SpringerLink
Log in

A nonlinear analytical approach for estimating vehicle braking distance based on multi-body dynamic simulation

  • Published:
Sādhanā Aims and scope Submit manuscript

Abstract

The friction coefficient resulting from the road surface condition directly affects the vehicle braking condition and distance. In the American Association of State Highway and Transportation Officials (AASHTO), the braking distance for different speeds, upgrades, and downgrades is recommended. However, the effects of the variations in the friction coefficient, type, and weight of vehicles are not directly taken into account. Therefore, the present study firstly examines the effects of the variations in vehicle speed, friction coefficient, longitudinal grade (upgrade and downgrade) in AASHTO 2018, and the vehicle type on braking distance. Secondly, a nonlinear analytical approach is considered to estimate braking distance and determine a critical friction coefficient that causes decreases in the braking distance. The findings indicate that under dry road surface conditions (with a friction coefficient of 0.6), the weight of the vehicle predominantly influences braking distance, resulting in heavier vehicles having shorter braking distances than lighter vehicles across all selected grades, except at a − 6%. However, when the road surface friction coefficient decreases from 0.6 to 0.18, the impact of vehicle weight surpasses the effect of the longitudinal grade, leading to an increase in braking distance. Consequently, when designing a road, it's essential to consider not only the technical road parameters and vehicle dimensions but also the vehicle weight and the road surface conditions in areas prone to snowfall, among other factors. If we wish to account for snowy and icy conditions, in addition to AASHTO's classification of wet conditions, in order to refine predictions of braking distance, the results of this study can provide valuable insights for road designers.

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

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16

Similar content being viewed by others

References

  1. Zhao Y Q, Li H Q, Lin F, Wang J and Ji X W 2017 Estimation of road friction coefficient in different road conditions based on vehicle braking dynamics. Chin. J. Mech. Eng. 30(4): 982–990

    Article  Google Scholar 

  2. Pal S and Roy S K 2019 Impact of side friction on performance of rural highways in India. J. Infrastruct. Syst. 25(2): 04019006

    Article  Google Scholar 

  3. Šarkan B, Jaśkiewicz M and Kiktová M 2020 The impact of the truck loads on the braking efficiency assessment. Open Eng. 10(1): 105–112

    Article  Google Scholar 

  4. Günay M, Korkmaz M E and Özmen R 2020 An investigation on braking systems used in railway vehicles. Eng. Sci. Technol. Int. J. 23(2): 421–431

    Google Scholar 

  5. Greibe P 2008 Determination of braking distance and driver behaviour based on braking trials. In: 87th Transportation Research Board Annual Meeting, Washington DC, USA

  6. Izeppi E D L, Flintsch G W and McGhee K K 2010 Field Performance of High Friction Surfaces (No. FHWA/VTRC 10-CR6)

  7. Sabri M and Fauza A 2018 Analysis of vehicle braking behaviour and distance stop**. IOP Conf. Ser. Mater. Sci. Eng. 309(1): 012020

    Article  Google Scholar 

  8. Himes S, Porter R J, Hamilton I and Donnell E 2019 Safety evaluation of geometric design criteria: horizontal curve radius and side friction demand on rural, two-lane highways. Transp. Res. Rec. 2673(3): 516–525

    Article  Google Scholar 

  9. Rezaei D, Aghayan I and Hadadi F 2021 Studying perturbations and wave propagations by lane closures on traffic characteristics based on a dynamic approach. Phys. A Stat. Mech. Appl. 566: 125654

    Article  MathSciNet  Google Scholar 

  10. Aghayan I, Noii N and Kunt M M 2012 Extended traffic crash modelling through precision and response time using fuzzy clustering algorithms compared with multi-layer perceptron. Promet Traffic Transp. 24(6): 455–467

    Google Scholar 

  11. Malin F, Norros I and Innamaa S 2019 Accident risk of road and weather conditions on different road types. Accid. Anal. Prevent. 122: 181–188

