Factors Influencing Crash Frequency on Colombian Rural Roads

  • Andrea Arévalo-Támara Santo Tomás University, Bogotá, Colombia
  • Mauricio Orozco-Fontalvo Universidad Militar Nueva Granada, Bogotá, Colombia
  • Víctor Cantillo Universidad del Norte, Barranquilla, Colombia
Keywords: traffic crashes, crash frequency, Colombian rural roads, Negative Binomial model, Zero-inflated model, generalized linear mixed model, traffic safety


Traffic crashes in Colombia have become a public health problem causing about 7,000 deaths and 45,000 severe injuries per year. Around 40% of these events occur on rural roads, taking note that the vulnerable users (pedestrians, motorcyclists, cyclists) account for the largest percentage of the victims. The objective of this research is to identify the factors that influence the frequency of crashes, including the singular orography of the country. For this purpose, we estimated Negative Binomial (Poisson-gamma) regression, Zero-inflated model, and generalized the linear mixed model, thus developing a comparative analysis of results in the Colombian context. The data used in the study came from the official sources regarding records about crashes with consequences; that is, with the occurrence of fatalities or injuries on the Colombian roads. For collecting the highway characteristics, an in-field inventory was conducted, gathering information about both infrastructure and operational parameters in more than three thousand kilometres of the national network. The events were geo-referenced, with registries of vehicles, involved victims, and their condition. The results suggest that highways in flat terrain have higher crash frequency than highways in rolling or mountainous terrain. Besides, the presence of pedestrians, the existence of a median and the density of intersections per kilometre also increase the probability of crashes. Meanwhile, roads with shoulders and wide lanes have lower crash frequency. Specific interventions in the infrastructure and control for reducing crashes risk attending the modelling results have been suggested.

Author Biographies

Andrea Arévalo-Támara, Santo Tomás University, Bogotá, Colombia

Andrea Arévalo-Támara received the Bs. Eng. degree in civil engineering in 2011 from Universidad Fransico de Paula Santander, Colombia; the MSc in Civil engineering in 2015 from Universidad del Norte, Colombia. She is an assistant professor at Santo Tomás University, Colombia and has developed several road safety projects around the country. Her research interests include: traffic accidents, road safety policies, accident modeling and sustainable transport.

Mauricio Orozco-Fontalvo, Universidad Militar Nueva Granada, Bogotá, Colombia

Mauricio Orozco received his BS Engineering in civil engineering in 2013 and MSc in, Civil engineering in 2015 from the Universidad del Norte, Colombia. He is an associate professor at the Universidad Militar Nueva Granada. He has also been working in other topics such as transportation planning and road safety.

Víctor Cantillo, Universidad del Norte, Barranquilla, Colombia

V. Cantillo, received the Bs. Eng. degree in Civil Engineering in 1987, from Universidad del Norte, Colombia; the MSc. in Traffic and Transport Engineering in 1990, from Universidad del Cauca, Colombia and the Ph.D. degree in Engineering Sciences (Transport), in 2004, from Pontificia Universidad Católica de Chile, Chile. He is an associate professor at Universidad del Norte, Colombia since 1990. He is the head of the Transport Research Group - TRANVIA. His research interests include: transport planning, transport economics, transport modeling, and logistics. He has also been working on other topics such as education in engineering, econometrics and construction materials.


Batrakova A, Gredasova O. Influence of Road Conditions on Traffic Safety. Procedia Engineering. 2016;134: 196-204. Available from: doi:10.1016/j.proeng.2016.01.060

Yakar F. Identification of Accident-Prone Road Sections by Using Relative Frequency Method. Promet - Traffic

&Transportation. 2015;27: 539-47. Available from: doi:10.7307/ptt.v27i6.1609

World Economic Forum. Global Competitiveness Report; 2018.

Medicina Legal. Forensis; 2017.

