Traffic management in smart cities using support vector machine for predicting the accuracy during peak traffic conditions. (2022)

• Record Type: Journal Article
• Authors: Devi, T.; Alice, K.; Deepa, N.
• Is Part Of: Materials today. Volume 62:Part 7(2022)
• Page Start: 4980
• Page End: 4984
• Publication Date: 2022
• Subjects: Big data -- Mobility -- Linear and logistic regression -- Support vector machine (SVM) -- Traffic management; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2022.03.722
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: Mobility is the main key for smart living, where navigation and automatic suggestions are also a strategy for a successful life in smart cities. Big Data analytics are behind urban changes in the mobility of smart cities to bring sustainable life. By the year 2025 all over Indian states can reach the expected lifestyle by providing high security and mobility which can grow the opportunities also high. As the population is rapidly increasing, the needs of people are also increasing such that necessitating real-time apps for daily needs, communication devices, and so on. We focus our idea on the benefits of traffic and safety measures which are becoming a huge challenge nowadays. Many are preferred with sophistication when traveling for short distances. In such a way the big data analytics tools R studio and weka are used on the dataset smart city from the Kaggle website for traffic patterns during the high traffic duration. Using the dataset, the data are classified using a Support vector machine (SVM) and applied with regression such as linear, logistic regression to find the accuracy of traffic peak situations. The proposed work aims to compare the efficiency of big data technologies which can be applied using various classification and regression that can be shown on various tools such as R, Weka, map-reduce which can produce accurate results to visualize the smart cities and their traffic analysis.