Data Science And Simulation In Transportation Research

Given its effective techniques and theories from various sources and fields, data science is playing a vital role in transportation research and the consequences of the inevitable switch to electronic vehicles. This fundamental insight provides a step towards the solution of this important challenge. Data Science and Simulation in Transportation Research highlights entirely new and detailed spatial-temporal micro-simulation methodologies for human mobility and the emerging dynamics of our society. Bringing together novel ideas grounded in big data from various data mining and transportation science sources, this book is an essential tool for professionals, students, and researchers in the fields of transportation research and data mining.

"This book highlights entirely new and detailed spatial-temporal micro-simulation methodologies for human mobility and the emerging dynamics of our society, offering novel ideas grounded in big data from various data mining and transportation science sources"--

Transportation is a combination of systems that presents a variety of challenges often too intricate to be addressed by conventional parametric methods. Increasing data availability and recent advancements in machine learning provide new methods to tackle challenging transportation problems. This textbookis designed for college or graduate-level students in transportation or closely related fields to study and understand fundamentals in machine learning. Readers will learn how to develop and apply various types of machine learning models to transportation-related problems. Example applications include traffic sensing, data-quality control, traffic prediction, transportation asset management, traffic-system control and operations, and traffic-safety analysis. Introduces fundamental machine learning theories and methodologies Presents state-of-the-art machine learning methodologies and their incorporation into transportationdomain knowledge Includes case studies or examples in each chapter that illustrate the application of methodologies andtechniques for solving transportation problems Provides practice questions following each chapter to enhance understanding and learning Includes class projects to practice coding and the use of the methods

This comprehensive textbook/reference provides an in-depth overview of the key aspects of transportation analysis, with an emphasis on modeling real transportation systems and executing the models. Topics and features: presents comprehensive review questions at the end of each chapter, together with detailed case studies, useful links, references and suggestions for further reading; supplies a variety of teaching support materials at the book’s webpage on Springer.com, including a complete set of lecture slides; examines the classification of models used for multimodal transportation systems, and reviews the models and evaluation methods used in transportation planning; explains traffic assignment to road networks, and describes computer simulation integration platforms and their use in the transportation systems sector; provides an overview of transportation simulation tools, and discusses the critical issues in the design, development and use of the simulation models.

Simulation Approaches in Transportation Analysis: Recent Advances and Challenges presents the latest developments in transport simulation, including dynamic network simulation and micro-simulation of people’s movement in an urban area. It offers a collection of the major simulation models that are now in use throughout the world; it illustrates each model in detail, examines potential problems, and points to directions for future development. The reader will be able to understand the functioning, applicability, and usefulness of advanced transport simulation models. The material in this book will be of wide use to graduate students and practitioners as well as researchers in the transportation engineering and planning fields.

In recent years, the transport simulation of large road networks has become far more rapid and detailed, and many exciting developments in this field have emerged. Within this volume, the authors describe the simulation of automobile, pedestrian, and rail traffic coupled to new applications, such as the embedding of traffic simulation into driving simulators, to give a more realistic environment of driver behavior surrounding the subject vehicle. New approaches to traffic simulation are described, including the hybrid mesoscopic-microscopic model and floor-field agent-based simulation. Written by an invited panel of experts, this book addresses students, engineers, and scholars, as well as anyone who needs a state-of-the-art overview of transport simulation today.

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

Transportation Big Data: Theory and Methods is centered around big data theory and methods. As big data is now a key topic in transport because the volume of data has increased exponentially due to the growth in the amount of traffic (all modes) and detectors, this book provides a structured analysis of commonly used methods for handling transportation big data. It is supported by a wealth of transportation engineering examples with codes. The book offers a concise, yet comprehensive description key techniques and important tools in transportation big data analysis. Covers big data applications in transportation engineering in real-world scenarios Shows how to select different machine learning algorithms for processing, analyzing, and modeling transportation data Provides an overview of the fundamental concepts of machine learning and how classical algorithms can be applied to transportation-related problems Provides an overview of Python's basic syntax and commonly used modules, enabling practical data analysis and modeling tasks using Python

Mobility Patterns, Big Data and Transport Analytics provides a guide to the new analytical framework and its relation to big data, focusing on capturing, predicting, visualizing and controlling mobility patterns - a key aspect of transportation modeling. The book features prominent international experts who provide overviews on new analytical frameworks, applications and concepts in mobility analysis and transportation systems. Users will find a detailed, mobility ‘structural’ analysis and a look at the extensive behavioral characteristics of transport, observability requirements and limitations for realistic transportation applications and transportation systems analysis that are related to complex processes and phenomena. This book bridges the gap between big data, data science, and transportation systems analysis with a study of big data’s impact on mobility and an introduction to the tools necessary to apply new techniques. The book covers in detail, mobility ‘structural’ analysis (and its dynamics), the extensive behavioral characteristics of transport, observability requirements and limitations for realistic transportation applications, and transportation systems analysis related to complex processes and phenomena. The book bridges the gap between big data, data science, and Transportation Systems Analysis with a study of big data’s impact on mobility, and an introduction to the tools necessary to apply new techniques. Guides readers through the paradigm-shifting opportunities and challenges of handling Big Data in transportation modeling and analytics Covers current analytical innovations focused on capturing, predicting, visualizing, and controlling mobility patterns, while discussing future trends Delivers an introduction to transportation-related information advances, providing a benchmark reference by world-leading experts in the field Captures and manages mobility patterns, covering multiple purposes and alternative transport modes, in a multi-disciplinary approach Companion website features videos showing the analyses performed, as well as test codes and data-sets, allowing readers to recreate the presented analyses and apply the highlighted techniques to their own data

Dynamics and Stochasticity in Transportation Systems: Solutions for Transportation Network Modeling breaks new ground on the topics, providing consistent and comprehensive coverage of steady state equilibrium and dynamic assignment within a common strategy. The book details the most recent advances in network assignment, including day-to-day and within-day dynamics, providing a solid foundation to help transportation planners solve transient overload and other problems. Users will find a book that fills the gap in knowledge with its description on how to use and employ the latest dynamic network models for evaluation of traffic and transport demand interventions. This book demystifies the many different dynamic traffic assignment approaches and requires no previous knowledge on the part of the reader. All results are fully described and proven, thus eliminating the need to seek out other references. The skills described will appeal to transportation professionals, researchers and graduate students alike. Presents a consistent and comprehensive theory on steady state equilibrium assignment and day-to-day dynamic assignment models within a common framework Describes and solves modeling calculations in detail, with no need to reference other sources Includes numerical and graphical examples, text boxes and summaries at the end of each chapter to help readers better understand theoretical components Includes primary mathematical tools necessary for each dynamic model, easing comprehension