Developing a Soft Computing based Decision Tree Approach for Predicting Crashes Severity on Regional Highway Corrid

Document Type : علمی - پژوهشی

Author

Assistant Professor, Department of Civil Engineering, Faculty of Engineering, The University of Guilan, Rasht

Abstract

Developing intelligent and novel methods for crash prevention or reducing crash severity in regional highway corridor is one of the major goals of road safety research. This study presents a comprehensive approach using geospatial information systems and data mining to analyze the severity of highway corridors crashes and identify the most spatial contributing factors. The approach implements Fuzzy Classification and Regression Tree (FCART) on a database of spatial data and four year period accident records in the study corridor (Qazvin-Rasht). The proposed method is tested on the crash data using a 10-fold cross validation process and the results are compared with Classification and Regression Tree (CART) model. The results show that FCART model inducts crash severity better than CART model and its overall accuracy is higher than CART model. Moreover, the sensitivity analysis of FCART model indicates that beside vehicle failure, using seatbelt and weather condition factors, curve and the spatial distribution and prevalence of activities and land uses in the proximity of highway corridors are among the most important factors affecting the severity of injuries and increase opportunities for crash occurrences.

Keywords

References

  1. گزارش سازمان بهداشت جهانی، 2013، بازیابی: 19 تیر 1394 از وب‌سایت:
  2. http://www.who.int/violence_injury_prevention/publications/road_traffic/world_report/en/
  3. Ayati, E., 2012, The Cost of Interurban and Intercity Accidents, Transportation Research Institute report, the University of Science and Technology.
  4. Aworemi, J.R., Segun, A.I. & Oluwaseun, O., 2010, Analytical Study of the Causal Factors of Road Traffic Crashes in Southwestern Nigeria, Educational Research, Vol.1, No.4, pp. 118-124.
  5. Bhall, K., Naghavi, M., Shahraz, S., Bartels, D. & Murray, C.J.L., 2009, Building National Estimates of the Burden of Road Traffic Injuries in Developing Countries from All Available Data Sources: Iran, Injury Prevention, Vol.15, No. 3, 150–156.
  6. Clarke, R., Forsyth, R. & Wright, R., 1998, Machine Learning in Road Accident Research: Decision Trees Describing Road-Accidents During Cross-Flow Turns, Ergonomics, Vol.41, No.7, pp.1060–1079.
  7. Chang, L. & Chen W., 2005, Data Mining of Tree-Based Models to Analyze Freeway Accident Frequency, Safety Research, Vol.36, No.4, pp.365-375.
  8. Chiou, Y.C., Lan, L.W. & Chen, W.P., 2013, A Two-Stage Mining Framework to Explore Key Risk Conditions on One-Vehicle Crash Severity, Accident Analysis & Prevention, Vol.50, pp. 405– 415.
  9. Delen, D., Sharda, R. & Bessonov, M., 2006, Identifying Significant Predictors of Injury Severity in Traffic Accidents Using a Series of Artificial Neural Networks, Accident Analysis & Prevention, Vol.38, No. 3, pp. 434-444.
  10. Effati, M., Rajabi, M.A., Samadzadegan, F. & Blais, J. A., 2012, Developing a Novel Method for Road Hazardous Segment Identification Based on Fuzzy Reasoning and GIS, Journal of Transportation Technologies, Vol.2, pp.32-40.
  11. Erdogan, S., Yilmaz, I., Baybura, T. & Gullu, M., 2008, Geographical Information Systems Aided Traffic Accident Analysis System Case Study: City of Afyonkarahisar, Accident Analysis and Prevention, Vol.40, No.1, pp.174-181.
  12. Engelbrecht, A.P., 2007, Computational Intelligence: An Introduction, Second Edition, John Wiley & Sons, Ltd.
  13. Flahaut, B., Mouchart, M., Martin, E.S. & Thomas, I., 2002, The Local Spatial Autocorrelation and Kernel Method for Identifying Black Zones a Comparative Approach, Accident Analysis and Prevention, Vol.35, pp. 991-1004.
  14. Gundogdu, I.B., 2010, Applying Linear Analysis Methods to GIS-Supported Procedures for Preventing Traffic Accidents: Case Study of Konya, Safety Science, Vol. 48, pp.763-769.
  15. Geurts, K., Thomas I. & Wets, G., 2005, Understanding Spatial Concentrations of Road Accidents Using Frequent Item Sets, Accident Analysis and Prevention, 37(4), pp. 787-799.
  16. Ha, H.H. & Thill, J.C., 2011, Analysis of Traffic Hazard Intensity: A Spatial Epidemiology Case Study of Urban Pedestrians, Computers. Environment and Urban Systems, Vol.35, pp. 230-240.
