Skip to main content

Advertisement

SpringerLink
Log in

Effects of Pavement Friction and Geometry on Traffic Crash Frequencies: A Case Study in Wyoming

  • Original Research Paper
  • Published:
International Journal of Pavement Research and Technology Aims and scope Submit manuscript

Abstract

Crash prediction models, also known as safety performance functions (SPFs), were developed in this study for different functional classes of roadways. SPFs were developed for total crashes and equivalent property-damage-only (EPDO) crashes to investigate the effects of pavement friction, roadway geometry, road surface conditions, and vehicle body types on traffic crash frequencies. Crash data, roadway geometric design data, and friction data obtained from the Wyoming Department of Transportation (WYDOT) were processed to develop the SPFs. The results of the SPFs revealed that lower friction numbers not only increased the occurrences of total crashes but also increased the occurrences of EPDO crashes. This suggests that maintaining adequate friction levels reduces both total and EPDO crashes. Several geometric characteristics of roadways were found to be significant from the results of the SPFs. Upgrades and downgrades were found to increase EPDO crashes in most facilities. Tangent segments were found to reduce total crashes but increase EPDO crashes. When road surface condition was considered, it was found that dry and wet road surfaces were positively associated with both total and EPDO crashes. The average annual daily traffic (AADT) volume, an exposure measure, was positively associated with the frequencies of total crashes and EPDO crashes, assuming that all else was unchanged. The results from the SPFs are of great value and will be beneficial in identify locations that are at risk of experiencing crashes because of low pavement friction numbers.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Najafi, S., Flintsch, G., & Medina, A. (2017). Linking roadway crashes and tire-pavement friction: A case study. International Journal of Pavement Engineering. https://doi.org/10.1080/10298436.2015.1039005

    Article  Google Scholar 

  2. McCarthy, R., Flintsch, G., Katicha, S., McGhee, K., & Medina-Flintsch, A. (2016). New approach for managing pavement friction and reducing road crashes. Transportation Research Record: Journal of the Transportation Research Board, 2591(1), 23–32. https://doi.org/10.3141/2591-04

    Article  Google Scholar 

  3. Kouchaki, S., Roshani, H., Prozzi, J., Garcia, N., & Hernandez, J. (2018). Field investigation of relationship between pavement surface texture and friction. Transportation Research Record: Journal of the Transportation Research Board, 2672(40), 395–407. https://doi.org/10.1177/0361198118777384

    Article  Google Scholar 

  4. D’Apuzzo, M., Evangelisti, A., & Nicolosi, V. (2020). An exploratory step for a general unified approach to labelling of road surface and tyre wet friction. Accident Analysis & Prevention, 138, 105462. https://doi.org/10.1016/j.aap.2020.105462

    Article  Google Scholar 

  5. Hall, J., Smith, K., Titus-Glover, L., Wambold, J., Yager, T., & Rado, Z. (2009). National Cooperative Highway Research Program Web-Only Document 108: Guide for Pavement Friction. National Cooperative Highway Research Program, Transportation Research Board, National Academies of Sciences, Engineering, and Medicine, Washington, D.C.

  6. Cafiso, S., Montella, A., D’Agostino, C., Mauriello, F., & Galante, F. (2021). Crash modification functions for pavement surface condition and geometric design indicators. Accident Analysis & Prevention, 149, 105887. https://doi.org/10.1016/j.aap.2020.105887

    Article  Google Scholar 

  7. Lyon, C., & Persaud, B. (2008). Safety effects of targeted program to improve skid resistance. Transportation Research Record: Journal of the Transportation Research Board., 2068(1), 135–140. https://doi.org/10.3141/2068-15

    Article  Google Scholar 

  8. Najafi, S., Flintsch, G., & Medina, A. (2014). Case study on the evaluation of the effect of tire–pavement friction on the rate of roadway crashes. In Presented at 93rd annual meeting of the transportation research board (pp. 14–5617). Washington, D.C.

    Google Scholar 

  9. Justo-Silva, R., & Ferreira, A. (2018). Accident prediction models considering pavements quality. In: Proceedings of the 5th International Conference on Road and Rail Infrastructure (CETRA), Zadar, CROATIA 2018 May pp. 1089–1095.

