Traffic Prediction For Intelligent Transport System Using Machine Learning

Authors

  • N. Akhila  Department of Electronics and Communication Engineering, JB Institute of Engineering and Technology, Moinabad, Telangana, India
  • M. Kavya  Department of Electronics and Communication Engineering, JB Institute of Engineering and Technology, Moinabad, Telangana, India
  • M. Soumith Reddy  Department of Electronics and Communication Engineering, JB Institute of Engineering and Technology, Moinabad, Telangana, India
  • Dr. Prasanta Kumar Pradhan  Associate Professor, Department of Electronics and Communication Engineering, JB Institute of Engineering and Technology, Moinabad, Telangana, India

Keywords:

Automobile manufacturers have developed various safety features to mitigate the risk of traffic accidents, but accidents continue to occur frequently in both urban and rural areas. To prevent accidents and improve safety measures, it is necessary to develop accurate prediction models that can identify patterns associated with different scenarios. By using these models, we can cluster accident scenarios and develop effective safety measures. We aim to achieve the maximum possible reduction in accidents using low-budget resources through scientific measures.To achieve this goal, we need to collect and analyze a vast amount of data related to traffic accidents, such as accident location, time, weather conditions, and road features. Machine learning algorithms can be used to automatically identify patterns in the data and predict accident scenarios based on these patterns. These models can then be used to cluster accidents into different categories and develop safety measures tailored to each category. By using this approach, we can develop cost-effective safety measures that can be implemented in a variety of settings. We believe that this approach has the potential to significantly reduce the number of traffic accidents and improve safety for drivers, passengers, and pedestrians alike.

Abstract

Automobile manufacturers have developed various safety features to mitigate the risk of traffic accidents, but accidents continue to occur frequently in both urban and rural areas. To prevent accidents and improve safety measures, it is necessary to develop accurate prediction models that can identify patterns associated with different scenarios. By using these models, we can cluster accident scenarios and develop effective safety measures. We aim to achieve the maximum possible reduction in accidents using low-budget resources through scientific measures.To achieve this goal, we need to collect and analyze a vast amount of data related to traffic accidents, such as accident location, time, weather conditions, and road features. Machine learning algorithms can be used to automatically identify patterns in the data and predict accident scenarios based on these patterns. These models can then be used to cluster accidents into different categories and develop safety measures tailored to each category. By using this approach, we can develop cost-effective safety measures that can be implemented in a variety of settings. We believe that this approach has the potential to significantly reduce the number of traffic accidents and improve safety for drivers, passengers, and pedestrians alike.

References

  1. Joaquín Abellán, Griselda López, and Juan De OñA. 2013. Analysisoftrafficaccidentseverityusingdecisionrulesviadecisiontrees. Expert Systems with Applications 40, 15 (2013), 6047–6054.
  2. Mikhail Belkin and Partha Niyogi. 2001. Laplacian eigenmaps and spectral techniques for embedding and clustering. In NIPS, Vol. 14. 585–591.
  3. Ruth Bergel-Hayat, Mohammed Debbarh, Constantinos Antoniou, and George Yannis. 2013. Explaining the road accident risk: Weather effects. Accident Analysis & Prevention 60 (2013), 456–465.
  4. Ciro Caliendo, Maurizio Guida, and Alessandra Parisi. 2007. A crash-prediction model for multilane roads. Accident Analysis & Prevention 39, 4 (2007), 657–670.
  5. Li-Yen Chang. 2005. Analysis of freeway accident frequencies: negative binomial regression versus artificial neural network. Safety science 43, 8 (2005), 541–557.
  6. Cheng Chen, Manchun Tan. Analysis of traffic flow in incident road section affected by traffic accident . Science Technology and Engineering, 2011; 28.
  7. Heydecker B G?Addison J D. Analysis and modeling of traffic flow under variable speed limits. Transportation Research Part C?2011;19: 206—217.
  8. MccreaJ?MoutariS. A hybrid macroscopic-based model for traffic flow in road networks. European Journal of Operational Research?2010; 207:676—684.
  9. [US] the United States Traffic Research Committee.Ren Futian, Xiaoming Liu, Jian Rong translation.Road Capacity Manual . Beijing: People's Communications Press, 2007,12.
  10. Jiang Liu. Mountain Two-lane road Capacity Research. Beijing: Beijing University of Technology, 2006.
  11. Kuanmin Chen, Baojie Yan. Road capacity analysis . Beijing: People's Communications Press, 2003: 44-51,67-91.
  12. Jin-chuan Chen, Xiao-ming Liu, Fu-tian Ren, et al.Advances in Operation Analysis of Road Interweaving Area . Highway Traffic Science and Technology.2000,17 (l): 46-50.
  13. Jian Rong, Futian Ren, Xiaoming Liu.Study on Simulation Model of Basic Section of Expressway. Beijing: Beijing University of Technology, Ministry of Communications Highway Research Institute. 1999: 1-60.
  14.  Jinyu Duan, Design of Traffic Simulation Software System for Ramp Area of Freeway. Journal of Highway and Transportation Research and Development, 1999,16 (l): 31-35.
  15. People's Republic of China Ministry of Housing and Urban-Rural Construction. Urban Road Engineering Design Code (CJJ37-2012) . Beijing: China Building Industry Press, 2012: 8-9.
  16. Yang Weizhong, Zhang Tian. SPSS statistical analysis and industry application case. Beijing: Tsinghua University Press, 2011: 49-51. [13] Structural Equation Modeling. Chongqing: Chongqing University Press, 2009: 120-150.
  17. Qisen Zhang, Yaping Zhang. Road capacity analysis. Beijing: People's Communications Press, 2002: 4-6,36-86 .

International Journal of Scientific Research in Science, Engineering and Technology

Downloads

Published

2023-04-30

Issue

Volume 10, Issue 2, March-April-2023

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
N. Akhila, M. Kavya, M. Soumith Reddy, Dr. Prasanta Kumar Pradhan "Traffic Prediction For Intelligent Transport System Using Machine Learning" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 10, Issue 2, pp.173-181, March-April-2023.