Transportation systems involve complex interactions with traffic demand, loading, and environmental factors, which result in non-linearities in system performance. The structural and functional conditions of a system determine its efficiency in meeting mobility demands. However, budget constraints impose serious limitations on actively monitoring these system responses to maintain reliability. Due to the intrinsic complexity of system responses and limited data availability, it is necessary to develop robust machine-learning models that can accurately characterize system performance and predict future states, based on which actions can be undertaken to maximize their performance under optimal settings. This dissertation focuses on the development and application of machine-learning strategies in evaluating and predicting the conditions of transportation systems like infrastructures using non-destructive evaluation (NDE) techniques, and road networks using real-time traffic data. Multi-dimensional NDE data that capture damage-specific signatures are interpreted to quantify the degree of damage and structural integrity in terms of condition ratings. Several spectral-based autonomous signal classification mechanisms and probabilistic sequential models like hidden Markov models, which perform well with limited data availability, have also been explored. Additionally, this dissertation contributes to the functional performance estimation of networks in terms of macroscopic traffic variables by analyzing real-time traffic datasets. In particular, it focuses on solving problems like traffic prediction and uncertainty quantification using advanced deep learning models, which are essential for efficient traffic operations and optimal control. Data-driven modeling-specific issues like data scarcity, synthetic data generation and transferability, and generalizability of the models on out-of-distribution datasets have been discussed in the context of both NDE and traffic data.

Data-Driven Solutions to Transportation Problems explores the fundamental principle of analyzing different types of transportation-related data using methodologies such as the data fusion model, the big data mining approach, computer vision-enabled traffic sensing data analysis, and machine learning. The book examines the state-of-the-art in data-enabled methodologies, technologies and applications in transportation. Readers will learn how to solve problems relating to energy efficiency under connected vehicle environments, urban travel behavior, trajectory data-based travel pattern identification, public transportation analysis, traffic signal control efficiency, optimizing traffic networks network, and much more. Synthesizes the newest developments in data-driven transportation science Includes case studies and examples in each chapter that illustrate the application of methodologies and technologies employed Useful for both theoretical and technically-oriented researchers

Data Analytics for Intelligent Transportation Systems provides in-depth coverage of data-enabled methods for analyzing intelligent transportation systems that includes detailed coverage of the tools needed to implement these methods using big data analytics and other computing techniques. The book examines the major characteristics of connected transportation systems, along with the fundamental concepts of how to analyze the data they produce. It explores collecting, archiving, processing, and distributing the data, designing data infrastructures, data management and delivery systems, and the required hardware and software technologies. Users will learn how to design effective data visualizations, tactics on the planning process, and how to evaluate alternative data analytics for different connected transportation applications, along with key safety and environmental applications for both commercial and passenger vehicles, data privacy and security issues, and the role of social media data in traffic planning. Includes case studies in each chapter that illustrate the application of concepts covered Presents extensive coverage of existing and forthcoming intelligent transportation systems and data analytics technologies Contains contributors from both leading academic and commercial researchers Explains how to design effective data visualizations, tactics on the planning process, and how to evaluate alternative data analytics for different connected transportation applications

Intelligent Transportation Systems: Functional Design for Economical and Efficient Traffic Management provides practical guidance on the efficient use of resources in the design of ITS. The author explains how functional design alternatives can meet project objectives and requirements with optimal cost effectiveness and clarifies how transportation planning and traffic diversion principles relate to functional ITS device selections and equipment locations. Methodologies for translating objectives to functional device types, determining device deployment densities and determining the best placement of CCTV cameras and message signs are provided, as are models for evaluating the benefits of design alternatives based on traffic conditions. Readers will learn how to reduce recurrent congestion, improve incident clearance time in non-recurrent congestion, provide real-time incident information to motorists, and leverage transportation management center data for lane control through important new active transportation and demand management (ATDM) methods. Finally, the author examines exciting developments in connected vehicle technologies, exploring their potential to greatly improve safety, mobility and energy efficiency. This resource will greatly benefit all ITS designers and managers and is of pivotal importance for operating agencies performing evaluations to justify operational funding and system expansions.

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.

This book explores the application of breakthrough technologies to improve transportation performance. Transportation systems represent the “blood vessels” of a society, in which people and goods travel. They also influence people’s lives and affect the liveability and sustainability of our cities. The book shows how emergent technologies are able to monitor the condition of the structure in real time in order to schedule the right moment for maintenance activities an so reduce the disturbance to users. This book is a valuable resource for those involved in research and development in this field. Part I discusses the context of transportation systems, highlighting the major issues and challenges, the importance of understating human factors that could affect the maintenance operations and the main goals in terms of safety standards. Part II focuses on process-oriented innovations in transportation systems; this section stresses the importance of including design parameters in the planning, offering a comparison between risk-based and condition-based maintenance and, lastly, showing applications of emergent technologies. Part III goes on to reflect on the technical-oriented innovations, discussing the importance of studying the physical phenomena that are behind transportation system failures and problems. It then introduces the general trend of collecting and analyzing big data using real-world cases to evaluate the positive and negative aspects of adopting extensive smart sensors for gathering information on the health of the assets. The last part (IV) explores cultural and behavioural changes, and new knowledge management methods, proposing novel forms of maintenance and vocational training, and introduces the need for radical new visions in transportation for managing unexpected events. The continuous evolution of maintenance fields suggests that this compendium of “state-of-the-art” applications will not be the only one; the authors are planning a collection of cutting-edge examples of transportation systems that can assist researchers and practitioners as well as students in the process of understanding the complex and multidisciplinary environment of maintenance engineering applied to the transport sector.

Demand for Emerging Transportation Systems: Modeling Adoption, Satisfaction, and Mobility Patterns comprehensively examines the concepts and factors affecting user quality-of-service satisfaction. The book provides an introduction to the latest trends in transportation, followed by a critical review of factors affecting traditional and emerging transportation system adoption rates and user retention. This collection includes a rigorous introduction to the tools necessary for analyzing these factors, as well as Big Data collection methodologies, such as smartphone and social media analysis. Researchers will be guided through the nuances of transport and mobility services adoption, closing with an outlook of, and recommendations for, future research on the topic. This resource will appeal to practitioners and graduate students. Examines the dynamics affecting adoption rates for public transportation, vehicle-sharing, ridesharing systems and autonomous vehicles Covers the rationale behind travelers' continuous use of mobility services and their satisfaction and development Includes case studies, featuring mobility stats and contributions from around the world