International Journal of Transportation Engineering

International Journal of Transportation Engineering

Determination of Factors Affecting Rural Crash Frequency at Marginal Areas around Cities

Document Type : Research Paper

Authors
Hamid Shamanian Esfahani 1
Seyed Teimour Hoseini 2
Reza Sakhaei 2
Majid Ehsani Sohi 2
1 Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 Amin University, Tehran, Iran
10.22119/ijte.2024.452279.1665
Abstract
The high rate of traffic crashes and consequent losses have caused many social and economic problems in developing countries like Iran. According to previous studies, 60-70% of rural damages in Iran occur in marginal areas around cities. Hence, identifying effective factors in rural crashes around cities is of great importance to take proper safety measures in such areas. This requires developing exclusive models to predict rural crash frequency around cities. Given the limitations of fixed-parameters models, such as Poisson and negative binomial regression, the present study uses a random-parameter approach, including the random parameters negative binomial (RPNB) and random effects negative binomial (RENB) models to determine the factors affecting rural crash frequency at marginal areas around cities. The data on rural crashes on roads leading to Isfahan over 3 years, were used to develop the models. The random-parameter models were used to consider the unobserved heterogeneity among crash observations. The goodness-of-fit measures indicated the acceptable accuracy of the developed models. Meanwhile, the likelihood ratio test revealed a slight difference between the RPNB and RENB. According to the outcomes, the factors influencing crashes around cities included traffic volume, the ratio of local and non-local vehicles, the proportion of longitudinal distance-limit violations, access points controlling, and access to rest area facilities.
Keywords
Marginal areas around cities
crash frequency modelling
unobserved heterogeneity
random parameters negative binomial (RPNB) model
random effects negative binomial (RENB) model
  • Abdel-Aty, M. A. and Radwan, A. E. (2000) “Modeling traffic accident occurrence and involvement”, Accident Analysis & Prevention, 32(5), 633-642.

https://doi.org/10.1016/S0001-4575(99)00094-9

 

  • Abdel-Aty, M., Devarasetty, P. C., and Pande, A. (2009) “Safety evaluation of multilane arterials in Florida”, Accident Analysis & Prevention, 41(4), 777-788.

https://doi.org/10.1016/j.aap.2009.03.015

 

  • Afandizadeh, S. and Hassanpour, S. (2020) “Evaluating the Effect of Roadway and Development Factors on the Rural Road Safety Risk Index”, Advances in Civil Engineering, 2020.

https://doi.org/10.1155/2020/7820565

 

  • Afghari, A. P., Washington, S., Haque, M. M., and Li, Z. (2018) “A comprehensive joint econometric model of motor vehicle crashes arising from multiple sources of risk”, Analytic methods in accident research, 18, 1-14.

https://doi.org/10.1016/j.amar.2018.03.002

  • Ahmadinejad, M., Afandizadeh Zargari, S., and Jalalkamali, R. (2018) “Are deceleration numbers a suitable index for road safety?”, In Proceedings of the Institution of Civil Engineers-Transport (Vol. 171, No. 5, pp. 247-252). Thomas Telford Ltd.

https://doi.org/10.1680/jtran.16.00117

 

  • Ahmed, M., Huang, H., Abdel-Aty, M., and Guevara, B. (2011) “Exploring a Bayesian hierarchical approach for developing safety performance functions for a mountainous freeway”, Accident Analysis & Prevention, 43(4), 1581-1589.

https://doi.org/10.1016/j.aap.2011.03.021

 

  • Alrejjal, A., Moomen, M., and Ksaibati, K. (2021) “Evaluating the effectiveness of law enforcement in reducing truck crashes for a rural mountainous freeway in Wyoming”, Transportation Letters, 1-11.

https://doi.org/10.1080/19427867.2021.1949089

 

  • Anastasopoulos, P. C. and Mannering, F. L. (2009) “A note on modeling vehicle accident frequencies with random-parameters count models”, Accident Analysis & Prevention, 41(1), 153-159.

https://doi.org/10.1016/j.aap.2008.10.005

 

  • Anastasopoulos, P. C., Mannering, F. L., Shankar, V. N., and Haddock, J. E. (2012) “A study of factors affecting highway accident rates using the random-parameters tobit model. Accident”, Analysis & Prevention, 45, 628-633.

https://doi.org/10.1016/j.aap.2011.09.015

 

  • Ayati, E., and Abbasi, E. (2011) “Investigation on the role of traffic volume in accidents on urban highways”, Journal of safety research, 42(3), 209-214.

https://doi.org/10.1016/j.jsr.2011.03.006

 

  • 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. https://doi.org/10.1680/tran.2010.163.1.19

 