  17. Janikow, C.Z., 2004, FID4, 1: An Overview, IEEE 2, PP. 877-881.
  18. Jha, M., McCall, C. & Schonfeld, P., 2001, Using GIS, Genetic Algorithms and Visualization in Highway Development, Journal of Computer - Aided Civil and Infrastructure Engineering, Vol. 16, No.6, pp. 399 – 414.
  19. Joshua, S.C. & Garber, N., 1990, Estimating Truck Accident Rate and Involvements Using Linear and Poisson Regression Models, Transportation Planning and Technology, Vol. 15, pp. 41-58.
  20. Koetse, M.J. & Rietveld P., 2009, The Impact of Climate Change and Weather on Transport: An Overview of Empirical Findings, Transportation Research Part D, Vol. 14, No.3, pp. 205-221.
  21. Martin, J.L., 2002, Relationship between Crash Rrate and Hourly Traffic Flow on Interurban Motorways, Accident Analysis and Prevention, Vol. 34, No. 5, pp. 619 – 629.
  22. Miaou, S. & Lum, H., 1993, Modeling Vehicle, Accidents and Highway Geometric Design Relationships, Accident Analysis and Prevention, Vol. 25, No. 6, pp. 689-709.
  23. Mitchell, T., 1997, Machine Learning, New York: McGraw-Hill.
  24. Morgan, A. & Mannering, F., 2011, The Effects of Road-Surface Conditions, Age, and Gender on Driver-Injury Severities, Accident Analysis and Prevention, Vol. 43(5), pp.1852–1863.
  25. Mujalli, R.O. & De Oña, J., 2011, A Method for Simplifying the Analysis of Traffic Accidents Injury Severity on Two-Lane Highways Using Bayesian Networks, Journal of Safety Research, Vol. 42, pp. 317–326.
  26. Pakgohar, A., Tabrizi, R.S., Khalilli, M. & Esmaeili, A., 2010, The Role of Human Factor in Incidence and Severity of Road Crashes Based on the CART and LR regression: A Data Mining Approach, Procedia Computer Science, Vol. 3, pp. 764–769.
  27. Shanker, V., Mannering, F. & Barfield, W., 1995, Effect of Roadway Ggeometric and Environment Factors on Rural Freeway Accident Frequencies, Accident Analysis and Prevention, Vol. 27, pp. 11-23.
  28. Siddiqui, C., Abdel-Aty, M. & Huang, H., 2012, Aggregate Nonparametric Safety Analysis of Traffic Zones, Accident Analysis and Prevention, Vol. 45, pp. 317-325.
  29. Sohn, S. & Hyungwon, S., 2001, Pattern Recognition for a Road Traffic Accident Severity in Korea, Ergonomics, Vol. 44, No. 1, pp. 101-117.
  30. Steenberghen, T., Dufays, T., Thomas, I. & Flahaut, B., 2004, Intra-Urban Location and Clustering of Road Accidents Using GIS: A Belgian Case, International Journal of Geographic Information Science, Vol. 18, No. 2, pp.169–181.
  31. Tavakoli Kashani, A. & Shariat Mohaymany, A., 2011, Analysis of the Traffic Injury Severity on Two Lane, Two-Way Rural Roads Based on Classification Tree Models, Safety Science, Vol. 49, pp.1314–1320.
  32. Theofilatos, A., Graham, D. & Yannis, G., 2012, Factors Affecting Accident Severity Inside and Outside Urban Areas in Greece, Traffic Injury Prevention, Vol. 13(5), pp. 458-467.
  33. Umanol, M., Okamoto, H., Hatono, I., Tamura, H., Kawachi, F., Umedzu & Kinoshita, J.S., 1994, Fuzzy Decision Trees by Fuzzy ID3 Algorithm and Its Application to Diagnosis Systems, In Fuzzy Systems, IEEE world Congress on Computational Intelligence, Proceedings of the third IEEE conference : pp. 2113–2118.
  34. Wang J. & Wang, X., 2011, An Ontology-Based Traffic Accident Risk Mapping Framework, Advances in Spatial and Temporal Databases, Lecture Notes in Computer Science, Springer-Verlag Berlin Heidelberg 2011, Vol. 6849/2011, pp. 21-38.
  35. Wang, X. & Kockelman, K.M., 2007, Specification and Estimation of a Spatially and Temporally Autocorrelated Seemingly Unrelated Regression Model: Application to Crash Rates in China, Transportation, Vol. 34(3), pp. 281-300.
  36. Yamada, I. & Thill, J.C., 2004, Comparison of Planar and Network K-Functions in Traffic Accident Analysis, Journal of Transport Geography, Vol. 12, pp. 149-158.
  37. Yannis, G., Papadimitriou, E., Dupont, E. & Martensen, H., 2010, Estimation of Fatality and Injury Risk by Means of in-Depth Fatal Accident Investigation Data, Traffic Injury Prevention, Vol. 11(5), pp. 492-502.
  38. Yuan, Y. & Shaw, M.J., 1995, Induction of Fuzzy Decision Trees, Fuzzy Sets and Systems, Vol. 69, pp. 125–139.
Iranian Journal of Remote Sensing & GIS
Volume 8, Issue 1 - Serial Number 1
November 2016
Pages 37-54

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