Abstract
To explore the electric vehicle networks in smart cities through big data analysis technology, this study utilizes K-means and fuzzy theory in big data analysis technology to construct an objective function-based fuzzy mean clustering algorithm theory (FCM). Then, the FCM algorithm is improved, and the electric vehicle network is simulated. The results show that in the analysis of network data transmission performance when the probability of successful propagation is 100% and the λ value is between 0.01-0.05, it is closest to the actual result, and the data delay is the smallest. In the analysis of the route guidance effects, when facing congested road sections, the route guidance strategy of this study can restrain the spread of congestion effectively and achieve timely evacuation of traffic congestion. In the further analysis of the impact of different factors on traffic conditions, under route guidance, with the increase in market penetration rate (MPR) of devices, following rate (FR) of vehicles, and congestion level (CL), the improvement of the induction strategy becomes clearer, and greater economic benefits are achieved. This study has found that utilizing big data analysis technology to improve the electric vehicle transportation networks can reduce the network data transmission performance delay significantly and change the path to suppress the spread of congestion effectively, which has provided experimental references for the development of electric vehicle transportation networks.
-
Author Keywords
- Electric vehicle,
- big data analysis,
- FCM algorithm,
- path guidance,
- smart cities.
-
IEEE Keywords
- Electric vehicles,
- Big Data,
- Smart cities,
- Protocols,
- Control systems,
- Roads
Introduction
wITH the rapid development of science and technology, urban transportation systems are becoming more convenient, accompanied by an increasing number of vehicles. As vehicles increase, the exhaust emissions also increase, and the non-renewable energy sources are exhausted. Therefore, electric vehicles, powered by clean energy, are becoming increasingly popular. As electronic technology develops promptly, electric vehicles are also developing in a highly integrated and intelligent direction, including multiple functions such as artificial intelligence (AI) intelligent driving and network interconnection. Electric vehicles are not only the means of transportation but the inseparable link in intelligent transportation systems [1]. However, in the development process of electric vehicles, due to the increasing number of vehicles, problems such as traffic congestion and slow network data reception cannot be ignored, which has also become the research focus in related fields.
In the development process of smart cities, with the increase in vehicles, the number of road sections, intersections, and vehicles is increasing, and road conditions are also more complicated. For such a large-scale urban transportation system, the safety guarantee management of vehicles is a very complicated task. In the traditional state of traffic control, due to the lack of or incomplete implementation data, the dynamic traffic guidance lacks sufficient data support. As the application of the Internet of Things (IoT) technology to electric vehicles, the electric vehicle Internet of Vehicles (IoV) mainly includes three aspects, i.e., sensing and control network inside electric vehicles, self-organizing networks between vehicles, and on-board mobile Internet [2], [3]. With the increasing number of vehicles, to alleviate urban traffic jams and ensure the safety of vehicle driving, the electric vehicle has become an indispensable link for developing smart cities.Doubtlessly, as electric vehicle IoV has been realized, with the rapid development of the computer and network communication technologies, the IoV has become one of the most economical and potential applications on the Internet [4]. Also, with the emergence of technologies such as big data and cloud computing, it is possible to manage numerous vehicles in the transportation system effectively [5]. In addition, compared to traditional vehicles, precise control of electric vehicles is easier to achieve, which is reflected in the development of electric vehicle transportation networks in the era of big data. In summary, with the rapid popularization of electric vehicles, the emergence of connected vehicles has become inevitable. To improve the performance of electric vehicle transportation networks and make electric vehicles better integrated into smart cities, this study utilizes big data analysis technology to analyze the prediction and transmission performances in electric vehicle transportation networks, hoping to provide experimental evidence for the development of electric vehicle transportation networks in smart cities.
Conclusion
With the continuous improvement of people’s living standards, the number of electric vehicles in cities is increasing, and the inconvenience caused by transportation is also increasing accordingly. This study utilizes big data analysis techniques to solve the problems encountered in the electric vehicle transportation network and applies the improved FCMalgorithm to improve the electric vehicle networks and simulate the network performance. This study has found that utilizing big data analysis techniques to improve the electric vehicle transportation networks can reduce the system data transmission performance delay significantly and change the path to suppress the spread of congestion effectively, which has provided experimental references for the development of electric vehicle transportation networks. However, shortcomings are found in the research process. The experiment is still in the simulation stage. Therefore, in the subsequent study, the system will be improved further to test the electric vehicle transportation network in real life, thereby providing a more reliable reference for the development of the electric vehicle transportation industry. In the future, the proposed research can be improved by the Internet of Things and6G research[28]–[37].
About KSRA
The Kavian Scientific Research Association (KSRA) is a non-profit research organization to provide research / educational services in December 2013. The members of the community had formed a virtual group on the Viber social network. The core of the Kavian Scientific Association was formed with these members as founders. These individuals, led by Professor Siavosh Kaviani, decided to launch a scientific / research association with an emphasis on education.
KSRA research association, as a non-profit research firm, is committed to providing research services in the field of knowledge. The main beneficiaries of this association are public or private knowledge-based companies, students, researchers, researchers, professors, universities, and industrial and semi-industrial centers around the world.
Our main services Based on Education for all Spectrum people in the world. We want to make an integration between researches and educations. We believe education is the main right of Human beings. So our services should be concentrated on inclusive education.
The KSRA team partners with local under-served communities around the world to improve the access to and quality of knowledge based on education, amplify and augment learning programs where they exist, and create new opportunities for e-learning where traditional education systems are lacking or non-existent.
FULL Paper PDF file:
Big Data Analysis Technology for Electric Vehicle Networks in Smart CitiesBibliography
author
Year
2020
Title
Big Data Analysis Technology for Electric Vehicle Networks in Smart Cities
Publish in
in IEEE Transactions on Intelligent Transportation Systems,
Doi
10.1109/TITS.2020.3008884
PDF reference and original file: Click here
Somayeh Nosrati was born in 1982 in Tehran. She holds a Master's degree in artificial intelligence from Khatam University of Tehran.
Professor Siavosh Kaviani was born in 1961 in Tehran. He had a professorship. He holds a Ph.D. in Software Engineering from the QL University of Software Development Methodology and an honorary Ph.D. from the University of Chelsea.
Nasim Gazerani was born in 1983 in Arak. She holds a Master's degree in Software Engineering from UM University of Malaysia.
I am a Data Scientist with a strong background in software engineering; and used to handling a variety of data pipelines and databases, included unstructured ones.
