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Title |
Comparative Study of PSO-ANN in Estimating Traffic Accident Severity |
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Author |
Md. Ashikuzzaman, Wasim Akram1, Md. Mydul Islam Anik, Taskeed Jabid1, Mahamudul Hasan and Md. Sawkat Ali |
| Citation |
Vol. 23 No. 8 pp. 95-100 |
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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, Na?ve 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. |
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Keywords |
Machine learning model, Traffic accident severity, Particle swarm optimization, Hybrid artificial neural network, Prediction accuracy |
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