Research on Water Seepage Detection Technology of Tunnel Asphalt Pavement Based on Deep Learning and Digital Image Processing

In order to improve the traffic safety of the tunnel pavement and reduce the impact of water seepage on the pavement structure, a convolutional neural network (CNN) model is established based on image detection technology to realize the identification, classification and statistics of pavement seepage. First, compared with the MobileNet network model, the deep learning model EfficientNet network model was built, and the accuracy of the two models was analyzed for pavement seepage recognition. The F1 Score was introduced to evaluate the accuracy and comprehensive performance of the two models for different types of seepage characteristics. Then the three gray processing methods, six threshold segmentation methods, as well as three filtering methods were compared to extract water seepage characteristics of digital image. Finally, based on the processed image, a calculation method of water seepage area was proposed to identify the actual asphalt pavement water seepage. The result shows that the recognition accuracy of the EfficientNet network model in the training set and the validation set are 99.85% and 97.53%, respectively, and the prediction accuracy is 98.00%. The accuracy of pavement water seepage recognition and prediction is better than the MobileNet network model. Using the cvtColor function for gray processing, using THRESH_BINARY for threshold segmentation, and using a combination of median filtering and morphological opening operations for image noise reduction can effectively extract water seepage characteristics. The water seepage area calculated by the proposed method has a small difference with the actual water seepage area, and the effect is agreeable.

Improving Information Interaction between the Metallurgical Plant and Rail Operators

The current situation of development of the world economy presupposes intense competition in both external and internal markets. Under these conditions, it becomes more and more obvious that the growth of profits and, accordingly, further development of companies will be carried out not so much through expansion, but through improved service for customers, an increase in the range of goods and services offered, a better product quality and a decrease in production costs.The main role in optimisation of technological processes is currently played by digital transformation of production. The introduction of advanced information technologies is of great importance for all global companies, since the enhanced development of information systems results in improvement of business processes, better safety, and environmental friendliness.International studies show that the use of modern information technologies in transport industry is necessary to improve traffic safety, reduce environmental impact, increase the efficiency of the transportation process.The Russian mining and metallurgical sector, along with the oil and gas industry, makes a significant contribution to development of the country. Complex production technology, a large volume of traffic, hazardous and dangerous working conditions for personnel necessitate development of a digital environment to increase labour productivity and the volume of products.The objective of the research is to study the possibility of using information control and forecasting systems for solving technical, technological, and organisational problems of industrial railways of metallurgical plants.Based on comparative analysis, general scientific and mathematical research methods and the study of the role of information systems in digital transformation of production process, the authors suggest a methodology for creating a stochastic model for predicting the arrival of unit trains at an enterprise, and consider development trends in digital transformation of industrial transport.

Dynamics of the Rolling Stock and the Choice of Parameters of Vibration Dampers

Modern railway rolling stock should meet requirements regarding comfort (maximum travel speed with minimum vibrations of wagons, noiselessness of movement, etc.).To eliminate the influence of dynamic loads, rolling stock is equipped with vibration dampers. The objective of the work is to select the parameters of the vibration dampers of rolling stock, depending on its characteristics, to ensure the due indicators of comfort and safety of transportation of passengers and goods by rail. To achieve this objective, applied methods of mathematical modelling were based on numerical programming of operation of dynamic systems. The indicators of vibration dampers are evaluated according to the results of studies of the dynamics of the rolling stock (in particular, of vibration protection rates).Assessment of dynamic state of the rolling stock implies application of methods of mathematical and physical modelling, which include the development of a physical and mathematical model, a calculation algorithm, and computer programming. The study of the mathematical model by numerical methods makes it possible to carry out a multifactorial experiment using a large number of input parameters (factors) and to select the characteristics of vibration dampers that are optimal for the conditions under consideration.To solve dynamic problems, the harmonic perturbation model, which is the most widespread, was specified in the form of a sinusoid with a period corresponding to the rail length.A quantitative assessment of the vibration process (frequency, amplitude) makes it possible to identify the main processes occurring in the system under consideration under various types of external load. The introduced assumptions related to rigidity, mobility and geometric immutability of the system allow determining the methods for obtaining a mathematical model and considering the vibrations as flat ones.The equations were solved in MathCad Prime 4.0 package using the Runge–Kutta method with automatic step selection. The subsequent study of the properties of the dynamic system was carried out by changing the resistance parameter of dampers of the first stage of spring suspension, while recording the values of the amplitude of the vibrations of the system and the period.The analysis of the results has shown that the vibration period of the body and bogies under any changes in the resistance parameter of the damper remains unchanged, while rational parameters of resistance of axle box dampers have been revealed for specified indicators. Hydraulic vibration dampers with the revealed parameters used on rolling stock help to reduce wear and damageability of running gears, improve ride comfort and traffic safety, as well as to reduce repair and maintenance costs.

