Istrazivanja i projektovanja za privreduJournal of Applied Engineering Science

ANALYZING DRIVING ENVIRONMENT FACTORS IN PEDESTRIAN CRASHES INJURY LEVELS IN JAKARTA AND THE SURROUNDING CITIES


DOI 10.5937/jaes17-22121
This is an open access article distributed under the CC BY-NC-ND 4.0 terms and conditions. 
Creative Commons License

Volume 17 article 634 pages: 482 - 489

Martha Leni Siregar* 
Faculty of Engineering, Universitas Indonesia, Indonesia

R. Jachrizal Sumabrata 
Faculty of Engineering, Universitas Indonesia, Indonesia

Andyka Kusuma 
Faculty of Engineering, Universitas Indonesia, Indonesia

Omas Bulan Samosir 
Faculty of Economics and Business, Universitas Indonesia, Indonesia

Silvanus Nohan Rudrokasworo 
Faculty of Engineering, Universitas Indonesia, Indonesia

Pedestrian-vehicle crashes are the results of a combination of influencing factors including the driving enviroment. This paper looks into the driving environment factors in pedestrian crashes injury levels on road links in Jakarta and the surounding cities which contribute to the city traffic generation.  The  vehicle-pedestrian accident data used were obtained from the 2016 Indonesian national police accident database covering 4,646 pedestrian accidents on road links  from Jakarta, Depok, Tangerang Selatan, Tangerang and Bekasi, Indonesia. Various factors were analyzed including crash level severity, month of occurrence, weather condition, lighting condition, road function, road class, road type, road surface condition and road status. As injury levels were categorized into slight injury, severe injury and fatal injury and it was assumed that the dependent variables which were crash injury levels could not be perfectly predicted from the independent variables, Multinomial logistic regression (MNL) was used in the analysis to predict the probability of different categories of dependent variables. It was found that the relative risks of pedestrian accident risks factors changed with different categories both in terms of fatal and severe injuries. One of the findings shows that the risk of having severe injuries would decrease by 40.2% on national roads, by 70.5% on provincial roads and by 53.5% on urban roads. The results can be expected to be referred to in the improvement of pedestrian safety level and in the development of related measures.

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This work was supported by the PDUPT research grant from the Ministry of Research, Technology and Higher Education the Republic of Indonesia (contract number 411/UN2.R3.1/HKP05.00/2018).

1. United Nation Road Safety Collaboration [Internet]. Available from: https://www.who.int/roadsafety/en/ [accessed Jan 5, 2019]

2. OECD Scientific Expert Group on the Safety of Vulnerable Road Users (RS7). (1998). Safety of Vulnerable Road Users. [accessed Mar 13, 2019]

3. Moudon A.V., Lin L., Jiao J., Hurvitz P., Reeves P. (2009).The risk of pedestrian injury and fatality in collisions with motor vehicles, a social ecological study of state routes and city streets in King County, Washington. Accid Anal Prev, 2011;43(1):11–24, DOI: 10.1016/j.aap.2009.12.008

4. Retting R.A., Ferguson S.A., McCartt A.T., (2003). A Review of Evidence-Based Traffic Engineering Measures Designed to Reduce Pedestrian–Motor Vehicle Crashes. Am J Public Heal; 93(9):1456–63, DOI: 10.2105/ajph.93.9.1456

5. Roberts I., Crombie I., (1995). Child pedestrian deaths: sensitivity to traffic volume evidence from the USA. J Epidemiol Community Health.;49:186–8, DOI: 10.1136/jech.49.2.186

6. Tate F., Turner S.., (2007). Road geometry and drivers speed choice. In: IPENZ Transportation Group Conference Tauranga [Internet]. Tauranga;. Available from:  ipenz.org.nz/ipenztg/archives.htm [accessed: December 2, 2018]

7. Davis G.A.,(2001). Relating Severity of Pedestrian Injury to Impact Speed in Vehicle-Pedestrian Crashes Simple Threshold Model. Transp Res Rec.;1773(01):108–13, DOI: 10.3141/1773-13

8. Roudsari B., Mock C., Kaufman R., Grossman D., Henary B., Crandall J. (2004). Pedestrian crashes: higher injury severity and mortality rate for light truck vehicles compared with passenger vehicles. Inj Prev.,10(3):154–8, DOI: 10.1136/ip.2003.003814

9. Wong S., Sze N., Li Y. (2007). Contributory factors to traffic crashes at signalized intersections in Hong Kong. Accid Anal Prev.,39(6):1107–13, DOI: 10.1016/j.aap.2007.02.009

10. Gichaga F.J. (2016). The impact of road improvements on road safety and related characteristics. IATSS Res.,40(2):72–5, DOI: 10.1016/j.iatssr.2016.05.002

11. Pour-rouholamin M, Zhou H. (2016). Investigating the risk factors associated with pedestrian injury severity in Illinois. J Safety Res., 57:9–17, DOI: 10.1016/j.jsr.2016.03.004

12. Siregar M.L., Agah H.R., Hidayatullah F. Near-miss accident analysis for traffic safety improvement at a ‘channelized’ junction with U-turn. Int J Saf Secur Eng. 2018;8(1). DOI: 10.2495/SAFE-V8-N1-31-38

13. Javid, A. M., & Al-Neama, Y. M. [2018]. Identification of factors causing driver's distraction in Oman. Journal of Applied Engineering Science, 16(2), 153-160.

