This paper examines the relationship between fatal road traffic accidents and potential predictors using multilayer perceptron artificial neural network (MLANN) models. The initial analysis employed twelve potential predictors, including traffic volume, prevailing weather conditions, roadway characteristics and features, drivers’ age and gender, and number of lanes. Based on the output of the model and the variables’ importance factors, seven significant variables are identified and used for further analysis to improve the performance of models. The model is optimized by systematically changing the parameters, including the number of hidden layers and the activation function of both the hidden and output layers. The performances of the MLANN models are evaluated using the percentage of the achieved accuracy, R-squared, and Sum of Square Error (SSE) functions.
References
[1]
World Health Organization (2023) Road Traffic Injuries. https://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries
[2]
Xu, A., Chang, H., Xu, Y., Li, R., Li, X. and Zhao, Y. (2021) Applying Artificial Neural Networks (ANNs) to Solve Solid Waste-Related Issues: A Critical Review. Waste Management, 124, 385-402. https://doi.org/10.1016/j.wasman.2021.02.029
[3]
Shaik, M.E., Islam, M.M. and Hossain, Q.S. (2021) A Review on Neural Network Techniques for the Prediction of Road Traffic Accident Severity. Asian Transport Studies, 7, Article 100040. https://doi.org/10.1016/j.eastsj.2021.100040
[4]
Abiodun, O.I., Jantan, A., Omolara, A.E., Dada, K.V., Mohamed, N.A. and Arshad, H. (2018) State-of-the-Art in Artificial Neural Network Applications: A Survey. Heliyon, 4, e00938. https://doi.org/10.1016/j.heliyon.2018.e00938
[5]
Tealab, A. (2018) Time Series Forecasting Using Artificial Neural Networks Methodologies: A Systematic Review. Future Computing and Informatics Journal, 3, 334-340. https://doi.org/10.1016/j.fcij.2018.10.003
[6]
Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y. and Alsaadi, F.E. (2017) A Survey of Deep Neural Network Architectures and Their Applications. Neurocomputing, 234, 11-26. https://doi.org/10.1016/j.neucom.2016.12.038
[7]
Paliwal, M. and Kumar, U.A. (2009) Neural Networks and Statistical Techniques: A Review of Applications. Expert Systems with Applications, 36, 2-17. https://doi.org/10.1016/j.eswa.2007.10.005
[8]
Uzair, M. and Jamil, N. (2020) Effects of Hidden Layers on the Efficiency of Neural Networks. 2020 IEEE 23rd International Multitopic Conference (INMIC), Bahawalpur, 5-7 November 2020, 1-6. https://doi.org/10.1109/inmic50486.2020.9318195
[9]
Sowdagur, J.A., Rozbully-Sowdagur, B.T.B. and Suddul, G. (2022) An Artificial Neural Network Approach for Road Accident Severity Prediction. 2022 IEEE Zooming Innovation in Consumer Technologies Conference (ZINC), Novi Sad, 25-26 May 2022, 267-270. https://doi.org/10.1109/zinc55034.2022.9840576
[10]
Ma, X., Dai, Z., He, Z., Ma, J., Wang, Y. and Wang, Y. (2017) Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction. Sensors, 17, Article 818. https://doi.org/10.3390/s17040818
[11]
Tang, J., Liang, J., Han, C., Li, Z. and Huang, H. (2019) Crash Injury Severity Analysis Using a Two-Layer Stacking Framework. Accident Analysis & Prevention, 122, 226-238. https://doi.org/10.1016/j.aap.2018.10.016
[12]
Fu, X., Meng, H., Wang, X., Yang, H. and Wang, J. (2022) A Hybrid Neural Network for Driving Behavior Risk Prediction Based on Distracted Driving Behavior Data. PLOS ONE, 17, e0263030. https://doi.org/10.1371/journal.pone.0263030
[13]
Kunt, M.M., Aghayan, I. and Noii, N. (2012) Prediction for Traffic Accident Severity: Comparing the Artificial Neural Network, Genetic Algorithm, Combined Genetic Algorithm and Pattern Search Methods. Transport, 26, 353-366. https://doi.org/10.3846/16484142.2011.635465
[14]
de Medrano, R. and Aznarte, J.L. (2021) A New Spatio-Temporal Neural Network Approach for Traffic Accident Forecasting. Applied Artificial Intelligence, 35, 782-801. https://doi.org/10.1080/08839514.2021.1935588
[15]
Delasalles, E., Ziat, A., Denoyer, L. and Gallinari, P. (2019) Spatio-Temporal Neural Networks for Space-Time Data Modeling and Relation Discovery. Knowledge and Information Systems, 61, 1241-1267. https://doi.org/10.1007/s10115-018-1291-x
[16]
Sharma, B., Kumar, S., Tiwari, P., Yadav, P. and Nezhurina, M.I. (2018) ANN Based Short-Term Traffic Flow Forecasting in Undivided Two Lane Highway. Journal of Big Data, 5, Article No. 48. https://doi.org/10.1186/s40537-018-0157-0
[17]
Ali, A., Ud-Din, S., Saad, S., Ammad, S., Rasheed, K. and Ahmad, F. (2021) Artificial Neural Network Approach to Study the Effect of Driver Characteristics on Road Traffic Accidents. 2021 International Conference on Data Analytics for Business and Industry (ICDABI), Sakheer, 25-26 October 2021, 277-280. https://doi.org/10.1109/icdabi53623.2021.9655827
[18]
Kashania, A.T., Moghadama, M.R. and Amirifar, S. (2022) Factors Affecting Driver Injury Severity in Fatigue and Drowsiness Accidents: A Data Mining Framework. Journal Injury Violence Research, 14, 75-88.
