Prediction of tire/wet road friction and its variation with speed from road macro- and microtexture, and tire-related properties

In this paper, validation of a model for the speed dependency of friction is presented. Based on the shape of the friction – speed curve, the model assumes that calculation of friction at any speed would need an estimate of friction at very low speed, and the knowledge of its variation with speed described by the Stribeck curve. Two existing models developed at LCPC are then coupled to predict friction versus speed from the following characteristics: road surface macro and microtexture, rubber relaxation time, wheel slip, tire tread depth and water layer thickness. Description of the models is given. The unified model is validated on data obtained from experimental campaigns conducted in France from 2001 to 2003. On 30 sections, including test tracks and trafficked roads, friction was measured by means of the British Pendulum and the front-wheel braking method with the LCPC instrumented car giving μpeak and μlocked. Road microtexture profiles were measured by means of LCPC static laser systems. Profile characteristics are given. BPN and μpeak values were compared with results predicted from road microtexture and tire-rubber properties. For μlocked, which is obtained at higher speeds, its value was deduced from μpeak using the modelled Stribeck curve. Results are discussed.

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Additional Info

Field Value
Source SIIV Congress (New Technologies and Modeling Tools for Roads - Applications to Design and Management)
Author Do, Minh Tan, Delanne, Yves
Maintainer CCSD
Last Updated May 10, 2026, 02:56 (UTC)
Created May 10, 2026, 02:56 (UTC)
Identifier hal-00851305
Language en
Rights https://about.hal.science/hal-authorisation-v1/
contributor Division Entretien, Sécurité et Acoustique des Routes (LCPC/ESAR) ; Laboratoire Central des Ponts et Chaussées (LCPC)-PRES Université Nantes Angers Le Mans (UNAM)
creator Do, Minh Tan
date 2004-10-27T00:00:00
harvest_object_id f1965078-590f-4814-957a-32c5d8a4158a
harvest_source_id 3374d638-d20b-4672-ba96-a23232d55657
harvest_source_title test moissonnage SELUNE
metadata_modified 2022-09-30T00:00:00
set_spec type:COMM