International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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Comparative Study of PSO-ANN in Estimating Traffic Accident Severity

  • Md. Ashikuzzaman (Department of Computer Science and Engineering, East West University) ;
  • Wasim Akram (Department of Computer Science and Engineering, East West University) ;
  • Md. Mydul Islam Anik (Department of Computer Science and Engineering, East West University) ;
  • Taskeed Jabid (Department of Computer Science and Engineering, East West University) ;
  • Mahamudul Hasan (Department of Computer Science and Engineering, East West University) ;
  • Md. Sawkat Ali (Department of Computer Science and Engineering, East West University)
  • Received : 2023.08.05
  • Published : 2023.08.30
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Abstract

Due to Traffic accidents people faces health and economical casualties around the world. As the population increases vehicles on road increase which leads to congestion in cities. Congestion can lead to increasing accident risks due to the expansion in transportation systems. Modern cities are adopting various technologies to minimize traffic accidents by predicting mathematically. Traffic accidents cause economical casualties and potential death. Therefore, to ensure people's safety, the concept of the smart city makes sense. In a smart city, traffic accident factors like road condition, light condition, weather condition etcetera are important to consider to predict traffic accident severity. Several machine learning models can significantly be employed to determine and predict traffic accident severity. This research paper illustrated the performance of a hybridized neural network and compared it with other machine learning models in order to measure the accuracy of predicting traffic accident severity. Dataset of city Leeds, UK is being used to train and test the model. Then the results are being compared with each other. Particle Swarm optimization with artificial neural network (PSO-ANN) gave promising results compared to other machine learning models like Random Forest, Naïve Bayes, Nearest Centroid, K Nearest Neighbor Classification. PSO- ANN model can be adopted in the transportation system to counter traffic accident issues. The nearest centroid model gave the lowest accuracy score whereas PSO-ANN gave the highest accuracy score. All the test results and findings obtained in our study can provide valuable information on reducing traffic accidents.

Keywords

References

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