14. Bongiorno N., Bosurgi G., Pellegrino O., Sollazzo G. (2017). How is the Driver’s Workload Influenced by the Road Environment? Procedia Eng.,187:2–13, DOI: 10.1016/j.proeng.2017.04.343

15. WHO [Internet]. [cited 2018 Mar 23]. Available from: https://www.who.int/violence_injury_prevention/road_safety_status/2018/en/

16. IRSMS [Internet]. [cited 2018 Mar 23]. Available from: korlantas.info

17. Ulfarsson G.F., Kim S., Booth K.M. (2010), Analyzing fault in pedestrian – motor vehicle crashes in North Carolina. Accid Anal Prev., 42(6):1805–13, DOI: 10.1016/j.aap.2010.05.001

18. Perencanaan Geometrik Jalan Antar Kota No. 038/TBM/1997. Direktorat Jenderal Bina Marga, Departemen Pekerjaan Umum Republik Indonesia;

19. Tay R., Choi J., Kattan L., Khan A. (2011), A Multinomial Logit Model of Pedestrian–Vehicle Crash Severity. Int J Sustain Transp.,5(4):233–49, DOI: 10.1080/15568318.2010.49754710.1080/15568318.2010.497547

20. Anastasopoulos P.C., Mannering F.L., Shankar V., Haddock JE. (2011). A study of factors affecting highway accident rates using the random-parameters Tobit model. Accid Anal Prev., 45:628–33, DOI: 10.1016/j.aap.2011.09.015

21. Kim M., Kho S-Y., Kim D-K. (2017). Hierarchical ordered model for injury severity of pedestrian crashes in South Korea. J Safety Res., 61:22–40,  DOI: 10.1016/j.jsr.2017.02.011

22. Milton J., Shankar V., Mannering F. (2008). Highway accident severities and the mixed logit model: an exploratory empirical analysis. Accid Anal Prev. , 40:260–6,  DOI: 10.1016/j.aap.2007.06.006

23. Kim J., Ulfarsson G.F., Shankar V.N., Mannering F.L. (2010). A note on modeling pedestrian-injury severity in motor-vehicle crashes with the mixed logit model. Accid Anal Prev., 42(6):1751–8, DOI: 10.1016/j.aap.2010.04.016

24. Huang H., Abdel-Aty M.A., Darwich A.L. (2010). County-Level Crash Risk Analysis in Florida: Bayesian Spatial Modeling. Transp Res Rec., 2148(1):27–37, DOI: 10.3141/2148-04

25. Abdel-Aty M.A. (2003). Analysis of driver injury severity levels at multiple locations using ordered probit models. J Safety Res., 34, DOI: 10.1016/j.jsr.2003.05.009

26. Quddus; M.A, Wang C., Ison S.G. (2010). Road Traffic Congestion and Crash Severity: Econometric Analysis Using Ordered Response Models. J Transp Eng.,136(5), DOI: https://doi.org/10.1061/(ASCE)TE.1943-5436.0000044

27. Aziz H.M.A., Ukkusuri S.V., Hasan S. (2013). Exploring the determinants of pedestrian–vehicle crash severity in New York City. Accid Anal Prev.,50:1298–309,  DOI: 10.1016/j.aap.2012.09.034

28. Greene, W. H. (2012). Econometric Analysis (Seventh ed.). Boston: Pearson Education. pp. 803–806. ISBN 978-0-273-75356-8.

29. Wang C., Quddus M.A., Ison S.G. (2013).The effect of traffic and road characteristics on road safety: A review and future research direction. Saf Sci., 57:264–75, DOI: 10.1016/j.ssci.2013.02.012.

30. Rankavat S., Tiwari G. (2016). Pedestrians risk perception of traffic crash and built environment features – Delhi, India. Saf Sci.,1–7, https://doi.org/10.1016/j.ssci.2016.03.009 .

31. Chen S., Saeed T.U., Alinizzi M, Lavrenz S., Labi S. (2019). Safety sensitivity to roadway characteristics: A comparison across highway classes. Accid Anal Prev;123:39–50. https://doi.org/10.1016/j.aap.2018.10.020

32. Fotios S. (2019). Road lighting and the detection of slip hazards when walking. Light Res Technol., 51(2):324–5, DOI: 10.1177/1477153519837893.

33. Siregar M.L., Alawiyah T., Tjahjono T.. Remedial safety treatment of accident-prone locations. Int J Technol. 2015;6(4). DOI: 10.14716/ijtech.v6i4.1097

34.Lee C., Abdel-Aty M. (2005). Comprehensive analysis of vehicle-pedestrian crashes at intersections in Florida. Accid Anal Prev., 37(4):775–86, DOI: 10.1016/j.aap.2005.03.019.

35. Borowsky A., Oron-Gilad T, Meir A., Parmet Y. (2012). Drivers’ perception of vulnerable road users: A hazard perception approach. Accid Anal Prev., 44(1):160–6, DOI: 10.1016/j.aap.2010.11.029 .

36.Gitelman V., Doveh E., Carmel R., Pesahov F. (2014). The Relationship Between Road Accidents and Infrastructure Characteristics of Low-Volume Roads in Israel. In: Proceedings of Second International Conference on Traffic and Transport Engineering (ICTTE). Belgrade: Transportation Research Board, p. 350–8.

37. Vorko-Jovic A., Kern J., Biloglav Z. (2006). Risk factors in urban road traffic accidents. J Safety Res., 37, DOI: 10.1016/j.jsr.2005.08.009.

38. Demetriades D., Murray J., Martin M., Velmahos G., Salim A. Alo K., et al. (2004). Pedestrians injured by automobiles relationship of age to injury type and severity. J Trauma, 199:382–7, DOI: 10.1016/j.jamcollsurg.2004.03.027.

39. Kröyer H.R.G. (2015). Is 30 km/h a ‘safe’ speed? Injury severity of pedestrians struck by a vehicle and the relation to travel speed and age. IATSS Res., 39(1):42–50, DOI: 10.1016/j.iatssr.2014.08.001.