    Article  Google Scholar 

  12. Pacejka H B 2002 Tire and vehicle dynamics. Society of Automotive Engineers Inc, Warrendale, USA

    Google Scholar 

  13. Kim S, Kim J, Kim K, Kwon S, Lee B and Kang Y 2013 Parametric study using a braking distance simulation of an anti-locked braking system vehicle. Asian J. Chem. 25(9): 5263

    Article  Google Scholar 

  14. Wang B, Guan H, Lu P and Zhang A 2014 Road surface condition identification approach based on road characteristic value. J. Terramech. 56: 103–117

    Article  Google Scholar 

  15. Bartunek M, Moravčik O and Schreiber P 2012 Braking distance estimation by simulation. Appl. Mech. Mater. 128: 1131–1134

    Google Scholar 

  16. Fambro DB, Fitzpatrick K and Koppa RJ 1997 NCHRP Report 400: Determination of Stop** Sight Distances

  17. AASHTO 2018 A Policy on Geometric Design of Highways and Streets. American Association of State Highway and Transportation Officials, Washington, D.C

    Google Scholar 

  18. AASHTO 1940 A Policy on Geometric Design of Highways and Streets. American Association of State Highway and Transportation Officials, Washington, D.C

    Google Scholar 

  19. AASHTO 2001 A Policy on Geometric Design of Highways and Streets. American Association of State Highway and Transportation Officials, Washington, D.C

    Google Scholar 

  20. AASHTO 2004 A Policy on Geometric Design of Highways and Streets. American Association of State Highway and Transportation Officials, Washington, D.C

    Google Scholar 

  21. AASHTO 2011 A Policy on Geometric Design of Highways and Streets. American Association of State Highway and Transportation Officials, Washington, D.C

    Google Scholar 

  22. Raad L, Lu J and Hulsey L 2002 Traction performance of transit and paratransit vehicles on ice and snow surfaces. In: Cold Regions Engineering: Cold Regions Impacts on Transportation and Infrastructure, pp. 290–298

  23. Hall JW, Smith KL, Titus-Glover L, Wambold JC, Yager TJ and Rado Z 2009 Guide for pavement friction. NCHRP. Web-only document 108, Contractor's Final Report NCHRP Project 01-43. Transportation Research Board

  24. Fwa T F, Pasindu H R and Ong G P 2012 Critical rut depth for pavement maintenance based on vehicle skidding and hydroplaning consideration. J. Transp. Eng. 138(4): 423–429

    Article  Google Scholar 

  25. Oh Y and Lee H 2014 Characteristics of a tire friction and performances of a braking in a high speed driving. Adv. Mech. Eng. 6: 260428

    Article  Google Scholar 

  26. Shoop S A and Kestler M A 2015 Updating side friction factors for design of unpaved roads. Transp. Res. Rec. 2472(1): 19–28

    Article  Google Scholar 

  27. Qu G, He Y, Sun X and Tian J 2018 Modeling of lateral stability of tractor-semitrailer on combined alignments of freeway. Discrete Dyn. Nat. Soc

  28. Li P and He J 2016 Geometric design safety estimation based on tire–road side friction. Transp. Res. Part C Emerg. Technol. 63: 114–125

    Article  Google Scholar 

  29. Donnell E, Wood J, Himes S and Torbic D 2016 Use of side friction in horizontal curve design: a margin of safety assessment. Transp. Res. Rec. J. Transp. Res. Board 2588: 61–70

    Article  Google Scholar 

  30. Meyers D R and Austin T P 2012 Dry pavement friction reductions due to sanding applications. SAE Int. J. Commer. Veh. 5(2012-01–0603): 239–250

    Article  Google Scholar 

  31. Rajaei M, Sefidmazgi N R and Bahia H 2014 Establishment of relationship between pavement surface friction and mixture design properties (No. 14–4430)

  32. Kordani A A, Rahmani O, Nasiri A A and Boroomandrad S M 2018 Effect of adverse weather conditions on vehicle braking distance of highways. Civ. Eng. J. 4(1): 46–57