  • Buddhavarapu, P., Banerjee, A., and Prozzi, J. A. (2013) “Influence of pavement condition on horizontal curve safety”, Accident Analysis & Prevention, 52, 9-18.

https://doi.org/10.1016/j.aap.2012.12.010

 

  • Cai, Q., Abdel-Aty, M., Lee, J., Wang, L., and Wang, X. (2018) “Developing a grouped random-parameters multivariate spatial model to explore zonal effects for segment and intersection crash modeling”, Analytic methods in accident research, 19, 1-15.

https://doi.org/10.1016/j.amar.2018.05.001

 

  • Cafiso, S., D'Agostino, C., & Persaud, B. (2013). Investigating Influence of Segmentation in Estimating Safety Performance Functions for Roadway Sections (No. 13-4372).

 

  • Cenek, P.D., Davies, R.B., McLarin, M.W., Griffith-Jones, G., & Locke, N.J. (1997). Roadenvironment and traffic crashes. In: Transfund New Zealand Research Report 79, Wellington.

 

  • Chen, E., and Tarko, A. P. (2014) “Modeling safety of highway work zones with random-parameters and random effects models”, Analytic methods in accident research, 1, 86-95.

https://doi.org/10.1016/j.amar.2013.10.003

 

  • Dinu, R. R., and Veeraragavan, A. (2011) “Random-parameter models for accident prediction on two-lane undivided highways in India”, Journal of safety research, 42(1), 39-42.

https://doi.org/10.1016/j.jsr.2010.11.007

  • Dong, C., Nambisan, S. S., Richards, S. H., and Ma, Z. (2015) “Assessment of the effects of highway geometric design features on the frequency of truck involved crashes using bivariate regression”, Transportation Research Part A: Policy and Practice, 75, 30-41.

https://doi.org/10.1016/j.tra.2015.03.007

 

  • 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. http://dx.doi.org/10.4236/jtts.2012.21004

 

  • 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, 7(2), pp.139-152.

http://dx.doi.org/ 10.22119/IJTE.2018.130189.1404

 

  • 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. https://doi.org/10.1016/j.tra.2016.06.020

 

  • 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.

https://doi.org/10.1680/jtran.16.00070

 

  • Faezi, S. F., and Elyasi, M. R. (2020) “Predict road accidents resulted from carelessness using negative binomial regression model (A case study of rural highways in Hamadan Province)”, Journal of Transportation Research. Available:http://www.trijournal.ir/article_119190.html?lang=en

 

  • Fu, R., Guo, Y., Yuan, W., Feng, H., and Ma, Y. (2011) “The correlation between gradients of descending roads and accident rates”, Safety science, 49(3), 416-423.

https://doi.org/10.1016/j.ssci.2010.10.006

 

  • Garach, L., de Oña, J., López, G., and Baena, L. (2016) “Development of safety performance functions for Spanish two-lane rural highways on flat terrain”, Accident Analysis & Prevention, 95, 250-265.

https://doi.org/10.1016/j.aap.2016.07.021

 

  • Gong, H., Wang, F., Zhou, B. B., and Dent, S. (2020) “Application of random effects negative binomial model with clustered dataset for vehicle crash frequency analysis”, International journal of transportation science and technology, 9(3), 183-194.

https://doi.org/10.1016/j.ijtst.2020.03.010

 

  • Han, C., Huang, H., Lee, J., and Wang, J. (2018) “Investigating varying effect of road-level factors on crash frequency across regions: A Bayesian hierarchical random-parameter modeling approach”, Analytic methods in accident research, 20, 81-91.

https://doi.org/10.1016/j.amar.2018.10.002

 

  • Hosseinlou, M. H., Mahdavi, A., and Nooghabi, M. J. (2018) “Validation of the influencing factors associated with traffic violations and crashes on freeways of developing countries: A case study of Iran”, Accident Analysis & Prevention, 121, 358-366.

https://doi.org/10.1016/j.aap.2018.06.009

 

  • Hou, Q., Tarko, A. P., and Meng, X. (2018) “Investigating factors of crash frequency with random effects and random-parameters models: new insights from Chinese freeway study”, Accident Analysis & Prevention, 120, 1-12.

https://doi.org/10.1016/j.aap.2018.07.010

 

  • Imaninasab, R., Sekhavati, E., and Hajihoseinloo, M. (2016) “Modeling crash frequency of heavy vehicles in rural freeways”, Journal of Traffic and Logistics Engineering Vol, 4(2).

http://dx.doi.org/ 10.18178/jtle.4.2.98-102

 

  • Iran Road Maintenance and Transportation Organization (RMTO). (2022) “Road Statistical Yearbook”, Available:

http://www.rmto.ir/Pages/Home.aspx

 

