Matteo Pettinari

The tire–road friction coefficient is a critical evaluation index of the service performance of roads: it governs the stopping distance, traction control, and stability of vehicles. Moreover, friction information is also needed in many function units of modern vehicles. This paper proposes a novel data-driven approach for inference of the maximum tire–road friction coefficient using a combination of vehicle dynamics signal and machine vision data. The approach is aimed at robust road condition perception that can provide frequent measurements over large areas across all weather conditions. Two different neural network architectures were adopted to extract in-depth features behind vehicle dynamics signals and road surface images. Features from these two types of data were then fused in two different levels, namely feature level and decision level, forming a multi-feature fusion neural network. The proposed network performs better than models based only on dynamic signals or vision data. The method proposed was applied to real data obtained from an electric car in a highway driving scenario. For classification of the maximum tire–road friction coefficient, the proposed network can yield F1-score increments of 0.09 and 0.18 from dynamics-based and vision-based sub-models, respectively. For the maximum tire–road friction coefficient value regression, the proposed model also achieves the highest R-square score of 0.71. Of these two types of data collected under highway driving scenarios, the vision data contribute more to the overall performance of the proposed model. Nevertheless, the dynamics data possess excellent potential in poor lighting conditions. With these fused features, the proposed multi-feature fusion network can not only improve the accuracy of maximum tire–road friction coefficient estimation but also is deemed workable for a broader range of scenarios.

The road-transport sector has a significant impact on energy consumption. A relevant component of this energy usage is associated with rolling resistance (RR) between tires and pavement. In Denmark, CO₂ emissions from road transport alone have been quantified as 4.6 Mt/yr. Replacing standard stone asphalt with durable low-RR pavements is expected to reduce CO₂ emissions up to 1%. The Danish Road Directorate started, back in 2012, optimising a surface layer for reducing RR. The low-RR mixture was designed to provide a durable texture capable to meet all safety requirements. In 2016, two low-RR mixtures and reference asphalt concrete were paved on a test section. To evaluate the texture durability these mixtures were sampled at the construction site and tested with a circular road tester. The results show that the durability of low-RR pavements can be enhanced by using premodified binder, which reduces changes in texture properties and increases rutting resistance.

The road transportation sector is a dominant and growing energy consumer. Although investigations to quantify the road infrastructure’s impact on energy consumption have been carried out, there are currently no standard methods to measure or label the energy efficiency of road networks. Consequently, road agencies and operators are limited to restricted types of data when managing the road network. Moreover, initiatives meant to reduce energy consumption cannot be measured and quantified. This work is, therefore, motivated by the desire to provide road agencies with a road energy efficiency monitoring concept that can provide frequent measurements over large areas across all weather conditions. The proposed system is based on measurements from in-vehicle sensors. The measurements are collected onboard with an Internet-of-Things (IoT) device, then transmitted periodically before being processed, normalized, and saved in a database. The normalization procedure involves modeling the vehicle’s primary driving resistances in the driving direction. It is hypothesized that the energy remaining after normalization holds information about wind conditions, vehicle-related inefficiencies, and the physical condition of the road. The new method was first validated utilizing a limited dataset of vehicles driving at a constant speed on a short highway section. Next, the method was applied to data obtained from ten nominally identical electric cars driven over highways and urban roads. The normalized energy was compared with road roughness measurements collected by a standard road profilometer. The average measured energy consumption was 1.55 Wh per 10 m. The average normalized energy consumption was 0.13 and 0.37 Wh per 10 m for highways and urban roads, respectively. A correlation analysis showed that normalized energy consumption was positively correlated to road roughness. The average Pearson correlation coefficient was 0.88 for aggregated data and 0.32 and 0.39 for 1000-m road sections on highways and urban roads, respectively. An increase in IRI of 1 m/km resulted in a 3.4% increase in normalized energy consumption. The results show that the normalized energy holds information about the road roughness. Thus, considering the emergence of connected vehicle technologies, the method seems promising and can potentially be used as a platform for future large-scale road energy efficiency monitoring.