    Article  Google Scholar 

  33. Zamzamzadeh M, Saifizul A A, Ramli R and Soong M F 2018 Heavy vehicle multi-body dynamic simulations to estimate skidding distance. Baltic J. Road Bridge Eng. 13(1): 23–33

    Article  Google Scholar 

  34. Sharizli A, Ramli R, Karim M R and Abdullah A S 2014 Simulation and analysis on the effect of gross vehicle weight on braking distance of heavy vehicle. Appl. Mech. Mater. 564: 77–82

    Article  Google Scholar 

  35. Sharizli A, Ramli R, Karim M R and Saifizul A 2013 Novel method of determining braking distance of heavy vehicle using advanced simulation technique. In: Proceedings of the 3rd International Conference on Civil, Transport and Environment Engineering, 25–26 December, 2013. Bangkok, Thailand

  36. Ibrahim A A and Abdullah M L 2013 The effects of vehicle speed and type of road surface on the longitudinal slip of tires and the brake stop** distance. Eng. Technol. J. 31: 1854–1869

    Article  Google Scholar 

  37. Li S, Chen J and Huang H 2014 Investigation on emergency brake property of a heavy-duty vehicle based on functional virtual prototy** model. Open Mech. Eng. J. 8(1): 675–681

    Article  Google Scholar 

  38. Kavitha C, Ashok B, Nanthagopal K, Desai R, Rastogi N and Shetty S 2017 Braking distance algorithm for autonomous cars using road surface recognition. IOP Conf. Ser. Mater. Sci. Eng. 263(6): 062034

    Article  Google Scholar 

  39. Tong C and Li TZ 2015 Car driving safety analysis in rainy and snowy weather based on ADAMS/Car. In: CICTP 2015, pp. 2895–2903

  40. World Road Association (WRA) 2003 Road Safety Manual: Recommendations from the World Road Association (PIARC). Route2 Market

  41. MSC Software 2018 ADAMS manual. www.mscsoftware.com/product/adams

  42. Dunn A and Hoover R 2004 Class 8 truck tractor braking performance improvement study. Low coefficient of friction performance and stability plus parking brake evaluations of four foundation brake configurations. In: Transportation Research Center Inc./National Highway Traffic Safety Administration

  43. Abdollahzadeh Nasiri A S, Rahmani O, Abdi Kordani A, Karballaeezadeh N and Mosavi A 2020 Evaluation of safety in horizontal curves of roads using a multi-body dynamic simulation process. Int. J. Environ. Res. Public Health 17(16): 5975

    Article  Google Scholar 

  44. Rahmani O, Tehrani H G and Nasiri A S A 2022 A new procedure for analysis of ride quality in roads using multi-body dynamic simulation. Innov. Infrastruct. Solut. 7(3): 209

    Article  Google Scholar 

  45. Ferreira C 2006 Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence 21. Springer, New York

    Book  Google Scholar 

Download references

Acknowledgment

None to declare.

Funding

None to declare.

Author information

Authors and Affiliations

  1. Faculty of Civil Engineering, Shahrood University of Technology, Shahrood, Iran

    Omid Rahmani, Iman Aghayan & Farhad Hadadi

  2. Institute for Physical Infrastructure and Transportation (IPIT), University of Wisconsin–Milwaukee, Milwaukee, WI, 53201, USA

    Iman Aghayan

  3. Department of Civil Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran

    Amir Saman Abdollahzadeh Nasiri

  4. Université Gustave Eiffel, Campus de Nantes, ME/EASE, Bouguenais Cedex, France

    Malal Kane

Authors
  1. Omid Rahmani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Iman Aghayan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amir Saman Abdollahzadeh Nasiri
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Malal Kane
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Farhad Hadadi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Omid Rahmani.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest in the paper.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rahmani, O., Aghayan, I., Abdollahzadeh Nasiri, A.S. et al. A nonlinear analytical approach for estimating vehicle braking distance based on multi-body dynamic simulation. Sādhanā 49, 20 (2024). https://doi.org/10.1007/s12046-023-02381-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12046-023-02381-z

Keywords

Search

Navigation