Mining Relationships among Knowledge, Attitude, and Practice of Drivers Using Self‑organizing Map and Decision Tree: The Case of Bandar Abbas City Taxi Drivers

Document Type: Original Article

Authors

1 Department of Industrial Engineering, Birjand University of Technology, Birjand

2 Departments of Occupational Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran

3 Research Center for Social Determinants in Health Promotion, Hormozgan University of Medical Sciences, Bandar Abbas, Iran

4 Departments of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran

Abstract

Background and Objectives: Traffic accidents are the leading causes of fatal or nonfatal work‑related injuries in many countries. Analyzing
influencing factors on knowledge, attitude, and practice of drivers is a topic of interest for policymakers to decrease traffic accident injury
victims. Materials and Methods: In this article, a two‑stage data mining approach was presented for determining the mining relationships
among knowledge, attitude, and practice of drivers. In the first stage, because of existing multidimensional practice variables, self‑organizing map
neural network was utilized to automatically arrange drivers into two safe and unsafe driving practice clusters. In the second stage, a decision
tree was used to model relationships among knowledge and attitude of drivers and practice clusters. The authors’ designed questionnaires were
used to collect data in 235 male taxi drivers of Bandar Abbas city in Iran regarding the drivers’ knowledge and attitude toward traffic regulations.
The driving practices were assessed using a prepared checklist. Results: The most important attribute affecting practice of drivers was the
maximum safe speed in the city. Conclusions: The results of this investigation showed that drivers’ knowledge toward traffic regulations had
a dramatic impact on safe driving practices. Levels of drivers’ education can influence practice of drivers.

Keywords


1. Tajvar A, Yekaninejad MS, Aghamolaei T, Shahraki SH, Madani A, Omidi L. Knowledge, attitudes, and practice of drivers towards traffic regulations in Bandar‑Abbas, Iran. Electron Physician 2015;7:1566‑74.

2. Redhwan A, Karim A. Knowledge, attitude and practice towards road traffic regulations among university students, Malaysia. Int Med J Malays 2010;9:29-34.

3. Salari H, Motevalian SA, Arab M, EsfandiariA, Akbari SariA. Exploring measures to control road traffic injuries in Iran: Key informants points of view. Iran J Public Health 2017;46:671‑6.

4. Moghaddam AM, Tabibi Z, Sadeghi A, Ayati E, Ravandi AG. Screening out accident‑prone Iranian drivers: Are their at‑fault accidents related to driving behavior? Transp Res F Traffic Psychol Behav 2017;46:451‑61.

5. Machin MA, De Souza JM. Predicting health outcomes and safety behaviour in taxi drivers. Transp Res F Traffic Psychol Behav 2004;7:257‑70.

6. Al‑Khaldi YM. Attitude and practice towards road traffic regulations among students of Health Sciences College in Aseer Region. J Family Community Med 2006;13:109‑13.

7. Mirzaei R, Hafezi‑Nejad N, Sadegh Sabagh M, Ansari Moghaddam A, Eslami V, Rakhshani F, et al. Dominant role of drivers’ attitude in prevention of road traffic crashes: A study on knowledge, attitude, and practice of drivers in Iran. Accid Anal Prev 2014;66:36‑42.

8. Yunesian M, Moradi A. Knowledge, attitude and practice of drivers regarding traffic regulations in Tehran. J Sch Public Health Inst Public Health Res 2005;3:57‑66.

9. Turner C, McClure R. Age and gender differences in risk‑taking behaviour as an explanation for high incidence of motor vehicle crashes as a driver in young males. Inj Control Saf Promot 2003;10:123‑30.

10. Ulleberg P, Rundmo T. Risk‑taking attitudes among young drivers: The psychometric qualities and dimensionality of an instrument to measure young drivers’ risk‑taking attitudes. Scand J Psychol 2002;43:227‑37.

11. Kashani AT, Mohaymany AS. Analysis of the traffic injury severity on two‑lane, two‑way rural roads based on classification tree models. Saf Sci 2011;49:1314‑20.

12. De Oliveira JV, Pedrycz W. Advances in Fuzzy Clustering and its Applications. Chichester: John Wiley & Sons; 2007.

13. Zarandi M, Hadavandi E, Turksen I. A hybrid fuzzy intelligent agent‐ based system for stock price prediction. Int J Intell Syst 2012;27:947‑69.

14. Hadavandi E, Shavandi H, Ghanbari A. Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting. Knowl Based Syst 2010;23:800‑8.

15. Kohonen T. The self‑organizing map. Proc IEEE 1990;78:1464‑80.

16. Kohonen T. Essentials of the self‑organizing map. Neural Netw 2013;37:52‑65.

17. Al‑Ghamdi AS. Using logistic regression to estimate the influence of accident factors on accident severity. Accid Anal Prev 2002;34:729‑41.

18. Abellán J, López G, De Oña J. Analysis of traffic accident severity using decision rules via decision trees. Expert Syst Appl 2013;40:6047‑54.

19. de Oña J, López G, Abellán J. Extracting decision rules from police accident reports through decision trees. Accid Anal Prev 2013;50:1151‑60.

20. Kass GV. An exploratory technique for investigating large quantities of categorical data. Appl Statist 1980;29:119-27.

21. Bekkar M, Djemaa HK, Alitouche TA. Evaluation measures for models assessment over imbalanced data sets. J Inf Eng Appl 2013;3:27-38.

22. Jolliffe I. Principal Component Analysis. New York: Wiley Online Library; 2005.

23. Björklund GM, Åberg L. Driver behaviour in intersections: Formal and informal traffic rules. Transp Res F Traffic Psychol Behav 2005;8:239‑53.

24. Greibe P. Accident prediction models for urban roads. Accid Anal Prev 2003;35:273‑85.

25. Hassen A, Godesso A, Abebe L, Girma E. Risky driving behaviors for road traffic accident among drivers in Mekele City, Northern Ethiopia. BMC Res Notes 2011;4:535.

26. Poursadeghiyan M, Omidi L, Hami M, Raei M, Biglari H. Epidemiology of fatal and non‑fatal industrial accidents in Khorasan Razavi Province, Iran. Int J Trop Med 2016;11:170‑4.