Communications - Scientific Letters of the University of Zilina 2024, 26(2):E1-E11 | DOI: 10.26552/com.C.2024.023

Estimation of Vulnerable Road User Accident Frequency through the Soft Computing Models

Saurabh Jaglan ORCID...1, *, Sunita Kumari ORCID...1, Praveen Aggarwal2
1 Civil Engineering Department, Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Sonepat, Haryana, India
2 Civil Engineering Department, National Institute of Technology, Kurukshetra, Haryana, India

Accident prediction models are mathematical expressions or algorithms used to determine the causal factors for road accidents and road safety engineers are using these models, as well. Modelling this kind of accident is quite challenging and required good quality of data. The results of the artificial neural network model, Gaussian processes model, and support vector machine model are compared for vulnerable road accident frequency in this study. The accident frequency dataset comprises 218 records, with 146 designated for training purposes and 72 reserved for testing. The model's accuracy was contingent on: the mean absolute error, root mean square error and coefficient of correlation. The findings suggest that for predicting the vulnerable road user accidents on roads, the artificial neural network gives better correlation results as (0.912) that the support vector machine (0.879) and Gaussian processes (0.853).

Keywords: vulnerable road user, accident frequency, artificial neural network, support vector machine and Gaussian processes
Grants and funding:

The authors received no financial support for the research, authorship and/or publication of this article.

Conflicts of interest:

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Received: October 27, 2023; Accepted: February 21, 2024; Prepublished online: March 11, 2024; Published: April 2, 2024  Show citation

