Investigating the Effect of Marginal Areas around the Cities on Rural Road Accidents in Iran Using Linear and Logistic Regression Approaches

Document Type : Research Paper


1 Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 Professor, School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran

3 Assistant Professor, Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran


According to previous studies, 60% to 70% of the total rural road accidents would occur at the city entrance zones in Iran. Therefore, the characteristics of these zones could be considered as effective parameters in rural road accidents. In all prior studies, a 30-km buffer of the cities' entrances has been assumed as the border of the entrance zone. The 30-km buffer could not be considered as the boundary of the influenced area (BIA) of the cites´ entrance for all types of the roads and cities, merely based on aggregate rural road accidents´ data and a traditional definition of the city entrance zone. Determining the BIA for various rural roads with different characteristics using the modelling approach is the innovative aspect of this research. Furthermore, according to their specifications, implementing safety improvements in these areas, not only reduce the number of rural road accidents and fatalities, but also prevent the loss of road safety costs due to the economic problems of Iran. Thus, this study aimed to develop linear and logistic regression models to predict the BIA of rural roads in Iran. The results of this study indicated a fit index value of 0.737 for the linear regression model, and 0.379 and 0.346 for the ordered probit (OP) and multinomial logit (ML) models, respectively. The analysis of significant variables at 95% confidence level, revealed that the access points' density, and the length of adjacent land uses are the most significant variables affecting the BIA.


  • Afandizadeh, S. and Golshan-Khavas, R. (2006) “Developing a model for determination the road safety index in city entrance zones”, Journal of Transportation Research, Vol. 3, No. 4, pp. 223-242. (In Persian)


  • Ahmadi, P. (2014) “Prioritization of proposed solutions in order to reduce road accidents on cities' entrance by multi-criteria decision making method”, MSc Thesis, Shomal University, Amol, Iran. (In Persian)


  • Akbarpour, M. (2013) “Studies of road accidents on cities' entrance (case study: Ahvaz–Andimeshk road)”, MSc Thesis, Shahid Chamran University, Ahvaz, Iran. (In Persian)


  • Akbarpour, M., Amini, A. and Najafi-Alamdarlou, M. (2021) “The review of effective factors in the occurrence of accidents at the entrance of cities (case study: Northwest exit road of Ahvaz)”, The 6th International Conference on Advanced Research in Science Engineering and Technology. (In Persian)


  • Allison, P.D. (1999) “Logistic regression using the SAS system: Theory and Application”, Cary NC, SAS Institute Inc.


  • Asare, I.O. and Mensah, A.C. (2020) “Crash severity modelling using ordinal logistic regression approach”, International Journal of Injury Control and Safety Promotion, pp. 1-8.


  • Boroujerdian, A.M., Saffarzadeh, M. and Abolhasannejad, V. (2010) “Developing a model for prioritising high crash road segments”, Proceedings of the Institution of Civil Engineers-Transport, Vol. 163, No. 1, pp. 19-29.


  • Dashtestaninejad, H., Amiri, M. and Ehsani-Sohi, M. (2018) “Discrete choice models to predict severity of accidents near the city of Tehran (30 kilometer buffer)”, The 17th International Conference on Traffic and Transportation Studies, Tehran, Iran. (In Persian)


  • Davoodi, S.R. and Ahmadi, P. (2015) “Prioritizing suggested strategies to reduce road accidents at the cities entrance using analytic hierarchy process”, International Journal of Asian Social Science, Vol. 5, No. 12, pp. 706-714.


  • Effati, M., Rajabi, M.A. and Samadzadegan, F. (2012) “Developing a novel method for road hazardous segment identification based on fuzzy reasoning and GIS”, Journal of Transportation Technologies, Vol. 2, No. 1, pp. 32-40.


  • Effati, M., Rajabi, M.A. and Samadzadegan, F. (2014) “A geospatial neuro-fuzzy approach for identification of hazardous zones in regional transportation corridors”, International Journal of Civil Engineering, Vol. 12, No. 3, pp. 289-303.


  • Ehsani-Sohi, M, Dashtestaninejad, H. and Khademi, E. (2019) “Effects of roadway and traffic characteristics on accidents frequency at city entrance zone”, International Journal of Transportation Engineering, Vol. 7, No. 2, pp. 139-152.


  • Elyasi, M. R., Saffarzadeh, M. and Boroujerdian, A. M. (2016) “A novel dynamic segmentation model for identification and prioritization of black spots based on the pattern of potential for safety improvement”, Transportation Research Part A, Vol. 91, pp. 346–357.


  • Elyasi, M. R., Saffarzadeh, M., Boroujerdian, A. M., Semnarshad, M. and Mazaheri, M. (2017) “Prioritization of suburban accident factors based on analytical network process”, International Journal of Transportation Engineering, Vol. 5, No. 2, pp. 197-209.


  • Elyasi, M.R., Saffarzadeh, M. and Boroujerdian, A.M. (2018) “Assessing the interrelations of traffic collisions' risk factors”, Proceedings of the Institution of Civil Engineers-Transport, Vol. 171, No. 6, pp. 309-318.


