Artificial Intelligence Applications in Road Traffic Forecasting: A Review of Current Research

Artificial intelligence (AI) is revolutionising global businesses and permanently changing industries forever; it has become an integral part of our daily lives, and the potential of this technology goes beyond home or personal use, as artificial intelligence continues to make great traction in transforming the future of humanity. The use of AI in traffic forecasting in particular and transportation planning in general, has gained attention in recent years due to its flexibility and ability to solve complex problems. However, research has shown that there is a gap in AI applications for longer terms forecasts, as most research focuses on real-time data analysis or within shorty or very short term where the vehicles are already presented within the road network. This research paper reviews the current state of AI applications in traffic forecasting, highlighting the challenges and opportunities

Road Deterioration detection A Machine Learning-Based System for Automated Pavement Crack Identification and Analysis

Road surfaces may deteriorate over time because of a number of external factors such as heavy traffic, unfavourable weather, and poor design. These flaws, which may include potholes, fissures, and uneven surfaces, can pose significant safety threats to both vehicles and pedestrians. This research aims to develop and evaluate an automated system for detecting and analyzing cracks in pavements based on machine learning. The research explores the utilisation of object detection techniques to identify and categorize different types of pavement cracks. Additionally, the proposed work investigates several approaches to integrate the outcome system with existing pavement management systems to enhance road maintenance and sustainability. The research focuses on identifying reliable data sources, creating accurate and effective object detection algorithms for pavement crack detection, classifying various types of cracks, and assessing their severity and extent. The research objectives include gathering reliable datasets, developing a precise and effective object detection algorithm, classifying different types of pavement cracks, and determining the severity and extent of the cracks. The study collected pavement crack images from various sources, including publicly available databases and images captured using mobile devices. Multiple object detection models, such as YOLOv5, YOLOv8, and CenterNet were trained and tested using the collected dataset. The proposed approaches were evaluated using different performance metrics, The achieved results indicated that the YOLOv5 model outperformed CenterNet by a significant margin

Physical and rheological characterization of carbonated bitumen for paving applications

In the paving industry, current attempts aimed at reducing greenhouse gas emissions have focused on the development of technologies that decrease bitumen viscosity so that asphalt mixtures can be produced at temperatures that are lower than conventional mixing temperature for hot-mix asphalt. This study focuses on the feasibility of producing new lower energy asphalt mixtures using CO2-modified bitumen. Gravimetric sorption techniques were used to establish the kinetics of CO2 diffusion in bitumen at multiple pressures. The rheological properties of the carbonated bitumen were characterized at multiple temperatures and loading frequencies using a dynamic shear rheometer.The results showed that CO2, at concentrations of up to about 0.3% w/w, caused significant (up to 3-folds) reduction in bitumen viscosity. A 10-fold increase in equilibrium CO2 uptake was observed when binders were conditioned in CO2 at 300 psi versus at 40 psi. The carbonated bitumen developed in this study has potential application in the production of lower energy asphalt mixtures. The work presents a novel application of CO2 at subcritical conditions, to reducing bitumen viscosity so that asphalt can be produced at lower temperature for paving applications. The work represents the first time such as attempt has been in the asphalt paving industry

Utilising Convolutional Neural Networks for Pavement Distress Classification and Detection

This paper examines deep learning models for accurate and efficient identification and classification of pavement distresses. In it, a variety of related studies conducted on the topic as well as the various identification and classification methods proposed, such as edge detection, machine learning classification informed by statistical feature extraction, artificial neural networks, and real-time object detection systems, are discussed. The study investigates the effect of image processing techniques such as grayscaling, background subtraction, and image resizing on the performance and generalizability of the models. Using convolutional neural networks (CNN) architectures, this paper proposes a model that correctly classifies images into five pavement distress categories, namely fatigue (or alligator), longitudinal, transverse, patches, and craters, with an accuracy rate of 90.4% and a recall rate of 90.1%. The model is contrasted to a current state-of-the-art model based on the You Only Look Once framework as well as a baseline CNN model to demonstrate the impact of the image processing and architecture building techniques discussed on performance. The findings of this paper contribute to the fields of computer vision and infrastructure monitoring by demonstrating the efficacy of convolutional neural networks (CNNs) in image classification and the viability of using CNNbased models to automate pavement condition monitoring

Observation of reversible moisture damage in asphalt mixtures

Durability of asphalt mixtures conditioned in hot water was investigated using stiffness measurements. Stiffness generally decreased with conditioning time. The effect of moisture on stiffness was found to be reversible as moisture conditioned-asphalt mixtures that had lost up to 80% of their initial stiffness fully recovered upon subsequent drying. Estimates of mastic film thickness and length of diffusion paths obtained from image analysis of X-ray CT scans of the asphalt mixtures suggest moisture diffusion was mainly restricted to the bulk mastic. The results suggest cohesive rather than adhesive failure dominated the durability of asphalt mixtures under the long-term moisture exposure used in this study

Influence of aggregate absorption and diffusion properties on moisture damage in asphalt mixtures

