Automatic detection of pavement cracks is an important task in transportation maintenance for driving safety assurance. However, it remains a challenging task due to the intensity inhomogeneity of ...cracks and complexity of the background, e.g., the low contrast with surrounding pavement and possible shadows with similar intensity. Inspired by recent success on applying deep learning to computer vision and medical problems, a deep-learning based method for crack detection is proposed in this paper. A supervised deep convolutional neural network is trained to classify each image patch in the collected images. Quantitative evaluation conducted on a data set of 500 images of size 3264 χ 2448, collected by a low-cost smart phone, demonstrates that the learned deep features with the proposed deep learning framework provide superior crack detection performance when compared with features extracted with existing hand-craft methods.
Aim: Collisions between wildlife and vehicles are recognized as one of the major causes of mortality for many species. Empirical estimates of road mortality show that some species are more likely to ...be killed than others, but to what extent this variation can be explained and predicted using intrinsic species characteristics remains poorly understood. This study aims to identify general macroecological patterns associated with road mortality and generate spatial and species-level predictions of risks. Location: Brazil. Time period: 2001–2014. Major taxa: Birds and mammals. Methods: We fitted trait-based random forest regression models (controlling for survey characteristics) to explain 783 empirical road mortality rates from Brazil, representing 170 bird and 73 mammalian species. Fitted models were then used to make spatial and species-level predictions of road mortality risk in Brazil, considering 1,775 birds and 623 mammals that occur within the continental boundaries of the country. Results: Survey frequency and geographical location were key predictors of observed rates, but mortality was also explained by the body size, reproductive speed and ecological specialization of the species. Spatial predictions revealed a high potential standardized (per kilometre of road) mortality risk in Amazonia for birds and mammals and, additionally, a high risk in Southern Brazil for mammals. Given the existing road network, these predictions mean that >8 million birds and >2 million mammals could be killed per year on Brazilian roads. Furthermore, predicted rates for all Brazilian endotherms uncovered potential vulnerability to road mortality of several understudied species that are currently listed as threatened by the International Union for Conservation of Nature. Conclusion: With a rapidly expanding global road network, there is an urgent need to develop improved approaches to assess and predict road-related impacts. This study illustrates the potential of trait-based models as assessment tools to gain a better understanding of the correlates of vulnerability to road mortality across species, and as predictive tools for difficult-to-sample or understudied species and areas.
Aim
The road network is increasing globally but the consequences of roadkill on the viability of wildlife populations are largely unknown. We provide a framework that allows us to estimate how risk ...of extinction of local populations increases due to roadkill and to generate a global assessment that identifies which mammalian species are most vulnerable to roadkill and the areas where they occur.
Location
Global.
Time period
1995–2015.
Major taxa studied
Terrestrial mammals.
Methods
We introduce a framework to quantify the effect of roadkill on terrestrial mammals worldwide that includes three steps: (a) compilation of roadkill rates to estimate the fraction of a local population killed on the roads, (b) prediction of population risk of extinction based on observed roadkill rates (for a target group of species of conservation concern and non‐threatened species with high roadkill rates), and (c) global assessment of vulnerability to roadkill for 4,677 terrestrial mammalian species estimated using phylogenetic regression models that link extinction risk to demographic parameters.
Results
We identified four populations among the 70 species in the target group that could become extinct in 50 years if observed roadkill levels persist in the study areas: maned wolf Chrysocyon brachyurus (Brazil), little spotted cat Leopardus tigrinus (Brazil), brown hyena Hyaena brunnea (Southern Africa) and leopard Panthera pardus (North India). The global assessment revealed roadkill as an added risk for 2.7% (n = 124) terrestrial mammals, including 83 species Threatened or Near Threatened. We identified regions of concern that have species vulnerable to roadkill with high road densities in areas of South Africa, central and Southeast Asia, and the Andes.
Main conclusions
Our framework revealed populations of threatened species that require special attention and can be incorporated into management and planning strategies informing road managers and conservation agencies.