  • Khoda Bakhshi, A., and Ahmed, M. M. (2021) “Coping with endogeneity and unobserved heterogeneity in real-time clustering critical crash occurrences nested within weather and road surface conditions”, International journal of injury control and safety promotion, 28(2), 208-221.

https://doi.org/10.1080/17457300.2021.1907595

 

  • Kim, K., Brunner, I. M., and Yamashita, E. Y. (2006) “Influence of land use, population, employment, and economic activity on accidents”, Transportation research record, 1953(1), 56-64.

https://doi.org/10.1177/0361198106195300107

 

  • Lord, D., Washington, S.P., and Ivan, J.N. (2007) “Further notes on the application of zero inflated models in highway safety”, Accident Analysis and Prevention, 39 (1), 53–57.

https://doi: 10.1016/j.aap.2006.06.004

 

  • Lord, D., and Mannering, F. (2010) “The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives”, Transportation research part A: policy and practice, 44(5), 291-305.

https://doi.org/10.1016/j.tra.2010.02.001

 

  • Ma, Z., Zhang, H., Steven, I., Chien, J., Wang, J., and Dong, C. (2017) “Predicting expressway crash frequency using a random effect negative binomial model: A case study in China”, Accident Analysis & Prevention, 98, 214-222. https://doi.org/10.1016/j.aap.2016.10.012

 

  • Mahmud, S. S., Ferreira, L., Hoque, M. S., and Tavassoli, A. (2019) “Micro-level safety risk assessment model for a two-lane heterogeneous traffic environment in a developing country: A comparative crash probability modeling approach”, Journal of Safety Research, 69, 125-134.

https://doi.org/10.1016/j.jsr.2019.03.008

 

  • Malyshkina, N. V., and Mannering, F. L. (2010) “Zero-state Markov switching count-data models: An empirical assessment”, Accident Analysis & Prevention, 42(1), 122-130.

https://doi.org/ 10.1016/j.aap.2009.07.012

 

  • Mannering, F. L., and Bhat, C. R. (2014) “Analytic methods in accident research: Methodological frontier and future directions”, Analytic methods in accident research, 1, 1-22.

https://doi.org/10.1016/j.amar.2013.09.001

 

  • Mannering, F. L., Shankar, V., and Bhat, C. R. (2016) “Unobserved heterogeneity and the statistical analysis of highway accident data”, Analytic methods in accident research, 11, 1-16.

https://doi.org/10.1016/j.amar.2016.04.001

 

  • Mannering, F., Bhat, C. R., Shankar, V., and Abdel-Aty, M. (2020) “Big data, traditional data and the tradeoffs between prediction and causality in highway-safety analysis”, Analytic methods in accident research, 25, 100113.

https://doi.org/10.1016/j.amar.2020.100113

 

  • Mohammadnazar, A., Arvin, R., and Khattak, A. J. (2021) “Classifying travelers' driving style using basic safety messages generated by connected vehicles: Application of unsupervised machine learning”, Transportation research part C: emerging technologies, 122, 102917.

https://doi.org/10.1016/j.trc.2020.102917

 

  • Mohammadnazar, A., Mahdinia, I., Ahmad, N., Khattak, A. J., and Liu, J. (2021a) “Understanding how relationships between crash frequency and correlates vary for multilane rural highways: Estimating geographically and temporally weighted regression models”, Accident Analysis & Prevention, 157, 106146.

https://doi.org/10.1016/j.aap.2021.106146

 

  • 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, 16(2), pp.113-119.

https://doi.org/10.1080/10095020.2013.766396

 

  • Moomen, M., Rezapour, M., Raja, M., and Ksaibati, K. (2020) “Predicting downgrade crash frequency with the random parameters negative binomial model: Insights into the impacts of geometric variables on downgrade crashes in Wyoming”, IATSS research, 44(2), 94-102. https://doi.org/10.1016/j.iatssr.2019.09.002

 

  • Mousavi, S. M., Marzoughi, H., Parr, S. A., Wolshon, B., and Pande, A. (2019) “A mixed crash frequency estimation model for interrupted flow segments”, In International Conference on Transportation and Development 2019: Smarter and Safer Mobility and Cities (pp. 72-83). Reston, VA: American Society of Civil Engineers. Available:

https://ascelibrary.org/doi/10.1061/9780784482575.008

 

  • Naik, B., Tung, L. W., Zhao, S., and Khattak, A. J. (2016) “Weather impacts on single-vehicle truck crash injury severity”, Journal of safety research, 58, 57-65.

https://doi.org/10.1016/j.jsr.2016.06.005

 