  • Fitzpatrick, K., Lord, D. and Park, B.J. (2010) “Horizontal curve accident modification factor with consideration of driveway density on rural four-lane highways in Texas”, J Transp Eng, Vol. 136, No. 9, pp. 827-835.


  • Ghasedi, M., Sarfjoo, M. and Bargegol, I. (2021) “Prediction and analysis of the severity and number of suburban accidents using logit model, factor analysis and machine learning: a case study in a developing country”, SN Applied Sciences, Vol. 3, No. 1, pp. 1-16.


  • Gundogdu, I.B. (2011) “A new approach for GIS-supported mapping of traffic accidents”, Proceedings of the institution of civil engineers-transport, Vol. 164, No. 2, pp. 87-96.


  • Haghani, M., Jalalkamali, R. and Berangi, M. (2019) “Assigning crashes to road segments in developing countries”, Proceedings of the Institution of Civil Engineers-Transport, Vol. 172, No. 5, pp. 299-307.


  • Hosseinlou, M.H. and Sohrabi, M. (2009) “Predicting and identifying traffic hot spots applying neuro-fuzzy systems in intercity roads”, International Journal of Environmental Science & Technology, Vol. 6, No. 2, pp. 309-314.


  • Khabiri, M. and Ahmadinejad, M. (2003) “Investigating the causes of accidents at the cities' entrance in Iran”, The 10th Students Conference on Civil Engineering, Tehran, Iran. (In Persian)


  • Mohaymany, A.S., Shahri, M. and Mirbagheri, B. (2013) “GIS-based method for detecting high-crash-risk road segments using network kernel density estimation”, Geo-spatial Information Science, Vol. 16, No. 2, pp. 113-119.


  • O'donnell, C.J. and Connor, D.H. (1996) “Predicting the severity of motor vehicle accident injuries using models of ordered multiple choice”, Accident Analysis & Prevention, Vol. 28, No. 6, pp. 739-753.


  • Pai, C.W. and Saleh, W. (2008) “Exploring motorcyclist injury severity in approach-turn collisions at T-junctions: Focusing on the effects of driver's failure to yield and junction control measures”, Accident Analysis & Prevention, Vol. 40, No. 2, pp. 479-486.


  • RMTO (Iran Road Maintenance and Transportation Organization) (1999) “Analysis of accidents at city entrance zones”, Tehran, Iran. (In Persian)
  • RMTO (Iran Road Maintenance and Transportation Organization) (2020) “Road Statistical Yearbook”, (In Persian)


  • Saheli, M.V. and Effati, M. (2021) “Segment-Based Count Regression Geospatial Modeling of the Effect of Roadside Land Uses on Pedestrian Crash Frequency in Rural Roads”, International Journal of Intelligent Transportation Systems Research, Vol. 19, No. 2, pp. 347-365.


  • Sajed, Y., Shafabakhsh, G. and Bagheri, M. (2019) “Hotspot Location Identification Using Accident Data, Traffic and Geometric Characteristics”, Engineering Journal, Vol. 23, No. 6, pp. 191-207.


  • Sasidharan, L. and Menéndez, M. (2014) “Partial proportional odds model-An alternate choice for analyzing pedestrian crash injury severities”, Accident Analysis & Prevention, Vol. 72, pp. 330-340.


  • Shafabakhsh, G. and Mousavi, M. (2006) “Investigating the causes of accidents at the cities' entrance and organizing surrounding areas for immunization”, The 13th Students Conference on Civil Engineering, Kerman, Iran. (In Persian)


  • Shafabakhsh, G.A., Famili, A. and Akbari, M. (2016) “Spatial analysis of data frequency and severity of rural accidents”, Transportation Letters, pp. 1-8.


  • Sheikholeslami, S., Bondarabadi, M.A. and Asadamraji, M. (2020) “A rural road accident probability model based on single-vehicle hazard properties including hazard color and mobility: a driving simulator study”, Journal of advanced transportation.


  • Singh, G., Pal, M., Yadav, Y. and Singla, T. (2020) “Deep neural network-based predictive modeling of road accidents”, Neural Computing and Applications, pp. 1-10.


  • Singh, G., Sachdeva, S.N. and Pal, M. (2018) “Support vector machine model for prediction of accidents on non-urban sections of highways”, Proceedings of the Institution of Civil Engineers-Transport, Vol. 171, No. 5, pp. 253-263.


  • Singh, G., Sachdeva, S.N. and Pal, M. (2018), “Comparison of three parametric and machine learning approaches for modeling accident severity on non-urban sections of Indian highways”, Adv Transp Stud Int J Sect B 45, pp. 123-140.


  • Wang, X. and Abdel-Aty, M. (2008) “Analysis of left-turn crash injury severity by conflicting pattern using partial proportional odds models”, Accident Analysis & Prevention, Vol. 40, No. 5, pp. 1674-1682.


  • Washington, S., Karlaftis, M. and Mannering, F. (2011) “Statistical and Econometric Methods for Transportation Data Analysis, 2nd ed”, Chapman and Hall/CRC, Boca Raton,FL.


  • WHO (World Health Organization) (2018) “Global Status Report on Road Safety”,