AI Enabled Accident Black Spot Alerting Mobile System to Enhance Road Safety Using GMM-SVM

Authors

  • M. Sobhana Department of Computer Science and Engineering, V.R Siddhartha Engineering College, Kanuru -520007, INDIA
  • V. Krishna Rohith Department of Computer Science and Engineering, V.R Siddhartha Engineering College, Kanuru -520007, INDIA
  • T. Avinash Department of Computer Science and Engineering, V.R Siddhartha Engineering College, Kanuru -520007, INDIA
  • N. Malathi Department of Civil Engineering, V.R Siddhartha Engineering College, Kanuru -520007, INDIA
  • Smitha Chowdary Ch Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, A.P., Guntur, 522302, India

Keywords:

Android Mobile Application, Blackspots, Gaussian Mixture Modelling, Machine Learning, Road accidents

Abstract

Andhra Pradesh consistently ranked in the top 10 Indian states with the highest number of traffic accidents over the past ten years, according to statistics made public by the National Crime Record Bureau. Andhra Pradesh saw a 20 percent rise in road accidents in the year 2021, totaling 21,556 accidents, of which 8,186 resulted in fatalities. Future accidents can be decreased by comprehending the causes affecting road accidents and using the insights gained from them. When analyzing the causes of traffic accidents, driver’s behavior is a crucial component to take into account. Inappropriate driving behaviors can lead to abnormal circumstances that may result in traffic accidents. The proposed methodology uses an integration of Gaussian Mixture Modelling and Machine Learning classification algorithms on the data of road crashes in the Vijayawada region to predict accidents in the future and notify drivers of impending danger. The Road Transportation Authority (RTA), Vijayawada, provided data on road accidents, including three accident classifications and variables influencing accidents. Firstly, the data has been preprocessed and then the proposed methodology is applied to classify the black spots. The developed model can potentially classify accidents based on severity into three classes: fatal, severely injured, and generally injured. Then the developed models are integrated with an Android mobile application through the Google Cloud platform. The mobile application keeps a database of all the crucial user information, including the user's age, gender, vehicle type, and age, and it uses GPS to monitor the user's location. The driver inputs his source and destination addresses to check for any susceptible blackspots before beginning the drive. He is also given the option of real-time safety support, which, when activated, warns the user when he is approaching a blackspot that would have serious repercussions.

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Published

21.09.2023

How to Cite

Sobhana, M. ., Rohith, V. K. ., Avinash, T. ., Malathi, N. ., & Chowdary Ch, S. . (2023). AI Enabled Accident Black Spot Alerting Mobile System to Enhance Road Safety Using GMM-SVM. International Journal of Intelligent Systems and Applications in Engineering, 11(4), 734–742. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3608

Issue

Vol. 11 No. 4 (2023)

Section

Research Article

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