Analysis of road accidents with emphasis on environmental and road characteristics in geospatial information system Case study of Karaj-Kandovan axis

Document Type : Research Paper

Authors

1 Master student of Civil Engineering-GIS, Islamic Azad University of Ramsar, Iran

2 Assistant Professor of Civil Engineering Department, Rahman Institute of Higher Education, Ramsar, Iran

Abstract

Road traffic accidents are considered as one of the most important causes of human death in all over the world and in our country Iran. The increase of daily travels and traffic volume are the key factors of high number of traffic accidens in Iran. In a road transportation system, human behaviour, road and vehicle are the main contributing factors in traffic accidents. While human errors relating to driver performance are the cause of almost 50 to 95% of crashes, road and environmnal factors in different locations and times of driving may also affect driver performance and increase the risk of a crash. Identification of road hotspots is a geographical decision. GIS offer both spatial and temporal dimensions required for data preparation, spatial analysis, decision making and management of road crashes.
The main objective of this research is the spatial analysis of casualty crashes and prioritization the available risk factors that contribute in crash incidence. The stady ares, is a part of Karaj-Chalous road, a main mountainous road connecting Karaj to North of Iran.
In doing so, in a GIS contex, a decision tree method is used for crash analysis. The decision tree is a hierarchical knowledge structure that represents a sequence of decision rules. The purpose of the method is to determine which attribute or criterion of attributes provides the best distribution of the actual data set regardless of the value of the given attribute. According to the objective of the research, this research method was conducted in several stages. Firstly, available sources and previouse similar studies were reviewd, and the accident data points were collected. Secondly, the points were corrected based on the collected data source and judtified and implemented in kilometers. Thirdly, information about the slope and climate of the area, the intersections and used points around the study area were collected and the required layers were prepared and standardised. Next, prepared and standardized layers were processed and drawn by the decision tree model and weighted decision tree model. Then the prepared layers were combined and the resulting output probability map of the accidents along the study area was prepared.
The results of studies showed that, according to the analysis, the most important effective factor in the occurrence of accidents is: curvature with a coefficient of 0.51. After that, the intersection layer with the coefficient of 0.21, and climate with the coefficient of 0.144 are the second and third factors respectively. Other variables such as density and slope are the next available probabibility factors of crash incidence in the study area. Based on the coefficients, a zoning map of the probability of accidents was prepared. The prepared map shows the high intensity of the probability of a crash in selected points. According to the obtained map, the highest risk area is at the entrance to the road and at points of intersection where the curvature of the road increases.
The results of this research and prioritization of the crash risk factors in decreasing the costs of road safety improvement.

