Improving road asset condition monitoring

Road networks often carry more than 80% of a country’s total passenger-km and over 50% of its freight ton-km according to the World Bank. Efficient maintenance of road networks is highly important. Road asset management, which is essential for maintenance programs, consist of monitoring, assessing and decision making necessary for maintenance, repair and/or replacement. This process is highly dependent on adequate and timely pavement condition data. Current practice for collecting and analysing such data is 99% manual. To optimize this process, research has been performed towards automation. Several methods to automatically detect road assets and pavement conditions are proposed. In this paper, we present an analysis of the current state of practice of road asset monitoring, a discussion of the limitations, and a qualitative evaluation of the proposed automation methods found in the literature. The objective of this paper is to understand the issues associated with current processes, and assess the available tools to address these problems. The current state of practice is categorized into: 1) type of data collected; 2) type of asset covered and 3) amount of information provided. The categories are evaluated in terms of a) accuracy; b) applicability (efficiency); c) cost; and d) overall improvement to current practice. Despite the methods available, the outcome of the study indicates that current condition monitoring lacks efficiency, and none provide a holistic solution to the problem of road asset condition monitorin

Large-scale assessment of mobile crowdsensed data: a case study

Mobile crowdsensing (MCS) is a well-established paradigm that leverages mobile devices’ ubiquitous nature and processing capabilities for large-scale data collection to monitor phenomena of common interest. Crowd-powered data collection is significantly faster and more cost-effective than traditional methods. However, it poses challenges in assessing the accuracy and extracting information from large volumes of user-generated data. SmartRoadSense (SRS) is an MCS technology that utilises sensors embedded in mobile phones to monitor the quality of road surfaces by computing a crowdsensed road roughness index (referred to as PPE). The present work performs statistical modelling of PPE to analyse its distribution across the road network and elucidate how it can be efficiently analysed and interpreted. Joint statistical analysis of open datasets is then carried out to investigate the effect of both internal and external road features on PPE . Several road properties affecting PPE as predicted are identified, providing evidence that SRS can be effectively applied to assess road quality conditions. Finally, the effect of road category and the speed limit on the mean and standard deviation of PPE is evaluated, incorporating previous results on the relationship between vehicle speed and PPE . These results enable more effective and confident use of the SRS platform and its data to help inform road construction and renovation decisions, especially where a lack of resources limits the use of conventional approaches. The work also exemplifies how crowdsensing technologies can benefit from open data integration and highlights the importance of making coherent, comprehensive, and well-structured open datasets available to the public

Vehicle classification in intelligent transport systems: an overview, methods and software perspective

Vehicle Classification (VC) is a key element of Intelligent Transportation Systems (ITS). Diverse ranges of ITS applications like security systems, surveillance frameworks, fleet monitoring, traffic safety, and automated parking are using VC. Basically, in the current VC methods, vehicles are classified locally as a vehicle passes through a monitoring area, by fixed sensors or using a compound method. This paper presents a pervasive study on the state of the art of VC methods. We introduce a detailed VC taxonomy and explore the different kinds of traffic information that can be extracted via each method. Subsequently, traditional and cutting edge VC systems are investigated from different aspects. Specifically, strengths and shortcomings of the existing VC methods are discussed and real-time alternatives like Vehicular Ad-hoc Networks (VANETs) are investigated to convey physical as well as kinematic characteristics of the vehicles. Finally, we review a broad range of soft computing solutions involved in VC in the context of machine learning, neural networks, miscellaneous features, models and other methods

Anomaly detection in roads with a data mining approach

Road condition has an important role in our daily live. Anomalies in road surface can cause accidents, mechanical failure, stress and discomfort in drivers and passengers. Governments spend millions each year in roads maintenance for maintaining roads in good condition. But extensive maintenance work can lead to traffic jams, causing frustration in road users. In way to avoid problems caused by road anomalies, we propose a system that can detect road anomalies using smartphone sensors. The approach is based in data-mining algorithms to mitigate the problem of hardware diversity. In this work we used scikit-learn, a python module, and Weka, as tools for data-mining. All cleaning data process was made using python language. The final results show that it is possible detect road anomalies using only a smartphone.European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020)This research is sponsored by the Portugal Incentive System for Research and Technological Development. Project in co-promotion nº 002797/2015 (INNOVCAR 2015-2018)info:eu-repo/semantics/publishedVersio

Smartphone-based molecular sensing for advanced characterization of asphalt concrete materials

Pavement systems deteriorate with time due to the aging of materials, excessive use, overloading, climatic conditions, inadequate maintenance, and deficiencies in inspection methods. Proper evaluation of pavement conditions provides important decision-support to implement preventative rehabilitation. This study presents an innovative smartphone-based monitoring method for advanced characterization of asphalt concrete materials. The proposed method is based on deploying a pocket-sized near-infrared (NIR) molecular sensor that is fully integrated with smartphones. The NIR spectrometer illuminates a sample with a broad-spectrum of near-infrared light, which can be absorbed, transmitted, reflected, or scattered by the sample. The light intensity is measured as a function of wavelength before and after interacting with the sample. Thereafter, the diffuse reflectance, a combination of absorbance and scattering, caused by the sample is calculated. This portable smartphone-based NIR method is used to detect asphalt binders with various performance grading (PG) and aging levels. To this end, a number of binder samples are tested in a wavelength range of 740 to 1070 nm. The results indicate that asphalt binders with different grades and aging levels yield significantly different spectrums. These distinctive spectrums can be attributed to the variations of binder components such as saturate, asphaltenic, resin, and aromatic. Furthermore, the molecular sensor is successfully deployed to detect and classify asphalt mixtures fabricated with a various binder and recycled material types such as styrene-butadiene-styrene (SBS), ground tire rubber (SBS), engineered crumbed rubber (ECR), reclaimed asphalt pavement (RAP), and recycled asphalt shingles (RAS). The proposed monitoring technology is not only a viable tool for asphalt material characterization but also a cost-effective platform capable of transforming the current physical and chemical methods for civil engineering material characterization.Includes bibliographical reference