The developing world has been faced with high rates of unemployment, exacerbated by extended enforced lockdowns due to the pandemic. Pressure is mounting for drastic intervention to accelerate ...economic growth and to provide employment opportunities. Most of these countries are faced with inadequate road transport facilities in support of economic growth. The construction of high-order roads in support of economic growth requires high degrees of compliance with limited opportunities for increased labour content. However, many of the existing surfaced roads are notoriously lacking periodic preventative maintenance operations needed to preserve the integrity of road surfaces to protect pavement structures against water ingress and resultant rapid deterioration. This article demonstrates the ability of available, proven nanotechnologies to restore the water-resistant properties of already compromised road surfacings. It is shown that traditionally used road products can substantially be improved (in terms of strength properties and resistance to environmental factors) through the addition of applicable nanotechnology modifiers. These modified products can be applied at ambient temperatures, ideally suited for labour intensive applications as demonstrated, showing several examples of actual applications. A combination of modified existing technologies is recommended to partially restore severely compromised road surfacings, especially applicable to secondary and tertiary urban road networks. The implementation of the recommended restoration programmes can go a long way towards road asset preservation, while simultaneously addressing the urgent need for rapid employment generation.
•Analysing Scholarly research on road safety in the context of low- and middle-income countries (LMIC).•Providing a scoping review of road safety research in LMIC (more than 2600 research ...items).•Contrasting the general literature of road safety with that of the LMIC.•Analysing patterns of authorships and co-authorships in road safety research in LMIC contexts.•Identifying trends, knowledge gaps and challenges of road safety research in LMIC.
Road users in low- and middle-income countries (LMICs) are overrepresented in road trauma statistics. Despite the relative success of many high-income countries (HICs) in reducing deaths on their roads, not much tangible progress has been made in LMICs. Also, on the research front, the vast majority of road safety knowledge has been emerging from institutes of HICs. Considering significant differences in driving culture, legislation, and traffic law enforcement between LMICs and HICs, it seems essential that research on road safety within LMICs intensifies beyond the existing rate to produce the much-needed local knowledge and to develop initiatives that meet their safety needs and upgrade their practices. To facilitate this, here, the landscape and temporal trends of road safety research in LMICs are analysed while contrasting them with those of the general scholarly literature on road safety. It is estimated that slightly less than 10% of the road safety research has been undertaken in the contexts of LMICs, which is extremely disproportionate considering the fact that most road traffic deaths and injuries occur in LMICs. Questionnaire-based research on socio-psychological aspects of driving, cycling, and walking as well as statistical modelling of road crash data seem to have made up the dominant focus of LMIC researchers within the recent years. Areas of road safety research that are underrepresented in LMIC studies are also identified in this work. Patterns of authorship and co-authorship in LMIC studies are also analysed at the level of countries, organisations, and authors. It is hoped that this effort can contribute to further invigoration of road safety research in LMICs and to highlighting the current knowledge gaps, while also giving better recognition to active road safety researchers of LMICs, and thereby, prompting more international collaborations in this domain.
► Impact area analysis approach for identifying critical links in large-scale networks. ► Consequences of a link closure are evaluated in its local impact area. ► Effects of demand variations and ...travelers’ risk-taking behaviour are investigated. ► Case studies using two real-world road networks are conducted.
To assess the vulnerability of congested road networks, the commonly used full network scan approach is to evaluate all possible scenarios of link closure using a form of traffic assignment. This approach can be computationally burdensome and may not be viable for identifying the most critical links in large-scale networks. In this study, an “impact area” vulnerability analysis approach is proposed to evaluate the consequences of a link closure within its impact area instead of the whole network. The proposed approach can significantly reduce the search space for determining the most critical links in large-scale networks. In addition, a new vulnerability index is introduced to examine properly the consequences of a link closure. The effects of demand uncertainty and heterogeneous travellers’ risk-taking behaviour are explicitly considered. Numerical results for two different road networks show that in practice the proposed approach is more efficient than traditional full scan approach for identifying the same set of critical links. Numerical results also demonstrate that both stochastic demand and travellers’ risk-taking behaviour have significant impacts on network vulnerability analysis, especially under high network congestion and large demand variations. Ignoring their impacts can underestimate the consequences of link closures and misidentify the most critical links.
Road detection and centerline extraction from very high-resolution (VHR) remote sensing imagery are of great significance in various practical applications. Road detection and centerline extraction ...operations depend on each other, to a certain extent. The road detection constrains the appearance of the centerline, and the centerline enhances the linear features of the road detection. However, most of the previous works have addressed these two tasks separately and have not considered the symbiotic relationship between them, making it difficult to obtain smooth and complete roads. In this paper, a novel multi-scale and multi-task deep learning framework for automatic road extraction (MSMT-RE) is proposed to build the relationship between them and simultaneously complete the road detection and centerline extraction tasks. U-Net is selected as the basic network for multi-task learning due to its strong ability to preserve spatial details. Multi-scale feature integration is also applied in the framework to increase the robustness of the feature extraction. Meanwhile, an adaptive loss function is introduced to solve the problems of roads taking up a small percentage of the training samples, and the fact that the positive samples of the two tasks are unbalanced. Finally, experiments were conducted on two public road data sets and two large images from Google Earth, and the proposed framework was compared with other state-of-the-art deep learning-based road extraction methods, both quantitatively and qualitatively. The proposed approach outperformed all the compared methods, confirming its advantages in automatic road extraction.