[19]
García de Soto, B., Bumbacher, A., Deublein, M. and Adey, B.T. (2018) Predicting Road Traffic Accidents Using Artificial Neural Network Models. Infrastructure Asset Management, 5, 132-144. https://doi.org/10.1680/jinam.17.00028
[20]
Delen, D., Sharda, R. and Bessonov, M. (2006) Identifying Significant Predictors of Injury Severity in Traffic Accidents Using a Series of Artificial Neural Networks. Accident Analysis & Prevention, 38, 434-444. https://doi.org/10.1016/j.aap.2005.06.024
[21]
Nuli, S., Vikranth, N. and Gupta, K.A. (2022) Real-Time Traffic Prediction Using Neural Networks. IOP Conference Series: Earth and Environmental Science, 1086, Article 012029. https://doi.org/10.1088/1755-1315/1086/1/012029
[22]
Sroczyński, A. and Czyżewski, A. (2023) Road Traffic Can Be Predicted by Machine Learning Equally Effectively as by Complex Microscopic Model. Scientific Reports, 13, Article No. 14523. https://doi.org/10.1038/s41598-023-41902-y
Moghaddam, F.R., Afandizadeh, S. and Ziyadi, M. (2011) Prediction of Accident Severity Using Artificial Neural Networks. International Journal of Civil Engineering, 9, 41-48.
[25]
Gorzelanczyk, P. (2023) Application of Neural Networks to Forecast the Number of Road Accidents in Provinces in Poland. Heliyon, 9, e12767. https://doi.org/10.1016/j.heliyon.2022.e12767
[26]
Dipto, I.C., Rahman, M.A., Islam, T. and Rahman, H.M.M. (2020) Prediction of Accident Severity Using Artificial Neural Network: A Comparison of Analytical Capabilities between Python and R. Journal of Data Analysis and Information Processing, 8, 134-157. https://doi.org/10.4236/jdaip.2020.83008
[27]
U.S. Department of Transportation (1990) Highway Performance Monitoring System. Field Manual, FHWA Publication 5600.1A.
[28]
Kassu, A. and Hasan, M. (2020) Factors Associated with Traffic Crashes on Urban Freeways. Transportation Engineering, 2, Article 100014. https://doi.org/10.1016/j.treng.2020.100014
[29]
Kassu, A. and Anderson, M. (2019) Analysis of Severe and Non-Severe Traffic Crashes on Wet and Dry Highways. Transportation Research Interdisciplinary Perspectives, 2, Article 100043. https://doi.org/10.1016/j.trip.2019.100043
[30]
Hadi, M.A., Aruldhas, J., Chow, L. and Wattleworth, J.A. (1995) Estimating Safety Effects of Cross-Section Design for Various Highway Types using Negative Binomial Regression. Transportation Research Record. Journal of the Transportation Research Board, 1500, 169-177.
[31]
Caliendo, C., Guida, M. and Parisi, A. (2007) A Crash-Prediction Model for Multilane Roads. Accident Analysis & Prevention, 39, 657-670. https://doi.org/10.1016/j.aap.2006.10.012
[32]
Venkataraman, N., Ulfarsson, G.F. and Shankar, V.N. (2013) Random Parameter Models of Interstate Crash Frequencies by Severity, Number of Vehicles Involved, Collision and Location Type. Accident Analysis & Prevention, 59, 309-318. https://doi.org/10.1016/j.aap.2013.06.021
[33]
Anastasopoulos, P.C., Tarko, A.P. and Mannering, F.L. (2008) Tobit Analysis of Vehicle Accident Rates on Interstate Highways. Accident Analysis & Prevention, 40, 768-775. https://doi.org/10.1016/j.aap.2007.09.006
[34]
Li, Z., Chen, C., Ci, Y., Zhang, G., Wu, Q., Liu, C., et al. (2018) Examining Driver Injury Severity in Intersection-Related Crashes Using Cluster Analysis and Hierarchical Bayesian Models. Accident Analysis & Prevention, 120, 139-151. https://doi.org/10.1016/j.aap.2018.08.009
[35]
Plainis, S., Murray, I.J. and Pallikaris, I.G. (2006) Road Traffic Casualties: Understanding the Night-Time Death Toll. Injury Prevention, 12, 125-138. https://doi.org/10.1136/ip.2005.011056
