on deep learning to impute the missing traffic data. The proposed approach treats the traffic data including observed data and missing data as a whole data item and restores the complete data with the deep structural network. The deep learning approach can discover the correlations contained in the data structure by a layer-wise pre-training and improve the imputation accuracy by conducting a fine-tuning afterwards. The authors analyze the imputation patterns that can be realized with the proposed approach and conduct a series of experiments. The results show that the proposed approach can keep a stable error under different traffic data missing rate. Deep learning is promising in the field of traffic data imputation.

BackgroundMissing data are a common problem in prospective studies with a long follow-up, and the volume, pattern and reasons for missing data may be relevant when estimating the cost of illness. We aimed to evaluate the effects of different methods for dealing with missing longitudinal cost data and for costing caregiver time on total societal costs in Alzheimer’s disease (AD).MethodsGERAS is an 18-month observational study of costs associated with AD. Total societal costs included patient health and social care costs, and caregiver health and informal care costs. Missing data were classified as missing completely at random (MCAR), missing at random (MAR) or missing not at random (MNAR). Simulation datasets were generated from baseline data with 10–40 % missing total cost data for each missing data mechanism. Datasets were also simulated to reflect the missing cost data pattern at 18 months using MAR and MNAR assumptions. Naïve and multiple imputation (MI) methods were applied to each dataset and results compared with complete GERAS 18-month cost data. Opportunity and replacement cost approaches were used for caregiver time, which was costed with and without supervision included and with time for working caregivers only being costed.ResultsTotal costs were available for 99.4 % of 1497 patients at baseline. For MCAR datasets, naïve methods performed as well as MI methods. For MAR, MI methods performed better than naïve methods. All imputation approaches were poor for MNAR data. For all approaches, percentage bias increased with missing data volume. For datasets reflecting 18-month patterns, a combination of imputation methods provided more accurate cost estimates (e.g. bias: −1 % vs −6 % for single MI method), although different approaches to costing caregiver time had a greater impact on estimated costs (29–43 % increase over base case estimate).ConclusionsMethods used to impute missing cost data in AD will impact on accuracy of cost estimates although varying approaches to costing informal caregiver time has the greatest impact on total costs. Tailoring imputation methods to the reason for missing data will further our understanding of the best analytical approach for studies involving cost outcomes.Electronic supplementary materialThe online version of this article (doi:10.1186/s12874-016-0188-1) contains supplementary material, which is available to authorized users.

The exponential increase in the use of technology in road management systems has led to real-time visual information in thousands of locations on road networks. A previous step in preventing or detecting accidents involves identifying vehicles on the road. The application of convolutional neural networks in object detection has significantly improved this field, enhancing classical computer vision techniques. Although, there are deficiencies due to the low detection rate provided by the available pre-trained models, especially for small objects. The main drawback is that they require manual labeling of the vehicles that appear in the images from each IP camera located on the road network to retrain the model. This task is not feasible if we have thousands of cameras distributed across the extensive road network of each nation or state. Our proposal presented a new automatic procedure for detecting small-scale objects in traffic sequences. In the first stage, vehicle patterns detected from a set of frames are generated automatically through an offline process, using super-resolution techniques and pre-trained object detection networks. Subsequently, the object detection model is retrained with the previously obtained data, adapting it to the analyzed scene. Finally, already online and in real-time, the retrained model is used in the rest of the traffic sequence or the video stream generated by the camera. This framework has been successfully tested on the NGSIM and the GRAM datasets.

A tensor-based method for missing traffic data completion

The quality of traffic data is crucial for modern transportation planning and operations. However, data could be missing for various reasons. Hence, the data imputation approaches which aim at predicting/replacing the missing data or bad data have been considered very important. The traditional traffic data imputation approaches mainly focus on using different probability models or regression methods to impute data, and they only take limited temporal or spatial information as inputs. Thus, they are not very accurate especially for data with a high missing ratio. To overcome the weaknesses of previous approaches, this study proposes an innovative traffic data imputation method, which first transforms the raw data into spatial‐temporal images and then implements a deep‐learning method on the images. The key idea of this approach is developing a convolutional neural network (CNN)‐based context encoder to reconstruct the complete image from the missing source. To the best of the authors’ knowledge, this is the first time a CNN method has been incorporated for traffic data imputation. Experiments are conducted on three months of data from 256 loop detectors. Through comparison with two state‐of‐the‐art approaches, the results indicate that this new approach increases the imputation accuracy greatly and has a stable error distribution.

