Fruitful Synergy Model of Artificial Intelligence and Internet of Thing for Smart Transportation System

Abstract

In urban transportation systems, bicycle sharing systems are majorly deployed in major cities of both developed and developing countries. The recent boom of bicycle sharing system along with its upgraded technology have opened new opportunities towards urban transportation system. With the enlargement of intelligent transportation systems (ITS's), smart bicycle sharing schemes are more popular to smart cities as a green transportation mode. In this article, the Internet of Things (IoT) and artificial intelligence-based monitoring devices have been proposed for the bicycles. This system contains a harmful exhaust gas sensor, wireless module, and a GPS receiver and camera that are capable to send data with time and date stamping. In addition, sensor also integrated on the bicycle for the fall detection. An artificial neural network (ANN) and support vector machine (SVM) applied to the data collected at central server is designed to analyze the root mean square error (RMSE), and coefficient of correlation (R2). Result shows that ANN performance is better when compared to SVM.

Artificial intelligence of things for smart cities: advanced solutions for enhancing transportation safety

“…The integration of AI with IoT has given rise to a transformative paradigm known as AIoT. This convergence finds numerous applications and profound implications, especially in smart city domains, including transportation and its safety (Alahi et al, 2023;Bibri & Jagatheesaperumal, 2023;Bibri et al, 2023a;Puri et al, 2020;Zhang & Tao, 2020). The conceptual framework presented here aims to elucidate the technical components of integrating AIoT within the realm of smart cities, with a specific focus on enhancing smart transportation systems, including safety considerations.…”

A Computational Comparison of Three Nature-Inspired, Population-Based Metaheuristic Algorithms for Modelling-to-Generate Alternatives

“…While "optimal" solutions can normally be calculated for the mathematical formulations, these answers may not produce the best outcomes in the original real system (Acharjya & Anitha, 2017;Brugnach et al, 2007;Fahad et al, 2017;Janssen et al, 2010;Loughlin et al, 2001). To improve decision-making under such circumstances, it is often preferable to create a limited number of dissimilar options that contribute very different perspectives (Matthies et al, 2007;Puri et al, 2020;Yeomans & Gunalay, 2011). Preferably these alternatives should all possess good (i.e.…”