Keywords

Main Subjects

References

  1. اطلاعات آماری مستخرج از پایگاه ‏اینترنتی سازمان پزشک قانونی کشور، 1398، برگرفته از سایت www.imo.ir
  2. اطلاعات آماری مستخرج از پایگاه ‏اینترنتی سازمان پزشک قانونی کشور، 1398 و 1399، برگرفته از سایت www.imo.ir
  3. رحمانی، محمد، 1395، پهنه‏بندی تصادفات جاده‏ای با هدف تعیین نقاط حادثه‏خیز با استفاده از GIS (نمونة موردی: مسیر همدان- ملایر)، فصل‏نامة آمایش محیط، شمارة 34، صص 155-175.
  4. رضایی، محمدرضا و نوری، محبوبه، 1397، نقش سرمایة اجتماعی در آمادگی افراد قبل از وقوع زلزله (مطالعة موردی: شهروندان شهر کرمان)، پژوهش و برنامه‏ریزی شهری.
  5. زنگی‏آبادی، علی؛ شیران، غلام‏رضا و گشتیل، خیری، 1391، بررسی علل تصادفات در بزرگراه‏ها (مورد مطالعه: بزرگراه‏های درون‏شهری اصفهان)، فصل‏نامة علمی- ترویجی راهور، سال نهم، شمارة 17، صص 37- 57.
  6. سازمان بهداشت جهانی، 1385، گزارش جهانی در خصوص پیشگیری از صدمات ناشی از تصادفات جاده‏ای، ناشر: پژوهشکدة حمل و نقل، صص 1-101.
  7. عبادی‏نژاد، سیدعلی؛ شادفر، صمد؛ شادمانی، علیرضا و جعفریان، محمدحسن، 1385، نقش مه در ایجاد حوادث جاده‏های کشور، فصل‏نامة دانش انتظامی، 8 (4)، 57-66.
  8. قبادی، محمد و حسن‏زاده، محمدرضا، 1396، بررسی عوامل مؤثر در وقوع تصادفات شهری، فصل‏نامة مطالعات مدیریت ترافیک، سال 12، شمارة 44، صص 71-86.
  9. کامیابی، سعید و علی‏پور، سیدخلیل، 1392، ارزیابی تصادفات جاده‏ای در شرایط مختلف جوی در جاده‏های اصلی استان سمنان، فصل‏نامة پژوهشی راهور، 3 (8)، 115-136.
  10. مالچفسکی، ی.، 1999، سامانة اطلاعات جغرافیایی و تحلیل تصمیم‏گیری چندمعیاری، ترجمة علی‏اکبر پرهیزکار، تهران: سمت.
  11. ماهپور، علی‏رضا، 1393، بررسی عوامل مؤثر بر شدت تصادفات برون‏شهری و ارائة مدل مناسب (مطالعة موردی: استان تهران)، طرح تحقیقاتی مرکز تحقیقات کاربردی پلیس راهور نهاجا، گزارش پژوهشی، شمارة 7.
  12. نیک‏زاد، میرفاضل، 1386، سوانح ترافیکی کشور و خسارات ناشی از آن، تهران: پلیس راهنمایی و رانندگی ناجا.
  13. Af Wåhlberg, A., 2012, Driver behaviour and accident research methodology: unresolved problems: Ashgate Publishing, Ltd.
  14. Alian, S.; Baker, R. G. V. and Wood, S., 2016, Rural casualty crashes on the Kings Highway: A new approach for road safety studies. Accident Analysis & Prevention, 95, 8-19.
  15. Chelghoum, N.; Zeitouni, K. and Boulmakoul, A., 2002, A decision tree for multi-layered spatial data. In Advances in Spatial Data Handling (pp. 1-10). Springer, Berlin, Heidelberg.
  16. Clarke, D. D.; Forsyth, R. and Wright, R., 1999, Junction road accidents during cross-flow turns: a sequence analysis of police case files. Accident Analysis & Prevention, 31(1-2), 31-43.
  17. ESRI, 2006, ArcView GIS, ESRI Inc.,http://www.esri.com/base/products/arcview/arcview.html (accessed 15/10/06).
  18. Haddo, Jr W., 1968, The changing approach to the epi-demiology, prevention, and amelioration of trauma: the transition to approaches etiologically rather than descriptively based. American Journal of Public Health, 58, 1431-1438.
  19. Han, J. and Kamber, M., 2001, Data mining concepts and techniques, Morgan Kaufmann Publishers. San Francisco, CA, 335-391.
  20. Hijar, M.; Vazquez-Vela, E. and Arreola-Risa, C., 2003, Pedestrian traffic injuries in Mexico: a country Injury Control and Safety Promotion, 10, 37-43.
  21. Huxhold, W. E., 1991, An introduction to urban geographic information systems. OUP Catalogue.
  22. Karsahim, M. and Sedral, T., 2002, Distribution of Hazardous Location on Highway through GIS. International Symposium on GIS, 23-26.
  23. Khorashadi, A.; Niemeier, D.; Shankar, V. and Mannering, F., 2005, Differences in rural and urban driver-injury severities in accidents involving large-trucks: an exploratory analysis. Accident Analysis & Prevention, 37(5), 910-921.
  24. Konduri, S.; Labi, S. and Sinha, K.C., 2003, Incident occurrence models for freeway incident management. Transportation Research Record, 1856, 125-135.
  25. Le, K.G.; Liu, P. and Lin, L.T., 2020, Determining the road traffic accident hotspots using GIS-based temporal-spatial statistical analytic techniques in Hanoi, Vietnam. Journal of Taylor and Francis, 23 (2), 153-164.
