Estimating Pavement Roughness and Macrotexture Using Vehicles Equipped with Smart Tires

Area of Science:

• Civil Engineering
• Transportation Engineering
• Materials Science

Background:

• Conventional pavement condition monitoring is slow and infrequent, hindering real-time assessment.
• Limitations include slow data acquisition and low survey frequency, impacting vast road networks.
• Continuous monitoring from moving vehicles is needed for digital road assessment.

Purpose of the Study:

• To evaluate smart tire technology for continuous pavement condition monitoring.
• To correlate smart tire indicators (Dynamic Index and Pr index) with traditional methods (IRI and MPD).
• To assess smart tire data for identifying time-dependent trends and ranking road segments.

Main Methods:

• Collected smart tire data (DI, Pr index) over 405 km of motorway.
• Correlated smart tire indicators with traditional pavement condition measurements (IRI, MPD).
• Analyzed pavement age trends and ranked motorway segments using smart tire data.

Main Results:

• Dynamic Index (DI) showed moderate correlation with International Roughness Index (IRI).
• Pr index exhibited a strong correlation with Mean Profile Depth (MPD).
• Smart tire indicators identified meaningful pavement-age trends for asphalt courses.

Conclusions:

• Smart tire technology shows potential as a complementary tool for network-level pavement management.
• Rankings based on smart tire data align with traditional methods, validating its effectiveness.
• This technology enables more efficient and real-time pavement asset management.