Electric Vehicle Usage Pattern Analysis Using Nonnegative Matrix Factorization in Renewable EV-Smart Charging Grid Environment

The global utilization of electric vehicles (EVs) is exponentially increasing due to the increased availability of cost-efficient EVs and infrastructure managements for the EVs. In spite of the increasing usage of EVs, the problem of EV usage patterns’ analysis and implementing sustainable infrastructure for the EV transportation is still under development. In addition to this, there is a challenging problem of long waiting hours in traffic signals. This study deals with these problems by proposing an architecture that includes EV usage pattern analysis using nonnegative matrix factorization (NMF) technique and renewable solar-powered wireless smart charging grid to effectively utilize or mitigate the long traffic signal waiting hours. The insights from the EV usage patterns are analyzed and presented showing the importance of usage pattern analysis alongside to the presented architecture of renewable solar-powered wireless EV-smart charging grid. These implementations improvise the usage of the EVs and enhancing the transportation experience, which in turn leads to the development of sustainable smart transportation.</jats:p

DeepWalk with Reinforcement Learning (DWRL) for node embedding

DeepWalk is used to convert nodes in an original graph into equivalent vectors in a latent space for performing various predictive tasks. To ensure second-order structural similarity between nodes in the original graph and their vectors in the latent space, dot products are applied to each pair of nodes explored on the random walk (RW) in the latent space. However. dot products for graphs with millions of nodes and billions of edges are computationally expensive. To minimize the computation time required for calculating the second-order structural similarity, DeepWalk with reinforcement learning (DWRL) is proposed herein. In DWRL, a level pointer for each node in the original graph is prepared. By identifying common nodes between each pair of nodes in the original graph, the number of computations in the dot product in the latent space is reduced, thereby ensuring second-order structural similarity. Additionally, repeated selection of the same node during RWs produces redundant samples for training. Therefore, the subsampling technique is used to choose the next node based on its degree, which improves the generalization of node representations in the latent space and increases accuracy. The proposed techniques are applied to popular datasets to perform multilabel classification and link prediction tasks, and their efficiency in reducing the computation time is verified. The proposed DWRL minimizes the computation time 47% for large graphs to build latent vectors and improves the average micro and macro F1 scores up to 12%. The link prediction performance also increases up to 20%.</p

Comparative Analysis of Intelligent Transportation Systems for Sustainable Environment in Smart Cities

In recent works on the Internet of Vehicles (IoV), “intelligent” and “sustainable” have been the buzzwords in the context of transportation. Maintaining sustainability in IoV is always a challenge. Sustainability in IoV can be achieved not only by the use of pollution-free vehicular systems, but also by maintenance of road traffic safety or prevention of accidents or collisions. With the aim of establishing an effective sustainable transportation planning system, this study performs a short analysis of existing sustainable transportation methods in the IoV. This study also analyzes various characteristics of sustainability and the advantages and disadvantages of existing transportation systems. Toward the end, this study provides a clear suggestion for effective sustainable transportation planning aimed at the maintenance of an eco-friendly environment and road traffic safety, which, in turn, would lead to a sustainable transportation system