An automatic method for generating multiple alignment alternatives for a railway bypass

This paper deals with the problem of designing a bypass on a railway line. Based on a geometrical model capable of determining automatically the need of major structures (bridges, tunnels, overpasses and underpasses), the optimal design of a railway bypass is formulated in the framework of Mixed Integer Non Linear Programming (MINLP), and it is solved with a numerical algorithm which provides different layout alternatives that are optimal solutions (local minima) from the economic point of view. The proposed method is tested on a case study with the aim of showing its practical usefulness as a support tool for engineers in order to accomplish the complex and time-consuming task to generate a set of initial alternatives for the design of a railway bypassThis research was funded by Ministerio de Ciencia e Innovación (Spain) grant number TED2021-129324B-I00, and by the collaboration agreement between Xunta de Galicia (Spain) and Universidade de Santiago de Compostela (Spain) which regulates the Specialization Campus “Campus Terra”. Additionally, the authors are grateful to Concello de Guitiriz (Spain) for financial support through the contract Optimal design of multiple alignment alternatives for a bypass on the railway line A Coruña-Palencia passing through Parga-Guitiriz (Lugo), ref. 2021-CP138 . Finally, third and fourth authors thank the support given by Xunta de Galicia (Spain) under research projects ref. ED341D R2016/023 and GI-1563ED431C2021/15, respectivelyS

Development of a new approach for predicting tram track degradation based on passenger ride/comfort data

These days tram as a type of the public transport system has become popular because of its attractive features such as road usage efficiency, low emission of pollutants, reduction in traffic congestion and efficiency in capital costs and maintenance expenses compared to private cars. For the case study, the Melbourne tram network, which is the longest tram network in the world, has been targeted. Melbourne tram system consists of 493 trams, 24 routes, and 1,763 tram stops. According to the operator of the Melbourne tram network, the total number of patronage in 2017-2018 was 206.3 million. In parallel with the annual increase in tram demand and patronage, tram infrastructure systems need to bear more stresses and traffic pressure. Track degradation is a common problem in the area of tram track infrastructure. One of the main aspects of track degradation is the presence of irregularity in track geometric parameters. In order to deal with degradation problems, tram track infrastructure maintenance management systems have been developed for design and implementation of maintenance works and renewal activities. Track degradation prediction models are the core and the main part of these management systems. Without accurately predicting the future condition of tram tracks, designing and providing preventive maintenance strategies are not feasible. In this research, the collected data which cover six sequential years (2010 to 2015) have been analysed and influencing parameters in tram track degradation have been identified. Gauge and twist were identified as the influencing track geometry parameters in the tram track degradation. Besides that, track surface and rail support as structural parameters were identified as significant parameters in prediction of future track geometry parameters and consequently tram track degradation. In order to develop tram track degradation prediction models and according to the successful experience of the previous studies, three types of prediction models including Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest Regression (RFR) models have been created. According to the results, RFR models provide better predictions in terms of the performance indicators including the coefficient of determination and Root Mean Squared Error (RMSE) compared to the ANN and SVM models. In this research, based on the Melbourne tram track dataset, a new track degradation index has been proposed. Track degradation indices can be used as an indicator of rail condition concerning the risk of damage or failure over a period of time. The index can be applied in establishing a sustainable tram track maintenance management system. The new index composed of two main parts including the mean value of the geometry deviation and the average differential geometry deviation. The proposed index has been compared with three major track geometry degradation indices. For this purpose, the predictability performance of the indices has been considered. In this regard, the Pearson correlation analysis was applied to previous and current values of the indices. According to the results, the correlation coefficient of the proposed index was higher than the other indices. The finding of the evaluation presented that the proposed index can be used as an effective measure for the assessment of the geometric condition of tram tracks. In this research, a new approach has been proposed to predict the tram track degradation were which is cost-effective and can be carried out repeatedly without imposing delay to tram services. Conventional approaches are mainly based on the previous track geometry parameters which have been discussed in this research. In the new approach, passenger ride comfort data or acceleration data has been used to predict the future condition of track geometry parameters which has been represented by the tram track degradation index. For developing the degradation prediction models, the previous models which have been used to predict the degradation based on the track geometry parameters were applied. The future degradation index has been targeted as the target variable and acceleration parameter besides the structural parameters have been used as the explanatory variables. According to the results of the evaluation, the RFR model can predict the future degradation index with approximately 10 percent higher R2 and 9 percent lower prediction error compared to other developed models. In this research two methods for predicting the future tram track degradation index, first was the method based on the previous track geometry parameters and the second was the method based on the acceleration data, have been presented. According to the results of the degradation index prediction based on the previous track geometry parameters, RMSE was 0.35 and R2 value was 0.95. On the other hand, for the prediction based on the acceleration data, RMSE was 1.04 and R2 value was 0.74. The comparison of these methods shows that although the prediction error has been increased and R2 value has been decreased in the latest method, the values of the performance indicators are still in acceptable ranges. These results imply that the prediction of tram track degradation based on the acceleration data can be considered as a reliable method along with conventional tram track degradation prediction method for maintaining tram tracks. The proposed method can provide more predictions of potential future faults by reducing inspection costs and inspection intervals

