Author : V.venkataraman, mathvvr@maths.sastra.edu
Keyword : Global positioning system, machine learning, classification, congestion, in-vehicle sensors, trajectories
Subject : Environmental
Article Type : Original article (research)
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Abstract : Road traffic congestion and accidents continue to be significant challenges for transportation systems around the world. Traditional methods for managing and preventing these issues are often reactive and costly. Machine learning methods have recently emerged as a promising approach for predicting and mitigating traffic congestion and accidents. This paper provides an overview of machine learning techniques that have been applied to these problems, including classification, regression, clustering, and deep learning. We discuss the data sources and preprocessing steps required for effective machine learning, as well as the challenges of interpreting and deploying these models in real-world settings. Finally, we present several case studies that illustrate the potential of machine learning in improving road safety and reducing traffic congestion. This type of analysis can provide insight into the underlying dynamics of congestion, such as how traffic jams form and dissipate and how different driving behaviors affect traffic flow. Recent advancements in data collection technologies, such as GPS and in-vehicle sensors, have enabled researchers to gather large-scale data on road traffic trajectories. These data sources provide a wealth of information for analyzing traffic patterns and identifying factors that contribute to congestion.
Article by : Sekarkr
Article add date : 2023-04-19
How to cite : V.venkataraman, mathvvr@maths.sastra.edu. (2023-April-19). A novel influence of traffic density on road safety, a study of accidents and fatality rates in urban regions helps anticipate future traffic patterns and trends. retrieved from https://www.openacessjournal.com/abstract/1250