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A Novel Approach to Avoid Road Traffic Accidents and Develop Safety Rules for Traffic Using Crash Prediction Model Technique

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Micro-Electronics and Telecommunication Engineering

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 617))

  • The original version of this chapter has been revised: The city name of the fourth author was incorrectly published with Muscat instead of Dhi Qar. The correction to this chapter is available at https://doi.org/10.1007/978-981-19-9512-5_62

Abstract

The expansion of nations and communities has resulted in a variety of externalities, such as an increase in traffic accidents. Many attempts have been undertaken to minimize the injuries and fatalities and their intensity. Traffic safety modeling is a most significant technique to motivate harmless mobility because it is capable of the creation of Crash Prediction Models (CPMs) as well as the investigation of the fundamentals that contribute to the incidence of crashes. Statistical modeling has been utilized in this process in the past, regardless of the fact that they are aware of the limits of this sort of strategy which allows you to experiment with other options, such as using machine learning approaches. Machine learning approaches applied to collision datasets can assist researchers in better knowing the features of motorist behavior, highway surroundings, and meteorological circumstances that are linked to varying mortality risk levels. If we build a reliable predictive model capable of automatically classifying the degree of injury in diverse traffic accidents, we may be able to discover patterns involved in severe wrecks. These patterns of behavior and road accidents can be used to design traffic safety rules.

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Change history

  • 31 August 2023

    A correction has been published.

References

  1. Sohn, S. Y., & Shin, H. (2001). Pattern recognition for road traffic accident severity in Korea. Ergonomics, 44(1), 107–117.

    Article  Google Scholar 

  2. Global Status Report on Road Safety (2015). http://www.who.int/violence_injury_prevention/road_safety_status/2015/en/

  3. Abdel-Aty, M., & Abdelwahab, H. (2004). Analysis and prediction of traffic fatalities resulting from angle collisions including the effect of vehicles’ configuration and compatibility. Accident Analysis and Prevention, 36(3), 457–469.

    Article  Google Scholar 

  4. Chang, L. Y., & Wang, H. W. (2006). Analysis of traffic injury severity: An application of non-parametric classification tree techniques. Accident Analysis & Prevention, 38(5), 1019–1027.

    Article  Google Scholar 

  5. Select How To Explain Gradient Boosting. https://explained.ai/gradientboosting/index.html

  6. Mohamed, E. A. (2014). Predicting causes of traffic road accidents using multi-class support vector machines. Journal of Communication and Computer, 11(5), 441–447.

    Google Scholar 

  7. Ossenbruggen, P. J., Pendharkar, J., & Ivan, J. (2001). Roadway safety in rural and small urbanized areas. Accident Analysis and Prevention, 33(4), 485–498.

    Article  Google Scholar 

  8. Alsolami, B., Mehmood, R., & Albeshri, A. (2020). Hybrid statistical and machine learning methods for road traffic prediction: A review and tutorial. Smart Infrastructure and Applications, 115–133.

    Google Scholar 

  9. Teju, V., Sowmya, K. V., Yuvanika, C., Saikumar, K., & Krishna, T. B. D. S. (2021, December). Detection of Diabetes Melittus, Kidney Disease with ML. In 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) (pp. 217–222). IEEE.

    Google Scholar 

  10. Mannepalli, K., Raju, K. B., Sirisha, J., Saikumar, K., & Reddy, K. S. (2021, December). LOW complex OFDM channel design using underwater-acoustic-communication using machine learning techniques. In 2021 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA) (pp. 1505–1513). IEEE.

    Google Scholar 

  11. Kumar, K. S., Vatambeti, R., Narender, M., & Saikumar, K. (2021, December). A real time fog computing applications their privacy issues and solutions. In 2021 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA) (pp. 740–747). IEEE.

    Google Scholar 

  12. Ajay, T., Reddy, K. N., Reddy, D. A., Kumar, P. S., & Saikumar, K. (2021, December). Analysis on SAR signal processing for high-performance flexible system design using signal processing. In 2021 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA) (pp. 30–34). IEEE.

    Google Scholar 

  13. Raju, K. B., Lakineni, P. K., Indrani, K. S., Latha, G. M. S., & Saikumar, K. (2021, October). Optimized building of machine learning technique for thyroid monitoring and analysis. In 2021 2nd International Conference on Smart Electronics and Communication (ICOSEC) (pp. 1–6). IEEE.

    Google Scholar 

  14. Kailasam, S., Achanta, S. D. M., Rao, P. R. K., Vatambeti, R., & Kayam, S. (2021). An IoT-based agriculture maintenance using pervasive computing with machine learning technique. International Journal of Intelligent Computing and Cybernetics.

    Google Scholar 

  15. Koppula, N., Sarada, K., Patel, I., Aamani, R., & Saikumar, K. (2021). Identification and recognition of speaker voice using a neural network-based algorithm: Deep learning. In Handbook of Research on Innovations and Applications of AI, IoT, and Cognitive Technologies (pp. 278–289). IGI Global.

