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"ROAD CONDITIONS"
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Road Condition Monitoring Using Vehicle Built-in Cameras and GPS Sensors: A Deep Learning Approach
Road authorities worldwide can leverage the advances in vehicle technology by continuously monitoring their roads’ conditions to minimize road maintenance costs. The existing methods for carrying out road condition surveys involve manual observations using standard survey forms, performed by qualified personnel. These methods are expensive, time-consuming, infrequent, and can hardly provide real-time information. Some automated approaches also exist but are very expensive since they require special vehicles equipped with computing devices and sensors for data collection and processing. This research aims to leverage the advances in vehicle technology in providing a cheap and real-time approach to carry out road condition monitoring (RCM). This study developed a deep learning model using the You Only Look Once, Version 5 (YOLOv5) algorithm that was trained to capture and categorize flexible pavement distresses (FPD) and reached 95% precision, 93.4% recall, and 97.2% mean Average Precision. Using vehicle built-in cameras and GPS sensors, these distresses were detected, images were captured, and locations were recorded. This was validated on campus roads and parking lots using a car featured with a built-in camera and GPS. The vehicles’ built-in technologies provided a more cost-effective and efficient road condition monitoring approach that could also provide real-time road conditions.
Journal Article
Road Condition Detection and Emergency Rescue Recognition Using On-Board UAV in the Wildness
Unmanned aerial vehicle (UAV) vision technology is becoming increasingly important, especially in wilderness rescue. For humans in the wilderness with poor network conditions and bad weather, this paper proposes a technique for road extraction and road condition detection from video captured by UAV multispectral cameras in real-time or pre-downloaded multispectral images from satellites, which in turn provides humans with optimal route planning. Additionally, depending on the flight altitude of the UAV, humans can interact with the UAV through dynamic gesture recognition to identify emergency situations and potential dangers for emergency rescue or re-routing. The purpose of this work is to detect the road condition and identify emergency situations in order to provide necessary and timely assistance to humans in the wild. By obtaining a normalized difference vegetation index (NDVI), the UAV can effectively distinguish between bare soil roads and gravel roads, refining the results of our previous route planning data. In the low-altitude human–machine interaction part, based on media-pipe hand landmarks, we combined machine learning methods to build a dataset of four basic hand gestures for sign for help dynamic gesture recognition. We tested the dataset on different classifiers, and the best results show that the model can achieve 99.99% accuracy on the testing set. In this proof-of-concept paper, the above experimental results confirm that our proposed scheme can achieve our expected tasks of UAV rescue and route planning.
Journal Article
China's Belt and Road Initiative, the Eurasian landbridge, and the new mega-regionalism
\"This contribution to the World Scientific series on the Belt and Road Initiative focuses on the overland connections west from China, the Silk Road Economic Belt component of the BRI. It emphasizes the economic underpinning of the Belt in the market-driven creation of the Eurasian Landbridge and the linking of regional value chains. A fundamental economic driver behind this is the twenty-first century evolution of international value chains, in which China plays a major role, and their transformation by new trade technologies. Finer fragmentation of production and wider scanning for participants in value chains underlie the need for common, preferably global, regulation of new trade technologies and the emergence of mega-regional trade agreements (and China's response to such agreements). Thus, the Eurasian part of the Belt and Road Initiative must be seen in conjunction with China's growing role in the twenty-first-century global economy. Especially since the 2016 US presidential election, these connections have become entwined with China's reactions to criticisms of the Belt and Road Initiative and China's recognition of the benefits of more nuanced economic diplomacy to find common ground with other economic powers, notably the European Union and signatories of the Comprehensive and Progressive Agreement for Trans-Pacific Partnership.\"--Back cover.
Book
A new car-following model accounting for varying road condition
In this paper, we develop a new car-following model with consideration of varying road condition based on the empirical data. Firstly, we explore the effects of road condition on uniform flow from analytical and numerical perspectives. The results indicate that road condition has great influences on uniform flow, i.e., good road condition can enhance the velocity and flow and their increments will increase when road condition becomes better; bad road conditions will reduce the velocity and flow and their reductions will increase when road condition turns worse. Secondly, we study the effects of road conditions on the starting and braking processes. The numerical results show that good road condition will speed up the two processes and that bad road condition will slow down the two processes. Finally, we study the effects of road condition on small perturbation. The numerical results indicate that the stop-and-go phenomena resulted by small perturbation will become more serious when the road condition becomes better.
Journal Article
Application of Ultrasonic Sensors in Road Surface Condition Distinction Methods
The number of accidents involving elderly individuals has been increasing with the increase of the aging population, posing increasingly serious challenges. Most accidents are caused by reduced judgment and physical abilities, which lead to severe consequences. Therefore, studies on support systems for elderly and visually impaired people to improve the safety and quality of daily life are attracting considerable attention. In this study, a road surface condition distinction method using reflection intensities obtained by an ultrasonic sensor was proposed. The proposed method was applied to movement support systems for elderly and visually impaired individuals to detect dangerous road surfaces and give an alarm. The method did not perform well in previous studies of puddle detection, because the alert provided by the method did not enable users to avoid puddles. This study extended the method proposed by previous studies with respect to puddle detection ability. The findings indicate the effectiveness of the proposed method by considering four road surface conditions. The proposed method could detect puddle conditions. The effectiveness of the proposed method was verified in all four conditions, since users could differentiate between road surface conditions and classify the conditions as either safe or dangerous.
Journal Article
Statistical Analysis of Urban Traffic Flow Using Deep Learning
In recent years, urbanization has brought about challenges such as population growth, increased demand for traffic, and traffic congestion. To address the need for accurate traffic condition statistics, this paper proposed an improved method that combines graph convolutional network (GCN) and long short-term memory network (LSTM) model for forecasting and statistics of traffic conditions. Through the modeling and analysis of urban road conditions and traffic flow, the combination of GCN model and LSTM model enabled more precise prediction of traffic flow trends. Experiments were carried out on the actual traffic data set of Cangzhou, Hebei. The results demonstrated that the proposed method achieved high accuracy and reliability in predicting traffic flow. By using the LSTM model to improve the GCN model, it effectively adapts to changes in urban traffic conditions while providing dependable predictions.
Journal Article
Appraisal on Different Sustainable Road Stabilization Techniques for Different Road Condition and Materials
Road condition in term of road surface condition depends on the subgrade soil strength. Therefore, a weak subgrade condition requires improvements in stabilization. Soil stabilization is the alteration of one or more geotechnical properties to create an improved soil material possessing the desired engineering properties. The main purpose of the soil stabilization is to increase the shear strength of an existing ground condition to enhance its load-bearing capacity, achieve a desired improved permeability and enhance the durability of the soil to resistance to the process of weathering, and traffic usage among others. Three method in soil stabilization considered in this study are chemical admixture, mechanical and geosynthetics methods. The difference in soil stabilization methods depends on the different road surface conditions. Road condition with weakness subgrade is more appropriate and economical in used stabilization chemical method. In this paper, discussing the road condition requires the ground improvement through soil stabilization
Journal Article