Search results

The objective of this research work is to design a data-based solution for administering traffic organization in a smart city by using the machine learning algorithm.

A machine learning framework for managing traffic infrastructure and air pollution in urban centers relies on a predictive analytics model. The model makes use of transportation data to predict traffic patterns based on the information gathered from numerous sources within the city. It can be promoted for strategic planning determination. The data features volume and calendar variables, including hours of the day, week and month. These variables are leveraged to identify time series-based seasonal patterns in the data. To achieve accurate traffic volume forecasting, the long short-term memory (LSTM) method is recommended.

The study has produced a model that is appropriate for the transportation sector in the city and other innovative urban applications. The findings indicate that the implementation of smart transportation systems enhances transportation and has a positive impact on air quality. The study's results are explored and connected to practical applications in the areas of air pollution control and smart transportation.

The present paper has created the machine learning framework for the transportation sector of smart cities that achieves a reasonable level of accuracy. Additionally, the paper examines the effects of smart transportation on both the environment and supply chain.

The purpose of this paper is to provide a systematic literature review on the technological aspects of smart cities and to give insights about current trends, sources of research, contributing authors and countries. It is required to understand technical concepts like information technology, big data analytics, Internet of Things and blockchain needed to implement smart city models successfully.

The data were collected from the Scopus database, and analysis techniques like bibliometric analysis, network analysis and content analysis were used to obtain research trends, publications growth, top contributing authors and nations in the domain of smart cities. Also, these analytical techniques identified various fields within the literature on smart cities and supported to design a conceptual framework for Industry 4.0 adoption in a smart city.

The bibliometric analysis shows that research publications have increased significantly over the last couple of years. It has found that developing countries like China is leading the research on smart cities. The network analytics and article classification identified six domains within the literature on smart cities. A conceptual framework for the smart city has proposed for the successful implementation of Industry 4.0 technologies.

This paper explores the role of Industry 4.0 technologies in smart cities. The bibliometric data on publications from the year 2013 to 2018 were used and investigated by using advanced analytical techniques. The paper reviewS key technical concepts for the successful execution of a smart city model. It also gives an idea about various technical considerations required for the implementation of the smart city model through a conceptual framework.