A novel processing methodology for traffic-speed road surveys using point lasers

The rapidly increasing traffic volumes using local road networks allied to the implications of climate change drive the demand for cost-effective, reliable and accurate road condition assessment. A particular concern for local road asset managers is the loss of material from the road surface known as fretting which unchecked can lead to potholes. In order to assess the road condition quantitatively and affordably, a system should be designed with low complexity, be capable of operating in a variety of weather conditions and operate at normal traffic-speeds. Many different techniques have been developed for road condition assessment such as ground penetrating radar, visual sensors and mobile scanning lasers. In this work, the use of the point laser technique for scanning the road surface is investigated. It has the advantages of being sufficiently accurate, is relatively unaffected by levels of illumination and it produces relatively low volumes of data. In this work, road fretting/surface disintegration was determined using a novel signal processing approach which considers a number of features of reflected laser signals. The proposed methodology was demonstrated using data collected from the UK's local road network. The experimental results indicate that the proposed system can assess road fretting to an accuracy which is comparable to a visual inspection, and at Information Quality Level (IQL) 3 which is sufficient for tactical road asset management whereby road sections requiring treatment are selected and appropriate treatments identified

Analytical Study of Deep Learning Methods for Road Condition Assessment

Automated pavement distress recognition is a key step in smart infrastructure assessment. Advances in deep learning and computer vision have improved the automated recognition of pavement distresses in road surface images. This task, however, remains challenging due to the high variations in road objects and pavement types, variety of lighting condition, low contrast, and background noises in pavement images. In this dissertation, we propose novel deep learning algorithms for image-based road condition assessment to tackle current challenges in detection, classification and segmentation of pavement images. Motivated by the need for classifying a wide range of objects in road monitoring, this dissertation introduces a Multi-Scale Convolution Neural Network (MCNN) for multi-class classification of pavement images. MCNN improves the classification performance by encoding contextual information through multi-scale input tiles. Then, an Attention-Based Multi-Scale CNN (A+MCNN) is proposed to further improve the classification results through a novel mid-fusion strategy for combining multi-scale features extracted from multi-scale input tiles. An attention module is designed as an adaptive fusion strategy to generate importance scores and integrate multi-scale features based on how informative they are to the classification task. Finally, Dual Attention CNN (DACNN) is introduced to improve the performance of multi-class classification using both intensity and range images collected with 3D laser imaging devices. DACNN integrates information in intensity and range images to enhance distinct features improving the objects classification in noisy images under various illumination conditions. The standard road condition assessment includes determining not only the type of defects but also the severity of detects. In this regard, a pavement crack segmentation algorithm, CrackSegmenter, is proposed to detect crack at pixel level. The CrackSegmenter leverages residual blocks, attention blocks, Atrous Spatial Pyramid Pooling (ASSP), and squeeze and excitation blocks to improve segmentation performance in pavement crack images

Identifikasi Kebutuhan Penanganan Jalan Nasional 2015-2019 Di Provinsi Sumatera Selatan

Multi-criteria analysis combines road condition assessment score (IRI, SDI, road width, V/C ratio, traffic volume, vehicle speed, travel time) with importance level of the development of the area. The analysis resultedthe need of maintenance of national road in South Sumatera (2015-2019) more optimally, efficiently and effectively. The result are as follows : (1) in 2015, 95,86% of the roads require routine maintenance and 4,14% require capacity improvement by widening the road to 7,0 meter; (2) among 2016 to 2019, 100,0% of the roads require routine maintenance without widening. The implementation of the maintenance does notaffect much to reduce travel time, although 100,0% of road has a width of more than 7,0 meter and about 90,37% of road has IRI less than 4,0 m/km. The condition is caused by there are no maintenance requirements concerning the improvement of the substandard geometric

An investigation of the suitability of smartphone devices for road condition assessment

The measurement of road roughness is important for asset management decision making. Not only is road roughness an indicator of road condition and thereby a means of determining road maintenance needs, but it is also used to determine vehicle operating costs (i.e. fuel consumption and vehicle maintenance). Road agencies with large road networks, because of resource issues, are unable to record the condition of the entire network on a sufficiently frequent basis to adequately determine road condition and therefore identify proactive road maintenance requirements. This research investigates whether a smartphone based system may be a suitable means for measuring road roughness at sufficient accuracy and if data from such a system could be used to inform asset management decision making and provide the road user with information about vehicle operating cost of using different routes. This research by means of an in depth review of the literature and the use of a vehicle dynamics package, identified the factors which can most influence the accurate measurement of road roughness by smartphone based systems and quantified the relative importance of these factors. The investigation found that measured vehicle body acceleration, speed, vehicle type and smartphone type are very influential inaccurately determining road roughness from a smartphone type approach. Thereafter, a variety of computational methods were trialled on a multi-variable dataset that had been built using a vehicle dynamic package, to determine if the algorithms could be used to infer road roughness from a dataset which might be available from a smartphone based system. As a result of this analysis, the random forest machine learning algorithm was identified as the most suitable for the task at hand. It was found that the developed algorithm could be used to determine precise measures of road roughness if data concerning vehicle speed and type, sprung mass, smartphone type and vehicle body acceleration were available. The same algorithm could also be used to classify road condition if only vehicle speed, vehicle type and measured vehicle vertical body acceleration were available in the dataset

Road condition assessment by OBIA and feature selection techniques using very high-resolution WorldView-2 imagery

Accurate information on the conditions of road asphalt is necessary for economic development and transportation management. In this study, object-based image analysis (OBIA) rule-sets are proposed based on feature selection technique to extract road asphalt conditions (good and poor) using WorldView-2 (WV-2) satellite data. Different feature selection techniques, including support vector machine (SVM), random forest (RF) and chi-square (CHI) are evaluated to indicate the most effective algorithm to identify the best set of OBIA attributes (spatial, spectral, textural and colour). The chi-square algorithm outperformed SVM and RF techniques. The classification result based on CHI algorithm achieved an overall accuracy of 83.19% for the training image (first site). Furthermore, the proposed model was used to examine its performance in different areas; and it achieved accuracy levels of 83.44, 87.80 and 80.26% for the different selected areas. Therefore, the selected method can be potentially useful for detecting road conditions based on WV-2 images