Guerrero Barbosa TE, Espinel-Bayona Y, Palacio-Sánchez D. Effects of the Attributes Associated with Roadway Geometry, Traffic Volumes and Speeds on the Incidence of Accidents in a Mid-Size City. Ingenieria y Universidad. 2015;19: 105. Available from: doi:10.11144/Javeriana.iyu19-2.eaar

Cantillo V, Garcés P, Márquez L. Factors influencing the occurrence of traffic accidents in urban roads: A combined GIS-Empirical Bayesian approach. DYNA. 2016;83: 21-8. Available from: doi:10.15446/dyna.v83n195.47229

Xi J, Zhao Z, Li W, Wang Q. A Traffic Accident Causation Analysis Method Based on AHP-Apriori. Procedia

Engineering. 2016;137: 680-7. Available from: doi:10.1016/j.proeng.2016.01.305

Ackaah W, Salifu M. Crash prediction model for two-lane rural highways in the Ashanti region of Ghana. IATSS Research. 2011;35: 34-40. Available from: doi:10.1016/j.iatssr.2011.02.001

Choi J, Kim S, Heo T-Y, Lee J. Safety effects of highway terrain types in vehicle crash model of major rural roads. KSCE Journal of Civil Engineering. 2011;15: 405-12. Available from: doi:10.1007/s12205-011-1124-x

Lord D. Modeling motor vehicle crashes using Poisson-gamma models: Examining the effects of low sample mean values and small sample size on the estimation of the fixed dispersion parameter. Accident Analysis & Prevention. 2006;38: 751-66. Available from: doi:10.1016/j.aap.2006.02.001

Zou Y, Ash JE, Park B-J, Lord D, Wu L. Empirical Bayes estimates of finite mixture of negative binomial regression models and its application to highway safety. Journal of Applied Statistics. 2018;45: 1652-69. Available from: doi:10.1080/02664763.2017.1389863

Lord D, Mannering F. The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives. Transportation Research Part A: Policy and Practice. 2010;44: 291-305. Available from: doi:10.1016/j.tra.2010.02.001

Lee J, Mannering F. Impact of roadside features on the frequency and severity of run-off-roadway accidents: an empirical analysis. Accident Analysis & Prevention. 2002;34: 149-61. Available from: doi:10.1016/S0001-4575(01)00009-4

Lord D, Washington SP, Ivan JN. Poisson, Poisson-gamma and zero-inflated regression models of motor vehicle crashes: Balancing statistical fit and theory. Accident Analysis & Prevention. 2005;37: 35-46. Available from: doi:10.1016/j.aap.2004.02.004

Miaou S-P. The relationship between truck accidents and geometric design of road sections: Poisson versus negative binomial regressions. Accident Analysis & Prevention. 1994;26: 471-82. Available from: doi:10.1016/0001-4575(94)90038-8

Lord D, Washington S, Ivan JN. Further notes on the application of zero-inflated models in highway safety. Accident Analysis & Prevention. 2007;39: 53-7. Available from: doi:10.1016/j.aap.2006.06.004

Miranda-Moreno LF, Fu L. A Comparative Study of Alternative Model Structures and Criteria for Ranking Locations for Safety Improvements. Networks and Spatial Economics. 2006;6: 97-110. Available from: doi:10.1007/s11067-006-7695-2

Aguero-Valverde J. Full Bayes Poisson gamma, Poisson lognormal, and zero inflated random effects models: Comparing the precision of crash frequency estimates. Accident Analysis & Prevention. 2013;50: 289-97. Available from: doi:10.1016/j.aap.2012.04.019

Shirazi M, Lord D, Dhavala SS, Geedipally SR. A semiparametric negative binomial generalized linear model for modeling over-dispersed count data with a heavy tail: Characteristics and applications to crash data. Accident Analysis & Prevention. 2016;91: 10-8. Available from: doi:10.1016/j.aap.2016.02.020

Geedipally SR, Lord D, Dhavala SS. The negative binomial-Lindley generalized linear model: Characteristics and application using crash data. Accident Analysis & Prevention. 2012;45: 258-65. Available from: doi:10.1016/j.aap.2011.07.012

Mussone L, Bassani M, Masci P. Analysis of factors affecting the severity of crashes in urban road intersections. Accident Analysis & Prevention. 2017;103: 112-22. Available from: doi:10.1016/j.aap.2017.04.007

Haule HJ, Sando T, Kitali AE, Richardson R. Investigating proximity of crash locations to aging pedestrian residences. Accident Analysis & Prevention. 2019;122: 215-25. Available from: doi:10.1016/j.aap.2018.10.008

Barffour M, Gupta S, Gururaj G, Hyder AA. Evidence-Based Road Safety Practice in India: Assessment of the Adequacy of Publicly Available Data in Meeting Requirements for Comprehensive Road Safety Data Systems. Traffic Injury Prevention. 2012;13(sup.1): 17-23. Available from: doi:10.1080/15389588.2011.636780