Purpose. Off-road tires (OTR) for mining and earthmoving applications which are specially developed for extreme mine site conditions can take 1020% of transportation costs. Up to 15% of OTR content ...is natural rubber produced from a rubber tree known as hevea brasiliensis. A significant number of OTR fail before the target life built in by tire manufacturers. This has certain negative impacts on the environment and wildlife due to deforestation effect. Thus, the purpose of the work is to increase the durability or, in the terminology of the operators, the mileage of the OTR. Methodology. The current study represents analyses of new modern digital technology for monitoring the mining haul road condition within a site study on Bogatyr Komir coal mine in North Kazakhstan. The influence of operating conditions was controlled, i.e. road quality, temperature conditions, tire pressure, and other parameters on tire life. Findings. The results show the effectiveness of digital technologies and the possibility of extending the life of tires by following the recommendations of the system in a timely manner. As a result of the analysis of temperature regimes and pressure in tires, especially in summer conditions, recommendations for rational operating conditions were determined, which makes it possible to increase the durability of tires. Originality. Modern operational digital methodology for monitoring open pit mines road condition defines ton kilometer per hour (TKPH) indicator per every trip, which provides on-time information for road design and maintenance. Practical value. Growing worldwide demand is driving the development of the mining industry. In the future, more and more fields with a low content of produced raw materials will be put into operation. The development of deposits with a low content of a useful product means that more rock mass must be moved in order for these developments to be profitable. Transportation costs in low-grade mines can be as high as 70% of mining costs due to more haul trucks and longer roads that need to be commissioned. Thus, an increase in the durability of OTR can give a great economic effect not only in the conditions of the Bogatyr Komir quarry under consideration but also in other mining and processing plants.
Summary
Recently, even the road industry has been involved in an evolutionary process, inspired by the novel ‘Industry 4.0’. This transition moves the attention towards big‐data and Internet of ...Things concepts, determining novel data flows and relevant management efforts. In smart roads, modern instrumented vehicles and networks of sensors will provide frequent helpful measures (traffic, weather, condition, accidents, mechanical performance, etc.), with reduced efforts and costs. However, this process is currently at the preliminary phases and several issues raise. Since the management and elaboration of huge amounts of data represent a relevant novel issue, in this study, an original web platform for collection and analysis of road performance data is proposed. This platform can acquire and process several data classes and support maintenance activity planning. This paper focuses on pavement maintenance, to provide a reliable decision support tool for road agencies, alternatively feedable by modern survey equipment and, in future, widespread sensors (when effective smart road sensors are installed on the main highways). The platform has been tested, in a preliminary form, on an existing motorway, considering high‐performance survey systems data, with interesting and positive results.
When driving on mountain roads, the drivers bear the dynamic load from the road environment. The sudden change of driving load will produce great safety risks and even lead to traffic accidents. In ...order to explore the driving risk of two‐lane road in mountainous areas, it is necessary to analyze the change of driving load from a quantitative point of view. Using the road driving experiment, 10 male drivers were selected according to the experimental road conditions, collect the driver's visual and heart rate indicators, analyze the driver's physiological indicators, and study the driver's driving load evolution process on the mountain road. Combined with the classification of road environment, a driving load evaluation model based on catastrophe progression method is constructed. The driving load is divided into four states: low load state, affordable state, chaotic state and high load state. It is found that most sections of mountain roads are in a low load and bearable state. In semi closed and closed environment, the driving load is in a chaotic state, which affects the driving safety. The evaluation model can be used to evaluate the traffic safety of road environment.
(1) The study found that the pupil area change rate, heart beat change rate and eyelid closure have good consistency for characterizing the driver's load. (2) In mountainous road, the driving load is different under different alignment conditions. The driving load is the largest when turning left, the second when turning right, and the smallest when going straight. (3) According to the catastrophe theory, when the road space type is different, the driving load is different. In the canopy space, the driving load is in a high state.
PDF