  10. Mataei, B., Zakeri, H., Zahedi, M., & Nejad, F. M. (2016). Pavement friction and skid resistance measurement methods: A literature review. Open Journal of Civil Engineering., 6(4), 537–565.

    Article  Google Scholar 

  11. Chowdhury, A., Kassem, E., Aldagari, S., & Masad, E. (2017). Validation of asphalt mixture pavement skid prediction model and development of skid prediction model for surface treatments. No. FHWA/TX-17/0–6746–01–1. Texas. Dept. of Transportation. Research and Technology Implementation Office.

  12. McCarthy, R., Flintsch, G., Katicha, S., Izeppi, E., & Guo, F. (2021). Determining investigatory levels of friction with crash modelling. International Journal of Pavement Engineering. https://doi.org/10.1080/10298436.2021.1888089

    Article  Google Scholar 

  13. Srinivasan, R., & Bauer, K. (2013). Safety performance function development guide: developing jurisdiction-specific SPFs. Report Number FHWA-SA-14–005. Federal Highway Administration, U.S. Department of Transportation, Washington, D.C.

  14. Rizenbergs, R., Burchett, J., & Napier, C. (1972). Skid Resistance of pavements. Report Number 929. Kentucky Transportation Center, University of Kentucky.

  15. Giles, C., Sabey, B., & Cardew, K. (1965). Development and performance of the portable skid-resistance tester. In: Symposium on skid resistance. West Conshohocken, Pennsylvania: American Society for Testing and Materials International.

    Google Scholar 

  16. Cairney, P. (1997). Skid resistance and crashes—A review of the literature. Australian Road Research Board Transport Research Ltd.

    Google Scholar 

  17. Gothies, M. (1996). Relationship between surface characteristics and accidents. In: Presented at 3rd international symposium on pavement surface characteristics, Christchurch, New Zealand (pp. 271–282).

    Google Scholar 

  18. Kamel, N., & Gartshore, T. (1982). Ontario’s wet pavement accident reduction program. In: Pavement surface characteristics and materials. American Society for Testing and Materials International.

    Google Scholar 

  19. McCullough, B., & Hankins, K. (1966). Skid resistance guidelines for surface improvements on texas highways. Transportation Research Record: Journal of the Transportation Research Board, 131, 204–217.

    Google Scholar 

  20. Miller, M., & Johnson, H. (1973). Effects of resistance to skidding on accidents: surface dressing on an elevated section of the M4 motorway. Report Number LR542. Transport and Road Research Laboratory, Department of the Environment, Crawthorne, U.K.

  21. McLean, J. (1995). The relationship between pavement conditions and road safety. Presented at Load-Pavement Interaction Workshop.

    Google Scholar 

  22. Noyce, D., Bahia, H., Yambo, J., Chapman, J., Bill, A. (2007). Incorporating Road Safety into Pavement Management: Maximizing Surface Friction for Road Safety Improvements. Report Number MRUTC 04-04. Wisconsin Department of Transportation, Madison, Wisconsin.

  23. Hall, J., Smith, K., Littleton, P. (2009). National Cooperative Highway Research Program Report 634: Texturing of Concrete Pavements. National Cooperative Highway Research Program, Transportation Research Board, National Academies of Sciences, Engineering, and Medicine, Washington, D.C.

  24. Mayora, J., & Piña, R. (2009). An Assessment of the skid resistance effect on traffic safety under wet pavement conditions. Accident Analysis & Prevention, 41(4), 881–886. https://doi.org/10.1016/j.aap.2009.05.004

    Article  Google Scholar 

  25. Craus, J., Livneh, M., & Ishai, I. (1991). Effect of pavement and shoulder condition on highway accidents. Transportation Research Record: Journal of the Transportation Research Board, 1318, 51–57.

    Google Scholar 

  26. Gandhi, M., Colucci, B., & Gandhi, S. (1991). Polishing of aggregates and wet-weather accident rates for flexible pavements. Transportation Research Record: Journal of the Transportation Research Board, 1300, 71–79.

