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Classification of Driver Behavior in Horizontal Curves of Two-Lane Rural Roads


The components of a road directly affect its safety and, by analyzing the consistency of the geometric design, this condition can be assessed. Vehicle speed is a factor in assessing geometric design consistency, and it is considered constant throughout the driver’s transition between the tangents of horizontal curves. However, in most cases, drivers modify the speed depending on the vehicle’s position before, during, and after the curve. Therefore, a speed profile allows one to describe this behavior better and study the relationship between speed and the curve elements. Previous literature shows that the rural environmental influences the speed of road users.

Additionally, horizontal curve negotiations are linked to a high number of accidents, most of which are caused by driving errors and aggressive/risky driving style. This study aims to determine the optimal number of groups of curve speed profiles that are strongly influenced by the horizontal alignment elements geometry. This research introduces a cluster-based analysis approach to identify speed profiles from field data in two steps; first, the optimal number of speed profiles is determined through the evaluation of several techniques and, second, speed profiles are classified using the k-means method with different sets of data. Free-flow velocity data observed travelling in both directions within 38 horizontal curves on two-lane rural roads in Puerto Rico was used for validation. The validation of the proposed methodology is carried out by directly comparing the number of behaviors observed in users with the geometric and operational characteristics of the curves. The results show that by employing three types of driving styles referred to as Aggressive, Moderate, and Cautious and, adding two inferred variables that consider the time, the driver stays near or above the speed limit and changes acceleration during their movement through the curve, allowing a clearer and more uniform categorization of speed profiles. Furthermore it is observed that the number of grouped profiles have a high dependence on the values taken by the variables of the curve design as expected due to their influence on the perception of safety by drivers.


cluster, curves, geometric design, rural road, speed profile, two-lanes road


Author Biography

Cristian-David Rosas-López

Roles: Formal Analysis, Investigation, Validation, Writing-original draft.

Carlos-Andres Gaviria-Mendoza

Roles: Methodology, conceptualization, Writing-review & editing.

Carlos-Anibal Calero-Valenzuela

Roles: Methodology, conceptualization, Writing-review & editing.