  26. Lonero, L., 2002, Road safety as a social construct. Ottawa, Northport Associates, 2002 (Transport Canada Report No. 8080-00-1112).
  27. Malczewski, J., 1999, GIS and multicriteria decision analysis. John Wiley & Sons.
  28. Mehmood, A., 2010, An integrated approach to evaluate policies for controlling traffic law violations, Accident Analysis & Prevention, 42(2), 427-436.
  29. Miller, H. J. and Han, J. (Eds.), 2009, Geographic data mining and knowledge discovery. CRC press.
  30. Mock, C. N. and Maier, R. V., 1997, Low utilization of formal medical services by injured persons in a developing nation: health service data underestimate the importance of trauma. Journal of Trauma and Acute Care Surgery, 42(3), 504-513.
  31. Mohan, D., 2002, Road safety in less-motorised environment: future concerns. International Journal of Epidemiology, 31, 527-532.
  32. Naboureh, A.; Feizizadeh, B.; Naboureh, A.; Bian, J.; Blaschke, T.; Ghorbanzadeh, O. and Moharrami, M., 2019, Traffic Accident Spatial Simulation Modeling for Planning of Road Emergency Services. International Journal of Geo-Information. 8(371), 1-16.
  33. Nantulya, VM. and Reich, MR., 2003, Equity dimensions of road Traffic injuries in low- and middle income countries. Injury Control and Safety Promotion, 10, 13-20.
  34. Park, S. H.; Kim, S. M. and Ha, Y. G., 2016, Highway traffic accident prediction using VDS big data analysis. The Journal of Supercomputing, 72(7), 2815-2831.
  35. Quinlan, J. R., 1979, Discovering rules by induction from large collections of examples. Expert systems in the micro electronics age.
  36. Sanaeinasab, H.; Irani, Gh. A.; Rafati, H. and Karimi, A. A., 2009, Traffic accidents: A survey on traffic accidents frequency and effective factors in a military base in Tehran. Police Management Studies Quarterly Science Journal, 4(1), 19-30, (in Persian).
  37. Seifollahi, S. and Nemati, F., 2012, The effective social factors of driving accidents’ occurrences in Tehran (Case of Study: Fatal Accidents), Journal of Iranian Social Development Studies, 4(2), 113-129, (in Persian).
  38. Shannon, C. E., 1948, A mathematical theory of communication. The Bell system technical journal, 27(3), 379-423.
  39. Tisca, I.A.; Istrat, N.; Dumitrescu, C.D. and Cornu, G., 2016, Issues concerning the road safety concept, Journal of Procedia Economics and Finance, 39: 441- 445.
  40. Twa, M. D.; Parthasarathy, S.; Raasch, T. W. and Bullimore, M. A., 2003, Decision tree classification of spatial data patterns from videokeratography using Zernike polynomials. In Proceedings of the 2003 SIAM International Conference on Data Mining (pp. 3-12). Society for Industrial and Applied Mathematics.
  41. Verma, D. and Nashine, R., 2012, Data Mining: Next Generation Challenges and FutureDirections. International Journal of Modeling and Optimization, 2(5), 603.
  42. Voget, A. and Bared, J., 1999, Accident models for two lane rural segments and intersection, Transportation Research Record, Issue 1635, pp. 18-29.
  43. Waller, P., 2001, Public health’s contribution to motor vehicle injury prevention. American Journal of Preventive Medicine, 21(Suppl. 4), 3-4.
  44. Wang, C.; Quddus, M. A. and Ison, S. G., 2013, The effect of traffic and road characteristics on road safety: A review and future research direction. Safety Science, 57(0), 264-275.
  45. WHO, 2010, Mobile phone use: a growing problem of driver distraction. Geneva, Switzerland: World Health Organisation.
Human Geography Research
Volume 54, Issue 2
May 2022
Pages 563-582

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History

  • Receive Date: 06 December 2020
  • Revise Date: 20 February 2021
  • Accept Date: 20 February 2021

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