Neural Network-Based Road Accident Forecasting in Transportation and Public Management

  • Georgios N. KouziokasEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 879)


The development of Information and Communication Technology (ICT) has influenced transportation management in multiple ways. The application of artificial intelligence techniques has gained ground lately in many scientific sectors. In this research, artificial neural network models were constructed in order to predict data about the road accidents in the study area. Several parameters were taken into consideration in order to optimize the predictions and to build the optimal forecasting model such as the number of the neurons in the hidden layers and the nature of the transfer functions. A Feedforward Multilayer Perceptron (FFMLP) was utilized, as it is considered as one of the most suitable structures for time series forecasting problems according to the literature. The optimal prediction model was tested in the study area and the results have shown a very good prediction accuracy. The road accident predictions will help public management to adopt the appropriate transportation management strategies.


Artificial neural networks Transportation management Transportation safety Public management 


  1. 1.
    Kouziokas, G.N.: Technology-based management of environmental organizations using an Environmental Management Information System (EMIS): design and development. Environ. Technol. Innov. 5, 106–116 (2016). Scholar
  2. 2.
    Kouziokas, G.N.: Geospatial based information system development in public administration for sustainable development and planning in urban environment. Eur. J. Sustain. Dev. 5(4), 347 (2016).
  3. 3.
    Grant-Muller, S., Usher, M.: Intelligent transport systems: the propensity for environmental and economic benefits. Technol. Forecast. Soc. Chang. 82, 149–166 (2014)CrossRefGoogle Scholar
  4. 4.
    Kouziokas, G.N., Perakis, K.: Decision support system based on artificial intelligence, GIS and remote sensing for sustainable public and judicial management. Eur. J. Sustain. Dev. 6(3), 397–404 (2017).
  5. 5.
    Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transp. Res. Procedia 24, 467–473 (2017). Scholar
  6. 6.
    Yu, R., Liu, X.: Study on traffic accidents prediction model based on RBF neural network. In: 2010 2nd International Conference on Information Engineering and Computer Science, pp. 1–4. IEEE (2010)Google Scholar
  7. 7.
    Tambouratzis, T., Souliou, D., Chalikias, M., Gregoriades, A.: Combining probabilistic neural networks and decision trees for maximally accurate and efficient accident prediction. In: The 2010 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2010)Google Scholar
  8. 8.
    Jilani, T.A., Burney, S.A., Ardil, C.: Multivariate high order fuzzy time series forecasting for car road accidents. Int. J. Comput. Intell. 4, 15–20 (2007)Google Scholar
  9. 9.
    Svozil, D., Kvasnicka, V., Pospichal, J.: Introduction to multi-layer feed-forward neural networks. Chemom. Intell. Lab. Syst. 39(1), 43–62 (1997)CrossRefGoogle Scholar
  10. 10.
    Hornik, K.: Approximation capabilities of multilayer feedforward networks. Neural Netw. 4(2), 251–257 (1991)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Møller, M.F.: A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw. 6, 525–533 (1993)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of EngineeringUniversity of ThessalyVolosGreece

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