Transportation is a major contributor to greenhouse gas emissions, encouraging the development of low–rolling resistance pavements to decrease vehicles’ energy consumption. This paper examines the laboratory performance of two stone-matrix asphalt (SMA) mixes specially designed by the Danish Road Directorate to reduce rolling resistance. The materials of interest were subjected to a viscoelastic characterization of the materials, dynamic modulus test for the mix, and Superpave performance grade (PG) grading for the source binders. Cracking and rutting potential of the mixes were evaluated via the Illinois flexibility index test and the Hamburg wheel track test, respectively. The SMA mixes had a lower modulus at low temperatures but higher modulus at high temperatures, which is desirable, and exhibited high flexibility index (FI) and low permanent deformation compared to the reference material. Applying the Illinois balance mix design (I-BMD) concept, on a two-dimensional (2D) interaction plot both SMAs behave as stiff and flexible, an indication that they can provide adequate crack and rutting resistance while reducing rolling resistance. However, adding a stiffness criterion for a (three-dimensional) 3D interaction plot, only the mix labeled SMA6 complied with all the thresholds.

In recent years the pavement engineering community has shown increasing interest in shifting from a stationary falling weight deflectometer (FWD) to moving testing platforms such as the traffic speed deflectometer (TSD). This paper dealt with comparing TSD measurements against FWD measurements; it focused on the comparison methodology, utilizing experimental data for demonstration. To better account for differences in loading conditions between the two devices a new FWD deflection index was formulated first. This index served as reference/benchmark for assessing the corresponding TSD measurements. Next, a Taylor diagram was proposed for visualizing several comparison statistics. Finally, a modern agreement metric was identified and applied for ranking comparison results across different datasets. Overall, the suggested methodology is deemed generic and highly applicable to future situations, especially for assessing the worth of emerging device upgrades or improved interpretation schemes (or both).

This paper present a laboratory study on the influence of filler type on the rheological properties of mastics. Mastic is the main component that undergoes any deformations in asphalt structure. Low shear resistance can lead to susceptibility to permanent deformations. The type of filler used in mastic can highly influence its rheological properties. The main objective of the study was to evaluate different types of mineral fillers and their stiffening effect on unmodified and modified mastics in terms of permanent deformations. During this study performance of three mineral fillers, namely Jelsa granite, Portland cement and Aggersund limestone were investigated. The fillers used in this study were evaluated according to common filler characteristics, such as Rigden voids (RV), density, specific surface area, fineness modulus and others. Dynamic shear rheometer was used for testing mastics under repeated shear stress with multiple stress creep recovery test method. Mastics performance was tested under three different temperature conditions. Analysis of the results showed that limestone filler provide best stiffening effect. The main factors affecting that is its fine gradation, high percentage of RV and high specific surface value. This subsequently results in high amount of bitumen being as fixed one. Granite filler with low value of RV showed poor performance.

A project named COOEE, financed by the Danish Innovation Fund, was commenced in 2012. This project dealt with reduction of CO₂ emissions by exploitation of rolling resistance modelling of pavements. The challenge was to establish a scientific background for the development of pavements with low rolling resistance that satisfy safety standards and provide at least 15 years durability. Stone mastic asphalt (SMA) is the most common surface layer adopted on the Danish road network and the COOEE project aimed at optimizing the mix design with the goal of reducing rolling resistance. The optimization procedure has led to mixes fulfil the project objectives but at the same time exhibited deficiency in texture stability and durability. An extensive laboratory investigation has been completed in order to identify the deficiency sources and suggest appropriate solutions. In this connection, a study of workability and mechanical properties of COOEE mixtures, supported by a rheological characterization of the adopted binder, was conducted and compared to those measured on a conventional SMA. The investigation results highlight the importance of providing a proper compaction degree—as a fundamental requirement for achieving desired mix performance. It is also found that investigating binder properties with a dynamic shear rheometer is essential for gaining better understanding of mixture response.