    Google Scholar 

  27. Kuttesch, J. (2004). Quantifying the Relationship between Skid Resistance and Wet Weather Accidents for Virginia Data. M.Sc. Thesis. Virginia Polytechnic Institute and State University. Blacksburg, Virginia.

  28. Organisation for Economic Cooperation and Development. (1984). Road surface characteristics: Their interaction and their optimisation. Organisation for Economic Cooperation and Development, Road Research Group.

    Google Scholar 

  29. Schulze, K., Gerbaldi, A., & Chavet, J. (1977). Skidding accidents, friction numbers, and the legal aspects involved report of the permanent international association of road congresses technical committee on slipperiness and evenness. Transportation Research Record: Journal of the Transportation Research Board., 623, 1–10.

    Google Scholar 

  30. Wallman, C.-G., & Åström, H. (2001). Friction measurement methods and the correlation between road friction and traffic safety: A literature review. Linkoping: Swedish National Road and Transport Research Institute.

    Google Scholar 

  31. Xiao, J., Kulakowski, B., & EI-Gindy, M. (2000). Prediction of risk of wet-pavement accidents: fuzzy logic model. Transportation Research Record: Journal of the Transportation Research Board. https://doi.org/10.3141/1717-05

    Article  Google Scholar 

  32. Ivan, J., Ravishanker, N., Jackson, E., Aronov, B., & Guo, S. (2012). A statistical analysis of the effect of wet-pavement friction on highway traffic safety. Journal of Transportation Safety & Security, 4(2), 116–136. https://doi.org/10.1080/19439962.2011.620218

    Article  Google Scholar 

  33. Geedipally, S., Pratt, M., & Lord, D. (2019). Effects of geometry and pavement friction on horizontal curve crash frequency. Journal of Transportation Safety & Security, 11(2), 167–188. https://doi.org/10.1080/19439962.2017.1365317

    Article  Google Scholar 

  34. Awad, H. A. & Parry, T. (2018). Investigation the effect of pavement condition characteristics on bend segments accident frequency: Application of fixed and random parameters negative binomial models. In: International conference on transportation and development 2018 (pp. 165–176). American Society of Civil Engineers.

    Chapter  Google Scholar 

  35. Lindenmann, H. P. (2006). New findings regarding the significance of pavement skid resistance for road safety on Swiss freeways. Journal of Safety Research, 37(4), 395–400. https://doi.org/10.1016/j.jsr.2006.04.006

    Article  Google Scholar 

  36. Najafi, S., Flintsch, G. W., & Khaleghian, S. (2019). Pavement friction management–artificial neural network approach. International Journal of Pavement Engineering, 20(2), 125–135. https://doi.org/10.1080/10298436.2016.1264221

    Article  Google Scholar 

  37. Lyon, C., Persaud, B., & Merritt, D. (2018). Quantifying the safety effects of pavement friction improvements–results from a large-scale study. International Journal of Pavement Engineering, 19(2), 145–152. https://doi.org/10.1080/10298436.2016.1172709

    Article  Google Scholar 

  38. Bella, F., Calvi, A., & D’Amico, F. (2012). Impact of pavement defects on motorcycles’ road safety. Procedia-Social and Behavioral Sciences., 53, 943–952. https://doi.org/10.1016/j.sbspro.2012.09.943

    Article  Google Scholar 

  39. Siriphun, S., Horpibulsuk, S., Chotisakul, S., Suddeepong, A., Chinkulkijniwat, A., & Arulrajah, A. (2019). Effect of cumulative traffic and statistical predictive modelling of field skid resistance. Road Materials and Pavement Design., 20(2), 426–439. https://doi.org/10.1080/14680629.2017.1385511

    Article  Google Scholar 

  40. Chan, C. Y., Huang, B., Yan, X., & Richards, S. (2009). Relationship between highway pavement condition, crash frequency, and crash type. Journal of Transportation Safety & Security., 1(4), 268–281. https://doi.org/10.1080/19439960903391395

    Article  Google Scholar 

  41. Xu, P., & Huang, H. (2015). Modeling crash spatial heterogeneity: Random parameter versus geographically weighting. Accident Analysis & Prevention. https://doi.org/10.1016/j.aap.2014.10.020

    Article  Google Scholar 

  42. Farid, A., Abdel-Aty, M., & Lee, J. (2018). A new approach for calibrating safety performance functions. Accident Analysis & Prevention, 119, 188–194. https://doi.org/10.1016/j.aap.2018.07.023

    Article  Google Scholar 

  43. American Association of State Highway and Transportation Officials. (2010). Highway Safety Manual. American Association of State Highway and Transportation Officials.