  1. World Health Organization, Global status report on road safety.
  2. G. M. Gibreel, S. M. Easa, A. Member, A. Y. Hassan, I. A. El-Dimeery, “State of the art of highway geometric design consistency,” Journal of Transportation Engineering, vol. 125, no. 4, pp. 305–313, 1999. DOI:
  3. V. Malaghan, D. S. Pawar, H. Dia, “Modeling Operating Speed Using Continuous Speed Profiles on Two-Lane Rural Highways in India,” Journal of Transportation Engineering: Part A Systems, vol. 146, no. 11, e04020124, 2020. DOI:
  4. D. Llopis-Castelló, B. González-Hernández, A. M. Pérez-Zuriaga, A. García, “Speed Prediction Models for Trucks on Horizontal Curves of Two-Lane Rural Roads,” Transportation Research Record, vol. 2672, no. 17, pp. 72–82, May 2018. DOI:
  5. M. L. Alonso Pla, “La integración del factor humano en el ámbito técnico de la gestión de las carreteras y la seguridad vial: Un enfoque investigativo,” Doctoral Thesis, Intituto Universitario de Investigación en Tráfico y Seguridad vial, Universdad de Valencia, Spain, 2016.
  6. C. A. Calero Valenzuela, “Metodología para la Evaluación de la Consistencia de Diseño de Carreteras Rurales de Dos Carriles,” Doctoral Thesis, Universidad de Puerto Rico, Puerto Rico, 2015.
  7. United Nations Road Safety, “Global plan for the Decade of Action for Road Safety 2011–2020,” Geneva WHO, p. 25, 2011
  8. AASHTO, A Policy on Geometric Design of Highways and Streets, 7th Edition. 2018.
  9. D. Chu, Z. Deng, Y. He, C. Wu, C. Sun, Z. Lu, “Curve speed model for driver assistance based on driving style classification,” IET Intelligent Transport Systems., vol. 11, no. 8, pp. 501–510, 2017. DOI:
  10. A. Laureshyn, K. Åström, K. Brundell-Freij, “From speed profile data to analysis of behaviour,” IATSS Reseach, vol. 33, no. 2, pp. 88–98, 2009. DOI:
  11. A. M. Bermúdez Arbona, “Velocidad y diseño geométrico: factores para identificar curvas potencialmente peligrosas en carreteras rurales de dos carriles,” Doctoral Thesis, Universidad de Puerto Rico, Puerto Rico, 2018.
  12. M. Dolatalizadeh, A. M. Boroujerdian, S. Abrishami Seyed, Ehsan, “Analysis of Speed Profiles at an Unsignalised Intersection for Left Turning Vehicles,” IJTE, vol. 8, no. 2, pp. 149–163, 2020.
  13. M. P. Bobermin, M. M. Silva, S. Ferreira, “Driving simulators to evaluate road geometric design effects on driver behaviour: A systematic review,” Accident Analysis and Prevention, vol. 150, e105923, 2021. DOI:
  14. M. Zolali, B. Mirbaha, M. Layegh, H. R. Behnood, “A Behavioral Model of Drivers’ Mean Speed Influenced by Weather Conditions, Road Geometry, and Driver Characteristics Using a Driving Simulator Study,” Advances in Civil Engineering, vol. 2021, e18, 2021. DOI:
  15. T. Choudhari, A. Maji, “Socio-demographic and experience factors affecting drivers’ runoff risk along horizontal curves of two-lane rural highway,” Journal of Safety Research, vol. 71, pp. 1–11, Dec. 2019. DOI:
  16. A. Keklikoglou, C. D. Fitzpatrick, M. A. Knodler, “Investigation of Time and Speed Perception using a Driving Simulator,” Transportation Research Record, vol. 2672, no. 37, pp. 132–140, May 2018. DOI:
  17. T. Liu, J. Xu, “The influence rule of highway curve radius on speed perception and operating speed,” IOP Conference Series Earth Environment Science, vol. 371, no. 2, e022080, Dec. 2019. DOI:
  18. J. Vos, H. Farah, M. Hagenzieker, “Speed behaviour upon approaching freeway curves,” Accident Analysis and Prevention, vol. 159, e106276, Sep. 2021. DOI:
  19. E. Papadimitriou, S. Mavromatis, D. Pavlou, G. Yannis, “Assessment of Speeding Profiles and Safety Margins from Tangent to Curve by means of Driving Simulation,” in Proceedings of the Road Safety & Simulation International Conference, 2017, pp. 17–19
  20. A. Jahangiri, V. J. Berardi, S. G. MacHiani, “Application of Real Field Connected Vehicle Data for Aggressive Driving Identification on Horizontal Curves,” IEEE Transactions on Intelligent Transportation Systems, vol. 19, no. 7, pp. 2316–2324, Jul. 2018. https// DOI:
  21. A. Jahangiri, S. G. Machiani, V. Balali, Big Data Exploration to Examine Aggressive Driving Behavior in the Era of Smart Cities. Auerbach Publications, 2019 DOI:
  22. Google LLC, Google Earth Pro, 2021.
  23. MetroCount, MetroCount, 2021.
  24. K. A. Rodríguez Polo, S. Henao Pérez, “Safety performance functions in Dedicated Bus Lane of BRT on Caracas Avenue Corridor at Bogotá city,” Inge Cuc, vol. 15, no. 2, pp. 66–77, 2019. DOI:
  25. N. Rincón Numpaque, L. Á. Moreno Anselmi, K. A. Rodriguez Polo, C. A. Gaviria Mendoza, “Alternatives to improve operational traffic in roundabouts using microsimulation,” Respuestas, vol. 25, no. 2, pp. 26–36, 2020. DOI:
  26. DataCamp, Run.clustering: Clustering the data, 2021.
  27. J. Devore, Probabilidad y estadística para engeniería y ciencias, vol. 7th Ed. 2008
  28. H. Fu, F. Deng, Y. Shao, Y. Liu, J. Zhang, “Road Centreline Extraction of High ‑ Resolution Remote Sensing Image with Improved Beamlet Transform and K ‑ Means Clustering,” Arabian Journal for Science and Engineering, vol. 46, no. 4, pp. 4153–4162, 2021. DOI:
  29. R. A. Robayo-Salazar, R. Mejía de Gutiérrez, A. J. Mulford-Carvajal, “Production of building elements based on alkali-activated red clay brick waste”, Revista Facultad de Ingenería, vol. 25, no. 43, pp. 21–30, 2016. DOI:


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