Graph neural networks (GNNs) have been extensively used in a wide variety of domains in recent years. Owing to their power in analyzing graph-structured data, they have become broadly popular in intelligent transportation systems (ITS) applications as well. Despite their widespread applications in different transportation domains, there is no comprehensive review of recent advancements and future research directions that covers all transportation areas. Accordingly, in this survey, for the first time, we provide an overview of GNN studies in the general domain of ITS. Unlike previous surveys, which have been limited to traffic forecasting problems, we explore how GNN frameworks have evolved for different ITS applications, including traffic forecasting, demand prediction, autonomous vehicles, intersection management, parking management, urban planning, and transportation safety. Also, we micro-categorize the studies based on their transportation application to identify domain-specific research directions, opportunities, and challenges, which have been missing in previous surveys. Moreover, we identify unique and undiscussed research opportunities and directions, which is the result of reviewing a wide range of transportation applications. The neglected role of edge and graph learning in ITS applications, developing multi-modal models, and exploiting the power of unsupervised and reinforcement learning methods for developing more powerful GNNs are some examples of such new discussions in this survey. Finally, we have identified popular baseline models and datasets in each transportation domain, which facilitate the development and evaluation of future GNN-based frameworks.

Since electricity plays a crucial role in countries' industrial infrastructures, power companies are trying to monitor and control infrastructures to improve energy management and scheduling. Accurate forecasting is a critical task for a stable and efficient energy supply, where load and supply are matched. This article discusses various algorithms and a new hybrid deep learning model which combines long short-term memory networks (LSTM) and convolutional neural network (CNN) model to analyze their performance for short-term load forecasting. The proposed model is called parallel LSTM-CNN Network or PLCNet. Two real-world data sets, namely “hourly load consumption of Malaysia ” as well as “daily power electric consumption of Germany”, are used to test and compare the presented models. To evaluate the tested models' performance, root mean squared error (RMSE), mean absolute percentage error (MAPE), and R-squared were used. In total, this article is divided into two parts. In the first part, different machine learning models, including the PLCNet, predict the next time step load. In the second part, the model's performance, which has shown the most accurate results in the first part, is discussed in different time horizons. The results show that deep neural networks models, especially PLCNet, are good candidates for being used as short-term prediction tools. PLCNet improved the accuracy from 83.17% to 91.18% for the German data and achieved 98.23% accuracy in Malaysian data, which is an excellent result in load forecasting.

We consider the problem of determining the structure of high-dimensional data without prior knowledge of the number of clusters. Data are represented by a finite mixture model based on the generalized Dirichlet distribution. The generalized Dirichlet distribution has a more general covariance structure than the Dirichlet distribution and offers high flexibility and ease of use for the approximation of both symmetric and asymmetric distributions. This makes the generalized Dirichlet distribution more practical and useful. An important problem in mixture modeling is the determination of the number of clusters. Indeed, a mixture with too many or too few components may not be appropriate to approximate the true model. Here, we consider the application of the minimum message length (MML) principle to determine the number of clusters. The MML is derived so as to choose the number of clusters in the mixture model that best describes the data. A comparison with other selection criteria is performed. The validation involves synthetic data, real data clustering, and two interesting real applications: classification of Web pages, and texture database summarization for efficient retrieval.

Anomaly-based intrusion detection systems (IDSs) have been deployed to monitor network activity and to protect systems and the Internet of Things (IoT) devices from attacks (or intrusions). The problem with these systems is that they generate a huge amount of inappropriate false alarms whenever abnormal activities are detected and they are not too flexible for a complex environment. The high-level rate of the generated false alarms reduces the performance of IDS against cyber-attacks and makes the tasks of the security analyst particularly difficult and the management of intrusion detection process computationally expensive. We study here one of the challenging aspects of computer and network security and we propose to build a detection model for both known and unknown intrusions (or anomaly detection) via a novel nonparametric Bayesian model. The design of our framework can be extended easily to be adequate for IoT technology and notably for intelligent smart city web-based applications. In our method, we learn the patterns of the activities (both normal and anomalous) through a Bayesian-based MCMC inference for infinite bounded generalized Gaussian mixture models. Contrary to classic clustering methods, our approach does not need to specify the number of clusters, takes into consideration the uncertainty via the introduction of prior knowledge for the parameters of the model, and permits to solve problems related to over- and under-fitting. In order to get better clustering performance, feature weights, model’s parameters, and the number of clusters are estimated simultaneously and automatically. The developed approach was evaluated using popular data sets. The obtained results demonstrate the efficiency of our approach in detecting various attacks.