Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools

Big data has been used widely in many areas including the transportation industry. Using various data sources, traffic states can be well estimated and further predicted for improving the overall operation efficiency. Combined with this trend, this study presents an up-to-date survey of open data and big data tools used for traffic estimation and prediction. Different data types are categorized and the off-the-shelf tools are introduced. To further promote the use of big data for traffic estimation and prediction tasks, challenges and future directions are given for future studies

Performance of video processing at the edge for crowd-monitoring applications

Video analytics has a key role to play in smart cities and connected community applications such as crowd counting, activity detection, event classification, traffic counting etc. Using a cloud-centric approach where data is funneled to a central processor presents a number of key problems such as available bandwidth, real-time responsiveness and personal data privacy issues. With the development of edge computing, a new paradigm for smart data management is emerging. Raw video feeds can be pre-processed at the point of capture while integration and deeper analytics is performed in the cloud. In this paper we explore the capacity of video processing at the edge and shown that basic image processing can be achieved in near real-time on low-powered gateway devices. We have also investigated deep learning model capabilities for crowd counting in this context showing that its performance is highly dependent on the input size and re-scaling video frames can optimise processing and performance. Increased edge processing resolves a number of issues in video analytics for crowd monitoring applications

A recurrent neural network for urban long-term traffic flow forecasting

This paper investigates the use of recurrent neural network to predict urban long-term traffic flows. A representation of the long-term flows with related weather and contextual information is first introduced. A recurrent neural network approach, named RNN-LF, is then proposed to predict the long-term of flows from multiple data sources. Moreover, a parallel implementation on GPU of the proposed solution is developed (GRNN-LF), which allows to boost the performance of RNN-LF. Several experiments have been carried out on real traffic flow including a small city (Odense, Denmark) and a very big city (Beijing). The results reveal that the sequential version (RNN-LF) is capable of dealing effectively with traffic of small cities. They also confirm the scalability of GRNN-LF compared to the most competitive GPU-based software tools when dealing with big traffic flow such as Beijing urban data

6G White Paper on Machine Learning in Wireless Communication Networks

The focus of this white paper is on machine learning (ML) in wireless communications. 6G wireless communication networks will be the backbone of the digital transformation of societies by providing ubiquitous, reliable, and near-instant wireless connectivity for humans and machines. Recent advances in ML research has led enable a wide range of novel technologies such as self-driving vehicles and voice assistants. Such innovation is possible as a result of the availability of advanced ML models, large datasets, and high computational power. On the other hand, the ever-increasing demand for connectivity will require a lot of innovation in 6G wireless networks, and ML tools will play a major role in solving problems in the wireless domain. In this paper, we provide an overview of the vision of how ML will impact the wireless communication systems. We first give an overview of the ML methods that have the highest potential to be used in wireless networks. Then, we discuss the problems that can be solved by using ML in various layers of the network such as the physical layer, medium access layer, and application layer. Zero-touch optimization of wireless networks using ML is another interesting aspect that is discussed in this paper. Finally, at the end of each section, important research questions that the section aims to answer are presented

Designing the next generation intelligent transportation sensor system using big data driven machine learning techniques

Accurate traffic data collection is essential for supporting advanced traffic management system operations. This study investigated a large-scale data-driven sequential traffic sensor health monitoring (TSHM) module that can be used to monitor sensor health conditions over large traffic networks. Our proposed module consists of three sequential steps for detecting different types of abnormal sensor issues. The first step detects sensors with abnormally high missing data rates, while the second step uses clustering anomaly detection to detect sensors reporting abnormal records. The final step introduces a novel Bayesian changepoint modeling technique to detect sensors reporting abnormal traffic data fluctuations by assuming a constant vehicle length distribution based on average effective vehicle length (AEVL). Our proposed method is then compared with two benchmark algorithms to show its efficacy. Results obtained by applying our method to the statewide traffic sensor data of Iowa show it can successfully detect different classes of sensor issues. This demonstrates that sequential TSHM modules can help transportation agencies determine traffic sensors’ exact problems, thereby enabling them to take the required corrective steps. The second research objective will focus on the traffic data imputation after we discard the anomaly/missing data collected from failure traffic sensors. Sufficient high-quality traffic data are a crucial component of various Intelligent Transportation System (ITS) applications and research related to congestion prediction, speed prediction, incident detection, and other traffic operation tasks. Nonetheless, missing traffic data are a common issue in sensor data which is inevitable due to several reasons, such as malfunctioning, poor maintenance or calibration, and intermittent communications. Such missing data issues often make data analysis and decision-making complicated and challenging. In this study, we have developed a generative adversarial network (GAN) based traffic sensor data imputation framework (TSDIGAN) to efficiently reconstruct the missing data by generating realistic synthetic data. In recent years, GANs have shown impressive success in image data generation. However, generating traffic data by taking advantage of GAN based modeling is a challenging task, since traffic data have strong time dependency. To address this problem, we propose a novel time-dependent encoding method called the Gramian Angular Summation Field (GASF) that converts the problem of traffic time-series data generation into that of image generation. We have evaluated and tested our proposed model using the benchmark dataset provided by Caltrans Performance Management Systems (PeMS). This study shows that the proposed model can significantly improve the traffic data imputation accuracy in terms of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) compared to state-of-the-art models on the benchmark dataset. Further, the model achieves reasonably high accuracy in imputation tasks even under a very high missing data rate (\u3e50%), which shows the robustness and efficiency of the proposed model. Besides the loop and radar sensors, traffic cameras have shown great ability to provide insightful traffic information using the image and video processing techniques. Therefore, the third and final part of this work aimed to introduce an end to end real-time cloud-enabled traffic video analysis (IVA) framework to support the development of the future smart city. As Artificial intelligence (AI) growing rapidly, Computer vision (CV) techniques are expected to significantly improve the development of intelligent transportation systems (ITS), which are anticipated to be a key component of future Smart City (SC) frameworks. Powered by computer vision techniques, the converting of existing traffic cameras into connected ``smart sensors called intelligent video analysis (IVA) systems has shown the great capability of producing insightful data to support ITS applications. However, developing such IVA systems for large-scale, real-time application deserves further study, as the current research efforts are focused more on model effectiveness instead of model efficiency. Therefore, we have introduced a real-time, large-scale, cloud-enabled traffic video analysis framework using NVIDIA DeepStream, which is a streaming analysis toolkit for AI-based video and image analysis. In this study, we have evaluated the technical and economic feasibility of our proposed framework to help traffic agency to build IVA systems more efficiently. Our study shows that the daily operating cost for our proposed framework on Google Cloud Platform (GCP) is less than $0.14 per camera, and that, compared with manual inspections, our framework achieves an average vehicle-counting accuracy of 83.7% on sunny days