  • Nassiri, H., Najafi, P., and Mohamadian Amiri, A. (2014) “Prediction of roadway accident frequencies: Count regressions versus machine learning models”, Scientia Iranica, 21(2), 263-275. Available:

http://scientiairanica.sharif.edu/article_1630.html

 

  • Park, J., Abdel-Aty, M., and Wang, J. H. (2017) “ime series trends of the safety effects of pavement resurfacing”, Accident Analysis & Prevention, 101, 78-86.

https://doi.org/10.1016/j.aap.2017.02.006

 

  • Rusli, R., Haque, M. M., King, M., and Voon, W. S. (2017) “Single-vehicle crashes along rural mountainous highways in Malaysia: an application of random parameters negative binomial model”, Accident Analysis & Prevention, 102, 153-164.

https://doi.org/10.1016/j.aap.2017.03.002

 

  • Russo, B. J., Savolainen, P. T., Schneider IV, W. H., and Anastasopoulos, P. C. (2014) “Comparison of factors affecting injury severity in angle collisions by fault status using a random-parameters bivariate ordered probit model”, Analytic methods in accident research, 2, 21-29.

https://doi.org/10.1016/j.amar.2014.03.001

 

  • Saeed, T. U., Hall, T., Baroud, H., and Volovski, M. J. (2019) “Analyzing road crash frequencies with uncorrelated and correlated random-parameters count models: An empirical assessment of multilane highways”, Analytic methods in accident research, 23, 100101.

https://doi.org/10.1016/j.amar.2019.100101

 

  • 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, 23(6), pp.191-207.

https://doi.org/10.4186/ej.2019.23.6.191

 

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

https://doi.org/10.1080/19427867.2016.1138605

 

  • Shamanian Esfahani, H., Afandizadeh, S., Naderan, A. (2022) “Investigating the Effect of Marginal Areas around the Cities on Rural Road Accidents in Iran Using Linear and Logistic Regression Approaches”, International Journal of Transportation Engineering, 9(3), 693-711. doi: 10.22119/ijte.2021.284119.1567

 

  • Shankar, V., Mannering, F., & Barfield, W. (1995). Effect of roadway geometrics and environmental factors on rural freeway accident frequencies. Accident Analysis & Prevention, 27(3), 371-389.

 

  • Shaon, M. R. R., Qin, X., Shirazi, M., Lord, D., and Geedipally, S. R. (2018) “Developing a Random parameters negative binomial-Lindley Model to analyze highly over-dispersed crash count data”, Analytic methods in accident research, 18, 33-44.

https://doi.org/10.1016/j.amar.2018.04.002

 

  • 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.

 

  • H. (2012) “Greene, Econometric Analysis”, Seventh, Prentice Hall, Boston, MA, 2012.

 

  • Washington, S., Karlaftis, M., Mannering, F., and Anastasopoulos, P. (2020) “Statistical and econometric methods for transportation data analysis”, Chapman and Hall/CRC.

https://doi.org/10.1201/9780429244018

 

  • Wen, X., Xie, Y., Wu, L., and Jiang, L. (2021) “Quantifying and comparing the effects of key risk factors on various types of roadway segment crashes with LightGBM and SHAP”, Accident Analysis & Prevention, 159, 106261.

https://doi.org/10.1016/j.aap.2021.106261

 

  • World Health Organization (WHO). 2018 “Global Status Report on Road safety”, Available:

https://www.who.int/publications/i/item/global

 

  • status-report-on-road-safety-2018.

 

  • Wu, P., Song, L., and Meng, X. (2021) “Influence of built environment and roadway characteristics on the frequency of vehicle crashes caused by driver inattention: a comparison between rural roads and urban roads”, Journal of safety research, 79, 199-210.

https://doi.org/10.1016/j.jsr.2021.09.001

 

  • Yan, Y., Zhang, Y., Yang, X., Hu, J., Tang, J., and Guo, Z. (2020) “Crash prediction based on random effect negative binomial model considering data heterogeneity”, Physica A: Statistical Mechanics and Its Applications, 547, 123858.

https://doi.org/10.1016/j.physa.2019.123858

  • Yu, R., Wang, Y., Quddus, M., and Li, J. (2019) “A marginalized random effects hurdle negative binomial model for analyzing refined-scale crash frequency data”, Analytic methods in accident research, 22, 100092.

https://doi.org/10.1016/j.amar.2019.100092

 

  • Zou, Y., and Tarko, A. P. (2016) “An insight into the performance of road barriers− redistribution of barrier-relevant crashes”, Accident Analysis & Prevention, 96, 152-161.

https://doi.org/10.1016/j.aap.2016.07.022

International Journal of Transportation Engineering
Volume 12, Issue 2 - Serial Number 46
Autumn 2024
Pages 1785-1802