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Smart city article details

Title	Traffic Jam Detection Using Regression Model Analysis On Iot-Based Smart City
ID_Doc	58610
Authors	Manjaiah D.H.; Praveena Kumari M.K.; Harishkumar K.S.; Bongale V.
Year	2023
Published	Lecture Notes in Networks and Systems, 653 LNNS
DOI	http://dx.doi.org/10.1007/978-981-99-0981-0_41
Abstract	A well-mannered transportation system has an impact on the economy, well-being, and personal satisfaction of a country. The rate at which vehicle numbers increase is substantially faster than the rate at which the general population grows, resulting in increasingly congested and unsafe streets. This problem will no longer be solved by merely increasing the number of roadways. It is important to study and understand the traffic flow in thronged cities, and also it is necessary to mine traffic data and apply machine learning algorithms to it for the development of smart cities. We can cut transport delays, fuel consumption, traveler and freight movement costs, the frequency of crashes, tailpipe pollution, and improve city life by mining traffic data. Many cities in wealthy countries currently use a range of sensors to collect real-time traffic data, which are subsequently studied by using machine learning algorithms to improve traffic flow. The real-time traffic data of Aarhus city of Denmark is used in our work for exploring the traffic conditions of 449 junctions in the year 2014. Four regression models—linear, polynomial, lasso, and ridge are applied to the dataset to predict the flow of traffic in Aarhus city. The performance of these regression models is tested using statistical measures such as root mean square error and coefficient of determination. The experiment findings indicate that, for various routes in the Aarhus city of Denmark, lasso regression predictions tend to be the most accurate in predicting real traffic flow. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Author Keywords	Forecast; IoT; ITS; Machine learning; Regression; Traffic

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