Istrazivanja i projektovanja za privreduJournal of Applied Engineering Science

ENDOGENOUS RELATIONSHIP OF ACCIDENT OCCURRENCE WITH SPEED, TRAFFIC HETEROGENEITY AND DRIVING ENVIRONMENT ON INTER-URBAN ROADS IN INDONESIA


DOI: 10.5937/jaes0-25837 
This is an open access article distributed under the CC BY 4.0
Creative Commons License

Martha Leni Siregar
Universitas Indonesia, Faculty of Engineering, Department of Civil and Environmental Engineering, West Java, Indonesia

Tri Tjahjono*
Universitas Indonesia, Faculty of Engineering, Department of Civil and Environmental Engineering, West Java, Indonesia

Nahry
Universitas Indonesia, Faculty of Engineering, Department of Civil and Environmental Engineering, West Java, Indonesia

Speed performances and characteristics of traffic have mostly been considered as homogeneous across vehicles. In countries where the roads are dominated by mixed types of vehicles, the heterogeneity needs to be considered. This study is aimed at modeling how traffic heterogeneity as captured in speed, speed deviation, and traffic volume determines the fatality rates and accident rates. Traffic volume, road geometry (bendiness, hilliness, bend density and hill density) and road surface condition (represented by IRI) become the independent variables in a simultaneous regression using structural equation model (SEM). SEM is adopted to represent the hierarchical causal effects between the independent variables and dependent variables. The data cover inter-urban roads in eight provinces in Indonesia from 2012-2016 and 2019. Speed is not significant in predicting accident rate, and speed deviation is not significant in predicting fatality rate. An increase in speed deviation lowers the accident rates; an increase in speed increases fatality rates. Road geometry and traffic volume negatively impact the speed deviations of all vehicle categories, indicating that when there is more traffic on the road, the speeds of all vehicle categories become more homogenous. Bend density, bendiness, hill density and hilliness negatively affect both the speed and the speed deviations of the vehicles of all categories The findings of the study can contribute to traffic policing and traffic safety improvement schemes for heterogeneous traffic.

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This research was funded by UI PUTI Doktor Grant 2020, contract number NKB-663/UN2.RST/HKP.05.00/2020. The Authors would also like to thank the Project Management Unit of the Australian-funded “EINRIP MONITORING & EVALUATION PROGRAMME, Fifth Monitoring Survey, Final Report 2017” for the permission to use the data.

1.Roy R, Saha P, Kumar A. Speed Distributional Trends on Two-lane Roads with Mixed Traffic under Heavy Flow. Procedia Eng. 2017;187:465–70.

2. Dhamaniya A, Chandra S. Speed Characteristics of Mixed Traffic Flow on Urban Arterials. World Acad Sci Eng Technol Int J Civ Environ Eng. 2013;7(11).

3. Wang X, Zhou Q, Quddus M, Fan T, Fang S. Speed, speed variation and crash relationships for urban arterials. Accid Anal Prev. 2018;113 (November 2016): 236–43.

4. Kloeden CN, McLean AJ, Glonek G. Reanalysis of Travelling speed and the risk of crash involvement in Adelaide, South Australia. onal Services, Australian Transport Safety Bureau; 2002.

5. Golob TF, Recker WW, Alvarez VM. Freeway safety as a function of traffic flow. Accid Anal Prev. 2004;36:933–46.

6. Cheng W, Gill GS, Sakrani T, Ralls D, Jia X. Modeling the endogeneity of lane-mean speeds and lanespeed deviations using a Bayesian structural equations approach with spatial correlation. Transp Res Part A Policy Pract. 2018;

7. Kockelman KM, Ma J. Freeway speeds and speed variations preceding crashes, within and across lanes. J Transp Res Forum. 2007;46(1):43–62.

8. Wang C, Quddus MA, Ison SG. The effect of traffic and road characteristics on road safety: A review and future research direction. Saf Sci. 2013;57:264–75.

9. Tanishita M, Wee B Van. Impact of vehicle speeds and changes in mean speeds on per vehicle-kilometer traf fic accident rates in Japan. IATSSR. 2016;0– 5.

10. Siregar, M.L., Sumabrata, R.J., Kusuma, A., Samosir, O.B., & Rudrokasworo, S. N. [2019]. Analyzing driving environment factors in pedestrian crashes injury levels in Jakarta and the surrounding cities. Journal of Applied Engineering Science, 17(4), 482- 489.

11. Papadimitriou E, Filtness A, Theofi latos A, Ziakopoulos A, Quigley C, Yannis G. Review and ranking of crash risk factors related to the road infrastructure. Accid Anal Prev. 2019;125.

12. IRSMS AIS [Internet]. [cited 2019 Mar 23]. Available from: http://118.97.77.160

13. EINRIP MONITORING & EVALUATION PROGRAMME, Fifth Monitoring Survey, Final Report. 2017.

14. Tang Z. Relationship between Speed Characteristics and Traffic Safety on Freeways in Mainland China. Period Polytech Transp Eng. 2018;47(4):318–28.

15. Hamzeie R, Savolainen PT, Gates TJ. Driver speed selection and crash risk: Insights from the naturalistic driving study. J Safety Res. 2017;63.

16. Taylor MC, Lynam DA, Baruya A. The effects of drivers speed on the frequency of road accidents,. Technical report. Crowthorne, England: TRL; 2000. 1–50 p.

17. Sadia R, Bekhor S, Polus A. Structural equations modelling of drivers’ speed selection using environmental, driver, and risk factors. Accid Anal Prev. 2018;116.

18. Sadia R, Bekhor S, Polus A. Speed variation for different drivers, situations, and road geometry: Simulator and survey analysis. J Transp Saf & Engineering. 2018;10(1–2):25–44.

19. Shinstine DS, Wulff SS, Ksaibati K. Factors associated with crash severity on rural roadways in Wyoming. J Traffic Transp Eng (English Ed. 2016;3(4).

20. Schreiber JB, Stage F, King J, Nora A, Barlow EA. Reporting Structural Equation Modeling and Confirmatory Factor Analysis Results: A Review. J Educ Res. 2006;99(6):323–37.

21. Bollen KA, Pearl J. Eight Myths About Causality and Structural Equation Models. In: Morgan SL, editor. Handbook of Causal Analysis for Social Research. Springer; 2013. p. 301–28.

22. Islam MT, El-Basyouny K. Full Bayesian evaluation of the safety effects of reducing the posted speed limit in urban residential areale. Accid Anal Prev. 2015;80:18–25.

23. Baruya A. Speed-Accident Relationship on European Roads. In: Road Safety in Europe. 1998. p. 1–19.

24. Quddus M. Exploring the Relationship Between Average Speed, Speed Variation, and Accident Rates Using Spatial Statistical Models and GIS. J Transp Saf Secur. 2013;5(1):27–45.

25. Choudhary P, Imprialou M, Velaga NR, Choudhary A. Impacts of speed variations on freeway crashes by severity and vehicle type. Accid Anal Prev [Internet]. 2018;121(January): 213–22. Available from: https://doi.org/10.1016/j.aap.2018.09.015

26. Tjahjono T. The Effect of Traffi c and Road Conditions to the Fatality Rates on Rural Roads in Eastern Indonesia. J East Asia Soc Transp Stud. 2010;8:2201– 13.

27. Gargoum SA, El-Basyouny K. Exploring the association between speed and safety: A path analysis approach. Accid Anal Prev. 2016;93.