The increasing complexity of urban transportation systems and the growing volume of vehicles have made traffic congestion a persistent challenge in modern cities. Efficient traffic flow prediction is essential for mitigating congestion, improving road safety, optimizing traffic signal control, and enhancing overall transportation efficiency. In recent years, artificial intelligence (AI) has emerged as a transformative tool in the field of traffic management, offering sophisticated algorithms capable of modeling, analyzing, and predicting complex traffic patterns with high accuracy. The application of AI in traffic flow prediction leverages vast amounts of real-time and historical data to generate precise forecasts, supporting data-driven decision-making by urban planners and traffic control authorities. The prediction of traffic flow involves analyzing time-series data that exhibit nonlinear, dynamic, and often stochastic behavior. Traditional statistical models, such as autoregressive integrated moving average (ARIMA), have proven to be limited in handling the high dimensionality and variability inherent in traffic systems. In contrast, AI algorithms possess the capacity to learn and adapt from complex data inputs without the need for explicit programming, making them particularly suitable for traffic-related applications. AI algorithms used in traffic flow prediction can be broadly categorized into machine learning (ML) and deep learning (DL) approaches. Machine learning algorithms such as k-nearest neighbors (KNN), support vector machines (SVM), decision trees, and random forests have demonstrated effectiveness in short-term traffic prediction tasks. These algorithms are capable of identifying hidden patterns in traffic data and adjusting to changes in traffic behavior over time. Ensemble methods, which combine the strengths of multiple learning models, further enhance prediction accuracy and robustness. Deep learning algorithms, a subfield of AI inspired by the human brain’s neural architecture, have shown exceptional performance in capturing spatial-temporal dependencies in traffic data. Recurrent neural networks (RNNs), particularly long short-term memory (LSTM) networks and gated recurrent units (GRUs), are widely used for their ability to process sequential data and retain information over extended time intervals. Convolutional neural networks (CNNs) are employed to extract spatial features from traffic sensor data or road network imagery. Hybrid models that integrate CNNs with RNNs have achieved high levels of predictive precision by simultaneously learning spatial and temporal correlations. In addition to supervised learning methods, unsupervised and reinforcement learning techniques are also applied in traffic flow prediction. Clustering algorithms, such as k-means and DBSCAN, assist in identifying traffic patterns, while reinforcement learning models optimize adaptive traffic signal control systems by learning optimal actions through environmental interaction. This study explores the different types of AI algorithms used in traffic flow prediction, examining their theoretical foundations, structural differences, and practical applications. It aims to evaluate the comparative advantages of various algorithms in addressing the challenges of real-time traffic prediction in increasingly complex transportation networks.

The running security and stability of massive open online course's teaching network directly affect the implementation process of massive open online course's teaching tasks. In order to provide valuable reference data for the management and maintenance of the network, this paper puts forward an analysis method of abnormal traffic of higher vocational massive open online course's teaching network based on deep convolution neural network. According to the structure of the teaching network of massive open online course in higher vocational colleges, a network model is built. Under this model, the flow data is collected, and the preprocessing of the initial flow data is completed through data cleaning, standardized conversion and clustering. The deep convolution neural network is established, and the characteristics of network traffic data are extracted through back propagation iteration. After discrete detection and feature matching, the abnormal traffic in the teaching network of higher vocational massive open online course is detected, and the visual analysis results are obtained. Compared with the traditional network abnormal traffic analysis method, it is found that the detection error and missed detection rate of the optimized design method are reduced by 3.75MB and 1.02% respectively, the accuracy of traffic abnormal type analysis is increased by 1%, and the analysis speed is obviously improved.KeywordsDeep Convolution Neural NetworkTeaching in Massive Open Online CourseTeaching NetworkAbnormal Flow Analysis