Sehaa: A big data analytics tool for healthcare symptoms and diseases detection using Twitter, Apache Spark, and Machine Learning

Smartness, which underpins smart cities and societies, is defined by our ability to engage with our environments, analyze them, and make decisions, all in a timely manner. Healthcare is the prime candidate needing the transformative capability of this smartness. Social media could enable a ubiquitous and continuous engagement between healthcare stakeholders, leading to better public health. Current works are limited in their scope, functionality, and scalability. This paper proposes Sehaa, a big data analytics tool for healthcare in the Kingdom of Saudi Arabia (KSA) using Twitter data in Arabic. Sehaa uses Naive Bayes, Logistic Regression, and multiple feature extraction methods to detect various diseases in the KSA. Sehaa found that the top five diseases in Saudi Arabia in terms of the actual aicted cases are dermal diseases, heart diseases, hypertension, cancer, and diabetes. Riyadh and Jeddah need to do more in creating awareness about the top diseases. Taif is the healthiest city in the KSA in terms of the detected diseases and awareness activities. Sehaa is developed over Apache Spark allowing true scalability. The dataset used comprises 18.9 million tweets collected from November 2018 to September 2019. The results are evaluated using well-known numerical criteria (Accuracy and F1-Score) and are validated against externally available statistics

A Novel Low Processing Time System for Criminal Activities Detection Applied to Command and Control Citizen Security Centers

[EN] This paper shows a Novel Low Processing Time System focused on criminal activities detection based on real-time video analysis applied to Command and Control Citizen Security Centers. This system was applied to the detection and classification of criminal events in a real-time video surveillance subsystem in the Command and Control Citizen Security Center of the Colombian National Police. It was developed using a novel application of Deep Learning, specifically a Faster Region-Based Convolutional Network (R-CNN) for the detection of criminal activities treated as "objects" to be detected in real-time video. In order to maximize the system efficiency and reduce the processing time of each video frame, the pretrained CNN (Convolutional Neural Network) model AlexNet was used and the fine training was carried out with a dataset built for this project, formed by objects commonly used in criminal activities such as short firearms and bladed weapons. In addition, the system was trained for street theft detection. The system can generate alarms when detecting street theft, short firearms and bladed weapons, improving situational awareness and facilitating strategic decision making in the Command and Control Citizen Security Center of the Colombian National Police.This work was co-funded by the European Commission as part of H2020 call SEC-12-FCT-2016-Subtopic3 under the project VICTORIA (No. 740754). This publication reflects the views only of the authors and the Commission cannot be held responsible for any use which may be made of the information contained therein.Suarez-Paez, J.; Salcedo-Gonzalez, M.; Climente, A.; Esteve Domingo, M.; Gomez, J.; Palau Salvador, CE.; Pérez Llopis, I. (2019). A Novel Low Processing Time System for Criminal Activities Detection Applied to Command and Control Citizen Security Centers. Information. 10(12):1-19. https://doi.org/10.3390/info10120365S1191012Wang, L., Rodriguez, R. M., & Wang, Y.-M. 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Exploring the potential of offline cryptography techniques for securing ECG signals in healthcare

In the research, a software for ECG signal based on Chaos encryption based on C#-programmed and Kit of Microsoft Visual Studio Development was implemented. A chaos logic map (ChLMp ) and its initial value are utilized to create Level-1 ECG signal based on Chaos encryption bit streams. A ChLMp, an initial value, a ChLMp bifurcation parameter, and two encryption level parameters are utilized to create level-2 ECG signal based on Chaos encryption bit streams. The level-3 ECG signal based on Chaos encryption software utilizes two parameters for the level of encryption, a permutation mechanism, an initial value, a bifurcation parameter of the level of encryption, and a ChLMp. We assess 16-channel ECG signals with great resolution utilizing encryption software. The level-3 ECG signal based on Chaos encryption program has the slowest and most reliable encryption speed. The encryption effect is superior, according to test findings, and when the right decoding parameter is utilized, the ECG signals may be completely recovered. The high resolution 16-channel ECG signals (HRMCECG) won't be recovered if an invalid input parameter occurred, such as a 0.00001% initial point error, which will result in chaotic encryption bit streams