Cafiso S, Di Graziano A, Di Silvestro G, La Cava G, Persaud B. Development of comprehensive accident models for two-lane rural highways using exposure, geometry, consistency and context variables. Accident Analysis & Prevention. 2010;42: 1072-9. Available from: doi:10.1016/j.aap.2009.12.015

Cafiso S, D’Agostino C, Persaud B. Investigating the influence of segmentation in estimating safety performance functions for roadway sections. Journal of Traffic and Transportation Engineering (English Edition). 2018;5: 129-36. Available from: doi:10.1016/j.jtte.2017.10.001

Miaou S-P, Lum H. Modeling vehicle accidents and highway geometric design relationships. Accident Analysis & Prevention. 1993;25: 689-709. Available from: doi:10.1016/0001-4575(93)90034-T

Wu L, Lord D, Zou Y. Validation of Crash Modification Factors Derived from Cross-Sectional Studies with Regression Models. Transportation Research Record: Journal of the Transportation Research Board. 2015;2514: 88-96. Available from: doi:10.3141/2514-10

ChikkaKrishna NK, Parida M, Jain SS. Identifying safety factors associated with crash frequency and severity on nonurban four-lane highway stretch in India. Journal of Transportation Safety & Security. 2017;9: 6-32. Available from: doi:10.1080/19439962.2016.1150927

Zeng Q, Huang H. Bayesian spatial joint modeling of traffic crashes on an urban road network. Accident Analysis & Prevention. 2014;67: 105-12. Available from: doi:10.1016/j.aap.2014.02.018

Washington S, Karlaftis MG, Mannering FL. Statistical and econometric methods for transportation data analysis. 2nd ed. Boca Raton, FL: CRC Press; 2011.

Geedipally SR. Examining the application of conway-maxwellpoisson models for analyzing traffic crash data. Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements for the degree of Doctor of Philosophy; 2008. 24 p.

Lambert. Zero-Inflated Poisson Regression with an Application to Defects in Manufacturing. Technometrics. 1992;34(1): 1-14. Available from: doi:10.1080/00401706.1992.10485228

Hosseinpour M, Yahaya AS, Sadullah AF. Exploring the effects of roadway characteristics on the frequency and severity of head-on crashes: Case studies from Malaysian Federal Roads. Accident Analysis & Prevention. 2014;62: 209-22. Available from: doi:10.1016/j.aap.2013.10.001

Kumara SSP, Chin HC. Modeling Accident Occurrence at Signalized Tee Intersections with Special Emphasis on Excess Zeros. Traffic Injury Prevention. 2003;4: 53-7. Available from: doi:10.1080/15389580309852

Lee J, Mannering F. Impact of roadside features on the frequency and severity of run-off-roadway accidents: an empirical analysis. Accident Analysis & Prevention. 2002;34: 149-61. Available from: doi:10.1016/S0001-4575(01)00009-4

Qin X, Ivan JN, Ravishanker N. Selecting exposure measures in crash rate prediction for two-lane highway segments. Accident Analysis & Prevention. 2004;36: 183-91. Available from: doi:10.1016/S0001-4575(02)00148-3

Shankar V, Milton J, Mannering F. Modeling accident frequencies as zero-altered probability processes: An empirical inquiry. Accident Analysis & Prevention. 1997;29: 829-37. Available from: doi:10.1016/S0001-4575(97)00052-3

Raihan MA, Alluri P, Wu W, Gan A. Estimation of bicycle crash modification factors (CMFs) on urban facilities using zero inflated negative binomial models. Accident Analysis & Prevention. 2019;123: 303-13. Available from: doi:10.1016/j.aap.2018.12.009

McCullagh P, Nelder JA. Generalized linear models. 2nd ed. Boca Raton: Chapman & Hall/CRC; 1998.

Casella G, Berger RL. Statistical inference. 2nd ed. Australia, Pacific Grove, CA: Thomson Learning; 2002.

Sasidharan L, Menéndez M. Partial proportional odds model—An alternate choice for analyzing pedestrian crash injury severities. Accident Analysis & Prevention. 2014;72: 330-40. Available from: doi:10.1016/j.aap.2014.07.025

How to Cite
Arévalo-Támara A, Orozco-Fontalvo M, Cantillo V. Factors Influencing Crash Frequency on Colombian Rural Roads. Promet - Traffic&Transportation. 2020;32(4):449-60. DOI: 10.7307/ptt.v32i4.3385