ACS AIP APA ASA Harvard Chicago Chicago Notes IEEE ISO690 MLA NLM Turabian Vancouver
Jaglan, S., Kumari, S., & Aggarwal, P. (2024). Estimation of Vulnerable Road User Accident Frequency through the Soft Computing Models. Communications - Scientific Letters of the University of Zilina, 26(2), E1-11. doi: 10.26552/com.C.2024.023
Share...
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. BUBELINY, O., DADOVA, I., KUBINA, M., SOVIAR, J. The use of smart elements for the transport operation in the Slovak cities. LOGI - Scientific Journal on Transport and Logistics [online]. 2019, 10, p. 51-60. eISSN 2336-3037. Available from: https://doi.org/10.2478/logi-2019-0015 Go to original source...
    2. Road accidents in India [online]. 2021. Available from: www.morth.nic.in
    3. SIHAG, P., TIWARI, N. K., RANJAN, S. Prediction of cumulative infiltration of sandy soil using random forest approach. Journal of Applied Water Engineering and Research [online]. 2019, 7(2), p. 118-142. eISSN 2324-9676. Available from: https://doi.org/10.1080/23249676.2018.1497557 Go to original source...
    4. SINGH, B., SIHAG, P., TOMAR, A. SEHGAD, A. Estimation of compressive strength of high-strength concrete by random forest and M5P model tree approaches. Journal of Materials and Engineering Structures JMES. 2019, 6(4), p. 583-892. eISSN 2170-127X.
    5. IVAZ, J., NIKOLIC, R. R., PETROVIC, D., DJOKOVIC, J. M., HADZIMA, B. Prediction of the work-related injuries based on neural networks. System Safety: Human - Technical Facility - Environment [online]. 2021, 3(1), p. 19-37. eISSN 2657-5450. Available from: https://doi.org/10.2478/czoto-2021-0003 Go to original source...
    6. PURI, D., KUMAR, R., SIHAG, P., THAKUR, M. S., PERVEEN, K., ALFAISAL, F. M., LEE, D. Analytical investigation of the impact of jet geometry on aeration effectiveness using soft computing techniques. ACS Omega [online]. 2023, 8(42), p. 31811-31825. eISSN 2470-1343. Available from: https://doi.org/10.1021/acsomega.3c03294 Go to original source...
    7. GOEL, A. K., KHAN, K., KUSHWAHA, A., SRIVASTAVA, V., MALIK, S., SINGH, A. A machine learning approach to analyze road accidents. In: 2022 IEEE International Conference on Blockchain and Distributed Systems Security ICBDS: proceedings [online]. IEEE. 2022. eISBN 978-1-6654-2832-3, p. 1-5. Available from: https://doi.org/10.1109/ICBDS53701.2022.9935867 Go to original source...
    8. SUTHAR, M., AGGARWAL, P. Modeling CBR value using RF and M5P techniques. Mendel [online]. 2019, 25(1), p. 73-78. ISSN 1803-3814, eISSN 2571-3701. Available from: https://doi.org/10.13164/mendel.2019.1.073 Go to original source...
    9. SUN, Z., XING, Y., WANG, J., GU, X., LU, H., CHEN, Y. Exploring injury severity of vulnerable road user involved crashes across seasons: a hybrid method integrating random parameter logit model and Bayesian network. Safety Science [online]. 2022, 150, 105682. ISSN 0925-7535, eISSN 1879-1042. Available from: https://doi.org/10.1016/j.ssci.2022.105682 Go to original source...
    10. KOMOL, M. M. R., HASAN, M. M., ELHENAWY, M., YASMIN, S., MASOUD, M., RAKOTONIRAINY, A. Crash severity analysis of vulnerable road users using machine learning. PLoS One [online]. 2021, 16, e0255828. eISSN 1932-6203. Available from: https://doi.org/10.1371/journal.pone.0255828 Go to original source...
    11. BAMEL, K., DASS, S., JAGLAN, S., SUTHAR, M. Statistical analysis and development of accident prediction model of road safety conditions in Hisar city. IOP Conference Series: Earth and Environmental Science [online]. 2021, 889, 012034. ISSN 1755-1315. Available from: https://doi.org/10.1088/1755-1315/889/1/012034 Go to original source...
    12. LEE, J., MANNERING, F. Impact of roadside features on the frequency and severity of run-off-roadway accidents: an empirical analysis. Accident Analysis and Prevention [online]. 2002, 34(2), p. 149-161. ISSN 0001-4575, eISSN 1879-2057. Available from: https://doi.org/10.1016/S0001-4575(01)00009-4 Go to original source...
    13. MILTON, J., MANNERING, F. The relationship among highway geometrics, traffic-related elements and motor-vehicle accident frequencies. Transportation [online]. 1998, 25, p. 395-413. ISSN 0049-4488, eISSN 1572-9435. Available from: https://doi.org/10.1023/A:1005095725001 Go to original source...
    14. JAGLAN, S., AGGARWAL, P., KUMARI, S. A comparative study of M5P, ANN and RENB models for prediction of vulnerable road accident frequency, n.d.
    15. JAGLAN, S., KUMARI, S., AGGARWAL, P. Development of prediction models for vulnerable road user accident severity. Optical Memory and Neural Networks [online]. 2023, 32, p. 346-363. ISSN 1060-992X, eISSN 1934-7898. Available from: https://doi.org/10.3103/S1060992X23040082 Go to original source...
    16. JAGLAN, S., AGGARWAL, P., SINGHAL, D., DASS, S. Vulnerable road user accidents analysis on various roads of Haryana. AIP Conference Proceedings [online]. 2023, 2856, 020003. ISSN 0094-243X, eISSN 1551-7616. Available from: https://doi.org/10.1063/5.0165909 Go to original source...
    17. GARCIA DE SOTO, B., BUMBACHER, A., DEUBLEIN, M., ADEY, B. T. Predicting road traffic accidents using artificial neural network models. Infrastructure Asset Management [online]. 2018, 5(4), p. 132-144. ISSN 2053-0242, eISSN 2053-0250. Available from: https://doi.org/10.1680/jinam.17.00028 Go to original source...
    18. CODUR, M. Y., TORTUM, A. An artificial neural network model for highway accident prediction: a case study of Erzurum, Turkey. Promet-Traffic and Transportation [online]. 2015, 27(3), p. 217-225. ISSN 0353-532, eISSN 1848-4069. Available from: https://doi.org/10.7307/ptt.v27i3.1551 Go to original source...
    19. CORTES, C., VAPNIK, V. Support - vector networks. Machine Learning [online]. 1995, 20, p. 273-297. ISSN 0885-6125, eISSN 1573-0565. Available from: https://doi.org/10.1007/BF00994018 Go to original source...
    20. KARCH, J. D., BRANDMAIER, A. M., VOELKLE, M. C. Gaussian process panel modelling - machine learning inspired analysis of longitudinal panel data. Frontiers in Psychology [online]. 2020, 11, 351. eISSN 1664-1078. Available from: https://doi.org/10.3389/fpsyg.2020.00351 Go to original source...

    This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.


    Return to the content