    Google Scholar 

  16. Rao, K. S., Reddy, B. V., Sarada, K., & Saikumar, K. (2021). A sequential data mining technique for identification of fault zone using FACTS-based transmission. In Handbook of Research on Innovations and Applications of AI, IoT, and Cognitive Technologies (pp. 408–419). IGI Global.

    Google Scholar 

  17. Raju, K., Pilli, S. K., Kumar, G. S. S., Saikumar, K., & Jagan, B. O. L. (2019). Implementation of natural random forest machine learning methods on multi spectral image compression. Journal of Critical Reviews, 6(5), 265–273.

    Google Scholar 

  18. Garigipati, R. K., Raghu, K., & Saikumar, K. (2022). Detection and identification of employee attrition using a machine learning algorithm. In Handbook of Research on Technologies and Systems for E-Collaboration During Global Crises (pp. 120–131). IGI Global.

    Google Scholar 

  19. Mythreya, S., Murthy, A. S. D., Saikumar, K., & Rajesh, V. (2022). Prediction and prevention of malicious URL using ML and LR techniques for network security: Machine learning. In Handbook of Research on Technologies and Systems for E-Collaboration During Global Crises (pp. 302–315). IGI Global.

    Google Scholar 

  20. Saikumar, K., Rajesh, V., & Babu, B. S. (2022). Heart disease detection based on feature fusion technique with augmented classification using deep learning technology. Traitement du Signal, 39(1), 31–42. https://doi.org/10.18280/ts.390104

  21. Appalaraju, V., Rajesh, V., Saikumar, K., Sabitha, P., & Kiran, K. R. (2021, December). Design and development of intelligent voice personal assistant using python. In 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) (pp. 1650–1654). IEEE.

    Google Scholar 

  22. Jothsna, V., Patel, I., Raghu, K., Jahnavi, P., Reddy, K. N., & Saikumar, K. (2021, March). A fuzzy expert system for the drowsiness detection from blink characteristics. In 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS) (Vol. 1, pp. 1976–1981). IEEE.

    Google Scholar 

  23. Saikumar, K., Rajesh, V., & Babu, B. S. (2022). Heart disease detection based on feature fusion technique with augmented classification using deep learning technology. Traitement du Signal, 39(1).

    Google Scholar 

  24. Anandkumar, R., Dinesh, K., Obaid, A. J., Malik, P., Sharma, R., Dumka, A., Singh, R., & Khatak, S. (2022). Securing e-Health application of cloud computing using hyperchaotic image encryption framework. Computers & Electrical Engineering, 100, 107860. ISSN 0045-7906. https://doi.org/10.1016/j.compeleceng.2022.107860

  25. https://scikitlearn.org/stable/modules/generated/sklearn.feature_selection.chi2.html#sklearn.feature_selection.chi2

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Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Guntur, India

    Sk Hasane Ahammad

  2. Department of Computer Science and Engineering, Vaagdevi College of Engineering, Warangal, Telangana, India

    M. Sukesh

  3. Department of Computer Science and Engineering, Jayamukhi Institute of Technological Sciences, Narsampet, Warangal, India

    Mekala Narender

  4. National University of Science and Technology, Dhi Qar, Iraq

    Sajjad Ali Ettyem

  5. Ashur University College, Baghdad, Iraq

    Kadhum Al-Majdi

  6. School of Engineering, Department of AI-ML, Malla Reddy University, Hyderabad, Telangana, 500043, India

    K. Saikumar

Authors
  1. Sk Hasane Ahammad
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  2. M. Sukesh
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  3. Mekala Narender
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  4. Sajjad Ali Ettyem
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  5. Kadhum Al-Majdi
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  6. K. Saikumar
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Corresponding author

Correspondence to M. Sukesh .

Editor information

Editors and Affiliations

  1. SRM Institute of Science and Technology, Ghaziabad, India

    Devendra Kumar Sharma

  2. National Taipei University of Business, Hualien, Taiwan

    Sheng-Lung Peng

  3. SRM Institute of Science and Technology, Ghaziabad, Uttar Pradesh, India

    Rohit Sharma

  4. Department of Embedded Systems Engineering, College of Information Technology, Incheon National University, Incheon, Korea (Republic of)

    Gwanggil Jeon

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Ahammad, S.H., Sukesh, M., Narender, M., Ettyem, S.A., Al-Majdi, K., Saikumar, K. (2023). A Novel Approach to Avoid Road Traffic Accidents and Develop Safety Rules for Traffic Using Crash Prediction Model Technique. In: Sharma, D.K., Peng, SL., Sharma, R., Jeon, G. (eds) Micro-Electronics and Telecommunication Engineering . Lecture Notes in Networks and Systems, vol 617. Springer, Singapore. https://doi.org/10.1007/978-981-19-9512-5_34

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  • DOI: https://doi.org/10.1007/978-981-19-9512-5_34

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-9511-8

  • Online ISBN: 978-981-19-9512-5

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