Design and Implementation of Intelligent Vehicle Control System Based on Internet of Things and Intelligent Transportation

With the improvement of urbanization and the continuous expansion of transportation scale, traffic problem has become an important problem in our life. How to ensure traffic safety has become the key issue for the government to implement social management. Nowadays, Internet of Things (IOT) technology is widely used in the industrial technology field. It will have a great impact on human production and life. Intelligent transportation system is a research field involving many high and new technologies. This paper proposes an intelligent transportation system based on Internet of Things technology. This paper presents the optimal design structure of intelligent transportation system based on Internet of Things technology. The experimental results show that the intelligent transportation system can effectively realize the information interaction between the vehicle and the control center and understand the road conditions in advance. At the same time, the intelligent transportation system can improve the driving speed of vehicles on the road, make effective use of resources, reduce economic losses during vehicle operation, and reduce air pollution caused by gasoline emission.

Traffic Risk Modelling and Analysis under Airport-Like Simulation Environment

Traffic safety plays a crucial role in the development of autonomous vehicles which attracts significant attention in the community. It is a challenge task to ensure autonomous vehicle safety under varied traffic environment interference, especially for airport-like closed-loop conditions. To that aim, we analyze autonomous vehicle safety at typical roadway conditions and traffic state constraints (e.g., car-following state at different speed distributions) by simulating the airport-like traffic conditions. The experimental results suggest that traffic collision risk is in a positive relationship with the speed difference and distance among adjacent vehicles. More specifically, the autonomous vehicle may collide with neighbors when the time to collision (TTC) indicator is lower than 4 s, and vice versa. The research findings can help both research community and practioners obtain additional information for improving traffic safety for autonomous vehicles.

Overview of Big Data and Its Visualization

Big Data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. This chapter discusses what Big Data is and its characteristics, and how this information revolution of Big Data is transforming our lives and the new technology and methodologies that have been developed to process data of these huge dimensionalities. This chapter discusses the components of the Big Data stack interface, categories of Big Data analytics software and platforms, descriptions of the top 20 Big Data analytics software. Big Data visualization techniques are discussed with real data from fatality analysis reporting system (FARS) managed by National Highway Traffic Safety Administration (NHTSA) of the United States Department of Transportation. Big Data web-based visualization software are discussed that are both JavaScript-based and user-interface-based. This chapter also discusses the challenges and opportunities of using Big Data and presents a flow diagram of the 30 chapters within this handbook.

Research on possibilities for monitoring stability of adjacent slopes to transport routes

Development of human society also implies modernization, respectively extension a road and railway transport structures. From this perspective, in order to achieve performance in the field of traffic safety, actions are also required to monitor the slopes, tailings dumps (active or greened) adjacent to transport routes where there are uncertainties regarding their stability. Ignoring stability and landslides can lead to loss of life, as well as significant material damage. Thus, the paper mainly addresses the issue of monitoring the slopes adjacent to road and rail transport routes where there are uncertainties regarding the control of landslides, as well as possibility of alerting before this occurs. From the multitude of possible solutions to be applied in the field, an equipment was developed and realized, which was experienced in real working conditions, and the results confirm validity of assumptions and certify the operation.

Semi and Nonparametric Conditional Probability Density, a Case Study of Pedestrian Crashes

Background: Kernel-based methods have gained popularity as employed model residual’s distribution might not be defined by any classical parametric distribution. Kernel-based method has been extended to estimate conditional densities instead of conditional distributions when data incorporate both discrete and continuous attributes. The method often has been based on smoothing parameters to use optimal values for various attributes. Thus, in case of an explanatory variable being independent of the dependent variable, that attribute would be dropped in the nonparametric method by assigning a large smoothing parameter, giving them uniform distributions so their variances to the model’s variance would be minimal. Objectives: The objective of this study was to identify factors to the severity of pedestrian crashes based on an unbiased method. Especially, this study was conducted to evaluate the applicability of kernel-based techniques of semi- and nonparametric methods on the crash dataset by means of confusion techniques. Methods: In this study, two non- and semi-parametric kernel-based methods were implemented to model the severity of pedestrian crashes. The estimation of the semi-parametric densities is based on the adoptive local smoothing and maximization of the quasi-likelihood function, which is similar somehow to the likelihood of the binary logit model. On the other hand, the nonparametric method is based on the selection of optimal smoothing parameters in estimation of the conditional probability density function to minimize mean integrated squared error (MISE). The performances of those models are evaluated by their prediction power. To have a benchmark for comparison, the standard logistic regression was also employed. Although those methods have been employed in other fields, this is one of the earliest studies that employed those techniques in the context of traffic safety. Results: The results highlighted that the nonparametric kernel-based method outperforms the semi-parametric (single-index model) and the standard logit model based on the confusion matrices. To have a vision about the bandwidth selection method for removal of the irrelevant attributes in nonparametric approach, we added some noisy predictors to the models and a comparison was made. Extensive discussion has been made in the content of this study regarding the methodological approach of the models. Conclusion: To summarize, alcohol and drug involvement, driving on non-level grade, and bad lighting conditions are some of the factors that increase the likelihood of pedestrian crash severity. This is one of the earliest studies that implemented the methods in the context of transportation problems. The nonparametric method is especially recommended to be used in the field of traffic safety when there are uncertainties regarding the importance of predictors as the technique would automatically drop unimportant predictors.