Communications - Scientific Letters of the University of Zilina 2022, 24(2):F36-F45 | DOI: 10.26552/com.C.2022.2.F36-F45

Application of Data Mining Algorithm to Investigate the Effect of Intelligent Transportation Systems on Road Accidents Reduction by Decision Tree

Mohammad Mehdi Khabiri ORCID..., Fatemeh Matin Ghahfarokhi ORCID..., Sara Sarfaraz ORCID..., Hasan Mohammadi Anaie ORCID...
Geotechnic and Road Departement, Faculty of Civil Engineering, Yazd University, Yazd, Iran

Due to the large amount of available data in this study, authors have utilized data mining algorithms, especially the decision tree, to process these data and obtain the information, which would result in increasing road safety, determining the causes affecting it and patterns leading to traffic accidents. The effective use of this tool and its role in controlling the number of driving accidents is the subject of this study with the help of data mining algorithms. The results show that the increase in the number of roadside assistances to more than 41; number of driving accidents (fatally injured) is not significantly different, hence one of the proposed strategies for intelligent relay stations and its organization with the intelligent transportation tool is available. The intelligent transportation system utilities comprise of monitoring, guidance and enforcement tools, plus service tools such as rescue, driver assistant and road improvement.

Keywords: data mining algorithm, intelligent transportation systems (ITS), driving accidents, road safety, decision tree

Received: March 1, 2021; Accepted: November 23, 2021; Prepublished online: March 7, 2022; Published: April 1, 2022  Show citation

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Khabiri, M.M., Matin Ghahfarokhi, F., Sarfaraz, S., & Anaie, H.M. (2022). Application of Data Mining Algorithm to Investigate the Effect of Intelligent Transportation Systems on Road Accidents Reduction by Decision Tree. Communications - Scientific Letters of the University of Zilina, 24(2), F36-45. doi: 10.26552/com.C.2022.2.F36-F45
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    References

    1. AHMADZADEH, T., KARKEABADI, Z. Investigating the effect of installing intelligent transportation systems on reducing road losses case study of Shahroud - Sabzevar. Quarterly Journal of Semnan Police Science. 2008, 8(27), p. 91-29. ISSN 2322-1771.
    2. SHEIKHOZEDDIN, H., SHARIFIAN, V., QURESHI, S. F. The need to use intelligent transportation systems to improve road safety. In: The 1st National Symposium to Improve Road Safety: proceedings. 2015.
    3. ZIARI, H., KHABIRI, M. M. Analysis characteristics and provide a prediction model of public bus accident in Tehran. Journal of Applied Sciences [online]. 2006, 6(2), p. 247-250. ISSN 1812-5654, eISSN 1812-5662. Available from: https://doi.org/10.3923/jas.2006.247.250 Go to original source...
    4. ASHRAFZADEH, A., GHORBANI, A., DOLAMA, H. Application of intelligent transportation systems in road traffic management. In: International Conference on Civil Engineering, Architecture and Urban Infrastructure: proceedings. 2015.
    5. AKBARI NIA, F., JAFARI, M. H. Locating variable message sign (VMS) in intra urban networks using ARCGIS. Journal of Civil Engineering and Structures. 2017, 1(1), p. 44-54. ISSN 2588-3275.
    6. TAHERI, V. Evaluation of car accident in Iran and the rule of intelligent transportation systems in decreasing of collisions in the highways. In: 3rd National Conference on Road Accidents, Rail and Air Accidents: proceedings. 2014. p. 1-13.
    7. TOLOUEI, M., ZOGHI, H., SIAMARDI, K. Application of intelligent driver assistance system in order to increase traffic safety on Iranian roads (case study of highways in the south of Tehran. In: 10th International Conference on Transportation and Traffic Engineering: proceedings. 2016.
    8. AMJADI TALEBI, M. H., KIAMEHR, R. Location of freeway intelligent transportation systems for traffic safety using GIS (study of speed control cameras on the Qazvin-Rasht freeway. In: The 1st Conference on New Ideas and Technologies in Geographical Sciences: proceedings. 2017.
    9. KUEHN, M., HUMMEL, T., BENDE, J. Benefit estimation of advanced driver assistance systems for cars derived from real-life accidents. In: 21st International Technical Conference on the Enhanced Safety of Vehicles ESV 2009: proceedings. Vol. 15. 2009.
    10. SONG, B. Potential safety benefit analysis of cooperative driver assistance systems via vehicle-to-vehicle communications. The Journal of the Korea Institute of Intelligent Transport Systems [online]. 2018, 17(2), p. 128-141. ISSN 1738-0774, eISSN 2384-1729. Available from: https://doi.org/10.12815/kits.2018.17.2.128 Go to original source...
    11. ZOU, Y., ASH, J. E., PARK, B. J., LORD, D., WU, L. Empirical Bayes estimates of finite mixture of negative binomial regression models and its application to highway safety. Journal of Applied Statistics [online]. 2018, 45(9), p. 1652-1669. ISSN 0266-4763. Available from: https://doi.org/10.1080/02664763.2017.1389863 Go to original source...
    12. NAGHAWI, H., AL QATAWNEH, B., AL LOUZI, R. Evaluation of Automated Enforcement Program in Amman. Periodica Polytechnica Transportation Engineering, [online]. 2018, 46(4), p. 201-206. ISSN 0303-7800, eISSN 1587-3811. Available from: https://doi.org/10.3311/PPtr.10939 Go to original source...
    13. CHANG, L.-Y. Analysis of effects of manhole covers on motorcycle driver maneuvers: a nonparametric classification tree approach. Traffic Injury Prevention [online]. 2014, 15(2), p. 212-206. ISSN 1538-9588, eISSN 1538-957X. Available from: https://doi.org/10.1080/15389588.2013.792110 Go to original source...
    14. NIAZI, M., ASGARI, A., NOURANI, E. The role of police surveillance cameras in reducing driving violations. Traffic Management Studies Quarterly. 2018, 47(4), p. 67-82. ISSN 2008-4005.
    15. VEISI, H. Machine learning, decision tree. Tehran: Faculty of Modern Sciences and Technologies, University of Tehran, 2014.
    16. SOLEIMANPOUR, S. M., MESBAH, S. H., HEDAYATI, B. Application of CART decision tree data mining to determine the most effective drinking water quality factors (case study: Kazeroon plain, Fars province). Iranian Journal of Health and Environment [online]. 2018, 11(2), p. 1-14. ISSN 2008-2029, eISSN 2008-3718. Available from: http://ijhe.tums.ac.ir/article-1-5881-en.html
    17. KHALILI, S., PASHAZADEH, S. Classification of current bank account data using the decision tree. In: 2nd Conference of Computer and Information Technology Scholars: proceedings. 2014. p. 1-8.
    18. ASHOORI, M., NAJIMOGHADAM, V., ALIZADEH, S., SAFI, M. Classification and clustering algorithm application for prediction of tablet numbers: case study diabetes disease. journal of the Health Information Management Association of Australia. 2013, 10(5), p. 739-749. ISSN 1833-3575.
    19. SHARM, H., KUMAR, S. A survey on decision tree algorithm of classification in data mining. International Journal of Science and Research. 2016, 5(4), p. 2024 -2097. eISSN 2319-7064. Go to original source...
    20. Statistical yearbook of road transportation. Summary of highways and road transportation statistics of the country in management view. Roads and Road Transportation Organization, Data and Communication Management and Public Relations Management, 2016.

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