In video surveillance, low resolution in face recognition is a major problem. Various Super Resolution (SR) approaches are introduced to perform the high resolution of face video recognition from low resolution videos. However, enhancing the resolution of face videos and reconstructing the high frequency data is a major problem in research area. Therefore, an effective face video Super Resolution method-based on Deep Convolutional Neural Network (Deep CNN) is introduced in this paper to achieve the face resolution effectively. Initially, the input video collected from the database is passed into the frame extraction stage, where the video frames are extracted and the face detection is carried out using the Viola Jones algorithm. Moreover, the detected image frame is processed by the Deep Convolutional Neural Network (Deep CNN) to enhance the image resolution. Deep CNN is highly effective in performing super resolution in face videos. However, the proposed Deep Convolutional Neural Network attains better performance using the metrics, like Second Derivative like Measure of Enhancement (SDME) as 0.9743 using video-1, and Feature SIMilarity index (FSIM) as 53.843 for video-4, respectively.

Traffic volume data is basic information that is used in a wide variety of fields. Existing missing traffic volume data imputation method did not take the effect on the rainfall. This research analyzed considering of the rainfall effect in missing traffic volume data imputation method. In order to consider the effect of rainfall, established the following assumption. When missing of traffic volume data generated in rainy days it would be more accurate to use only the traffic volume data of the past rainy days. To confirm this assumption, compared for accuracy of imputed results at three kinds of imputation method(Unconditional Mean, Auto Regression, Expectation-Maximization Algorithm). The analysis results, the case on consideration of the rainfall effect was more low error occurred.

An extensive analysis of the interaction between missing data types, imputation methods, and supervised classifiers

A novel way for classifying cervical cells using Convolutional Neural Networks (CNN) is presented in this work. Features are learned automatically in this technique. Classification accuracy and model diversity can be enhanced using ensemble of CNN. This is a multi-step process and it requires the training of various CNN models and they must selected accordingly for ensemble. So, for resolving these issues and for creating expert medical decision making systems, Convolutional Neural Network and Random forest with Boosting called CNNRFB is used in current research work. For cervical cancer Prediction, an optimal prediction model can be constructed by medical practitioners using this solution. One dataset with all relevant information about various cervical cancer is created initially. In an image, without information loss, image pre-processing is done using super resolution technique. Self-similarity based Non-Local-Mean (NLM) metric is used as a base for this Super-Resolution (SR) algorithm. The NLM weight is used as a self-similarity metric under noisy environment for computing best self-example accurately. Deep ensemble learning methods are used for performing image classification and cancer prediction. System's accuracy are decided by dataset values and deep learning training technique. Python code is used for executing and simulating all the techniques. The experimental results shows the accuracy, recall, f-measure and precision produced by proposed technique shows better results when compared with the traditional approches.

In contrast to sophisticated means of visual su-per resolution (SR), not much work has been done in the tactile SR field. Existing tactile SR algorithms for taxel-based sensors mainly focus on enhancing the localization accuracy, and generally associate with a specific type of hardware, sometimes not applicable to generic taxel-based tactile sensors. Inspired by image SR, we investigate the tactile pattern SR in this paper, and present how to transform successful image SR schemes, e.g. Convolutional Neural Network (CNN) and Generative Adversarial Network (GAN) to serve the tactile SR. We propose two tactile SR models, i.e. TactileSRCNN and TactileSRGAN, and establish a new tactile pattern SR dataset for model learning. The ground truth of high resolution (HR) tactile patterns in the dataset is obtained via multi-sampling (i.e. overlapping reception) and registration of low resolution (LR) sensor. One key contribution of this research lies in achieving ×100 (from 3×4×4 to 40×40) times tactile pattern SR with a one-time tapping of 3-axis taxel-based sensor. Different from existing tactile SR algorithms which improves the localization accuracy of a single contact point, the proposed scheme can provide multi-point contact detection to robotic applications.