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways

Prospects for Electric Mobility: Systemic, Economic and Environmental Issues

The transport sector, which is currently almost completely based on fossil fuels, is one of the major contributors to greenhouse gas emissions. Heading towards a more sustainable development of mobility could be possible with more energy efficient automotive technologies such as battery electric vehicles. The number of electric vehicles has been increasing over the last decade, but there are still many challenges that have to be solved in the future. This Special Issue “Prospects for Electric Mobility: Systemic, Economic and Environmental Issues” contributes to the better understanding of the current situation as well as the future prospects and impediments for electro mobility. The published papers range from historical development of electricity use in different transport modes and the recent challenges up to future perspectives

Modeling and Simulation in Engineering

This book provides an open platform to establish and share knowledge developed by scholars, scientists, and engineers from all over the world, about various applications of the modeling and simulation in the design process of products, in various engineering fields. The book consists of 12 chapters arranged in two sections (3D Modeling and Virtual Prototyping), reflecting the multidimensionality of applications related to modeling and simulation. Some of the most recent modeling and simulation techniques, as well as some of the most accurate and sophisticated software in treating complex systems, are applied. All the original contributions in this book are jointed by the basic principle of a successful modeling and simulation process: as complex as necessary, and as simple as possible. The idea is to manipulate the simplifying assumptions in a way that reduces the complexity of the model (in order to make a real-time simulation), but without altering the precision of the results

Climate Change and Environmental Sustainability-Volume 2

Our world is facing many challenges, such as poverty, hunger, resource shortage, environmental degradation, climate change, and increased inequalities and conflicts. To address such challenges, the United Nations proposed the Sustainable Development Goals (SDG), consisting of 17 interlinked global goals, as the strategic blueprint of world sustainable development. Nevertheless, the implementation of the SDG framework has been very challenging and the COVID-19 pandemic has further impeded the SDG implementation progress. Accelerated efforts are needed to enable all stakeholders, ranging from national and local governments, civil society, private sector, academia and youth, to contribute to addressing this dilemma. This volume of the Climate Change and Environmental Sustainability book series aims to offer inspiration and creativity on approaches to sustainable development. Among other things, it covers topics of COVID-19 and sustainability, environmental pollution, food production, clean energy, low-carbon transport promotion, and strategic governance for sustainable initiatives. This book can reveal facts about the challenges we are facing on the one hand and provide a better understanding of drivers, barriers, and motivations to achieve a better and more sustainable future for all on the other. Research presented in this volume can provide different stakeholders, including planners and policy makers, with better solutions for the implementation of SDGs. Prof. Bao-Jie He acknowledges the Project NO. 2021CDJQY-004 supported by the Fundamental Research Funds for the Central Universities. We appreciate the assistance from Mr. Lifeng Xiong, Mr. Wei Wang, Ms. Xueke Chen and Ms. Anxian Chen at School of Architecture and Urban Planning, Chongqing University, China

Sustainable Mobility and Transport

This Special Issue is dedicated to sustainable mobility and transport, with a special focus on technological advancements. Global transport systems are significant sources of air, land, and water emissions. A key motivator for this Special Issue was the diversity and complexity of mitigating transport emissions and industry adaptions towards increasingly stricter regulation. Originally, the Special Issue called for papers devoted to all forms of mobility and transports. The papers published in this Special Issue cover a wide range of topics, aiming to increase understanding of the impacts and effects of mobility and transport in working towards sustainability, where most studies place technological innovations at the heart of the matter. The goal of the Special Issue is to present research that focuses, on the one hand, on the challenges and obstacles on a system-level decision making of clean mobility, and on the other, on indirect effects caused by these changes

Intelligent Transportation Related Complex Systems and Sensors

Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems. It includes twenty-four papers, which cover scientific concepts, frameworks, architectures and various other ideas on analytics, trends and applications of transportation-related data