    Google Scholar 

  44. Ma, L., Yan, X., Wei, C., & Wang, J. (2016). Modeling the equivalent property damage only crash rate for road segments using the hurdle regression framework. Analytic Methods in Accident Research, 11, 48–61. https://doi.org/10.1016/j.amar.2016.07.001

    Article  Google Scholar 

  45. Afghari, A. P., Haque, M. M., & Washington, S. (2020). Applying a joint model of crash count and crash severity to identify road segments with high risk of fatal and serious injury crashes. Accident Analysis & Prevention. https://doi.org/10.1016/j.aap.2020.105615

    Article  Google Scholar 

  46. Farid, A. (2015). Examining Multiple Approaches for the Transferability of Safety Performance Functions. M.Sc. Thesis. University of Central Florida, Orlando, Florida.

  47. Dissanayake, S., & Roy, U. (2014). Crash severity analysis of single vehicle run-off-road crashes. Journal of Transportation Technologies. https://doi.org/10.4236/jtts.2014.41001

    Article  Google Scholar 

  48. Aldagari, S., Al-Assi, M., Kassem, E., Chowdhury, A., & Masad, E. (2020). Development of predictive models for skid resistance of asphalt pavements and seal coat. International Journal of Pavement Engineering. https://doi.org/10.1080/10298436.2020.1766685

    Article  Google Scholar 

  49. de León Izeppi, E., Flintsch, G., Katicha, S., McCarthy, R., & McGhee, K. (2019). Locked-Wheel and Sideway-Force CFME Friction Testing Equipment Comparison and Evaluation Report. No. FHWA-RC-19–001. United States. Federal Highway Administration.

  50. SAS Institute. (2015). I. SAS/STAT® 141 user’s guide: the NLMIXED procedure. SAS Institute Inc.

    Google Scholar 

  51. Milton, J., & Mannering, F. (1998). The relationship among highway geometrics, traffic-related elements and motor-vehicle accident frequencies. Transportation. https://doi.org/10.1023/A:1005095725001

    Article  Google Scholar 

  52. Amarasingha, N., & Dissanayake, S. (2013). Modeling frequency of truck crashes on limited-access highways. Journal of the Transportation Research Forum. https://doi.org/10.5399/osu/jtrf.52.3.4189

    Article  Google Scholar 

Download references

Acknowledgements

The authors appreciate the efforts of WYDOT for funding this work through project #RS05221. All opinions are solely those of the authors. The subject matter, all figures and equations, not previously copyrighted by outside sources, are copyrighted by WYDOT, the State of Wyoming, and the University of Wyoming. All rights reserved copyrighting in 2021.

Author information

Authors and Affiliations

  1. Department of Civil and Architectural Engineering, Wyoming Technology Transfer Center, University of Wyoming, 1000 E. University Ave., Dept. 3295, Laramie, WY, 82071, USA

    Uttara Roy & Khaled Ksaibati

  2. Department of Civil and Environmental Engineering, Cal Poly State University, San Luis Obispo, CA, 93407, USA

    Ahmed Farid

Authors
  1. Uttara Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ahmed Farid
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Khaled Ksaibati
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Uttara Roy.

Ethics declarations

Conflict of interest

The authors declare no conflicts of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Roy, U., Farid, A. & Ksaibati, K. Effects of Pavement Friction and Geometry on Traffic Crash Frequencies: A Case Study in Wyoming. Int. J. Pavement Res. Technol. 16, 1468–1481 (2023). https://doi.org/10.1007/s42947-022-00208-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42947-022-00208-4

Keywords

Search

Navigation