An experimental study was undertaken to characterise moisture sensitivity of asphalt mixtures by comparing certain physico-chemical properties of selected aggregates of different mineralogies to the moisture-induced strength degradation of the aggregate–mastic bonds. The aim of the study was to evaluate the effect of using different aggregate types (as substrates) with a single mastic type that had shown severe moisture sensitivity in the past when combined with a susceptible aggregate substrate. Four different aggregate types and an asphalt mastic (made with a 40/60 pen base bitumen) were used. Aggregate moisture sorption at ambient temperature was characterised using gravimetric techniques. Aggregate specific surface area was determined by octane adsorption using a dynamic vapour sorption device. Dynamic mechanical analysis techniques based on data from a dynamic shear rheometer were used to characterise the rheological properties of the asphalt mastic. Aggregate–mastic bond strength as a function of moisture conditioning time was determined using a tensile pull-off test set-up. The results were used to estimate equilibrium moisture uptake, diffusion coefficient, characteristic diffusion time, and aggregate ‘porosity’. The effect of moisture on bond strength was aggregate substrate-type-dependent with three out of the four aggregates performing well and the fourth performing poorly. The moisture absorption and diffusion properties of the poorly performing aggregates were worse than the ‘good’ performing aggregates. Susceptible aggregate–mastic bonds had high porosity, high moisture absorption, high diffusion coefficient and contained granite as substrates. Results of statistical analyses suggested that the differences in moisture sensitivity of the other three aggregates were not significant. Therefore, two unique damage models, one for ‘good’ performing and another for ‘poor’ performing were proposed to characterise moisture damage sensitivity of asphalt. The influence of aggregate moisture absorption and diffusion on asphalt mixture moisture damage was found to be aggregate-type-dependent. The results also suggested that in a susceptible mixture, the effect of the substrate aggregate may be more influential than the effect of mastic. The results have important implications for the selection of coarse aggregate for asphalt mix design

Moisture damage evaluation of aggregate–bitumen bonds with the respect of moisture absorption, tensile strength and failure surface

The moisture-induced deterioration of asphalt mixture is because of the loss of adhesion at the aggregate–bitumen interface and/or the loss of cohesion within the bitumen film. An experimental study was undertaken in this paper to characterise the effects of moisture on the direct tensile strength of aggregate–bitumen bonds. The aim of this paper was to evaluate the moisture sensitivity of aggregate–bitumen bonds in several different aspects, which included moisture absorption, tensile strength and failure surface examination. Moisture absorption and mineralogical compositions of aggregate were measured using gravimetric techniques and a Mineral Liberation Analyser (MLA), respectively, with the results being used to explain the moisture sensitivity of aggregate–bitumen bonds. Aggregate–bitumen bond strength was determined using a self-designed pull-off system with the capability of accurately controlling the bitumen film thickness. The photographs of the failure surface were quantitatively analysed using Image-J software. The results show that the magnitude of the aggregate–bitumen bonding strength in the dry condition is mainly controlled by bitumen. However, the retained tensile strength after moisture conditioning was found to be influenced by the mineralogical composition as well as the moisture diffusion properties of the aggregates. The linear relationship between retained tensile strength and the square root of moisture uptake suggests that the water absorption process controls the degradation of the aggregate–bitumen bond. The results also suggested that the deterioration of aggregate–bitumen bonds is linked to the decrease in cohesive failure percentage

Examination of moisture sensitivity of aggregate–bitumen bonding strength using loose asphalt mixture and physico-chemical surface energy property tests

In this study, the moisture sensitivity of different kinds of aggregates and bituminous binders is examined by comparing the performance between five empirical test methods for loose mixtures – static immersion test, rolling bottle test (RBT), boiling water test (BWT), total water immersion test and the ultrasonic method – with more fundamental surface energy-based test data. The RBT and BWT results showed that limestone aggregates perform better than granite aggregates and that, for unmodified binders, stiffer binders provide better moisture resistance compared with softer binder. Both tests were sensitive to aggregate type, binder type and anti-stripping agent type. Ranking of the mixtures by RBT and BWT was in general agreement with the surface energy-based tests, especially for mixtures that performed worst or best in RBT and BWT. The magnitude of the work of debonding in the presence of water was found to be aggregate type dependent which suggests the physico-chemical properties of aggregates may play a fundamental and more significant role in the generation of moisture damage, than bitumen properties

Recommendation of RILEM TC237-SIB on cohesion test of recycled asphalt

This recommendation describes how to evaluate the presence of potentially active bitumen in recycled asphalt (RA) materials through the cohesion test. The experimental protocol is designed according to the research performed by the RILEM Technical Committee 237-SIB ‘‘Testing and characterization of sustainable innovative bituminous materials and systems’’ with the purpose, to develop a new, simple and fast method for the characterization of RA while limiting the need for conventional rheological tests. The guidelines in this recommendation focus on the testing procedure including specimen preparation, data analysis and provide information on the preparation of a tests report

Assessing asphalt mixture moisture susceptibility through intrinsic adhesion, bitumen stripping and mechanical damage

Durability is one of the most important properties of an asphalt mixture. A key factor affecting the durability of asphalt pavements is moisture damage. Moisture damage is generally considered to be the result of two main mechanisms; the loss of adhesion between bitumen and the aggregate and the loss of cohesion within the mixture. Conventional test methods for evaluating moisture damage include tests conducted on loose bitumen-coated aggregates and those conducted on compacted asphalt mixtures. This paper looks at results from the rolling bottle and the saturated ageing tensile stiffness (SATS) tests in an attempt to better understand the underlying processes and mechanisms of moisture damage with the help of surface energy measurements on the constituent bitumen and aggregates. Combinations of materials were assessed using both the rolling bottle and SATS tests. The surface energy properties of the binders were measured using a dynamic contact angle analyser and those of the aggregates using a dynamic vapour sorption device. From these surface energy measurements, it was possible to predict the relative performance of both the simple rolling bottle test and the more complicated SATS test. Mineralogical composition of the aggregates determined using a mineral liberation analyser was used to explain the differences in performance of the mixtures considered