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Recent technological advancements and other related factors and trends are contributing to the production of an astoundingly large and rapidly accelerating collection of data, or ‘Big Data’. This data now allows us to examine urban and regional phenomena in ways that were previously not possible. Despite the tremendous potential of big data for regional science, its use and application in this context is fraught with issues and challenges. This book brings together leading contributors to present an interdisciplinary, agenda-setting and action-oriented platform for research and practice in the urban and regional community. This book provides a comprehensive, multidisciplinary and cutting-edge perspective on big data for regional science. Chapters contain a collection of research notes contributed by experts from all over the world with a wide array of disciplinary backgrounds. The content is organized along four themes: sources of big data; integration, processing and management of big data; analytics for big data; and, higher level policy and programmatic considerations. As well as concisely and comprehensively synthesising work done to date, the book also considers future challenges and prospects for the use of big data in regional science. Big Data for Regional Science provides a seminal contribution to the field of regional science and will appeal to a broad audience, including those at all levels of academia, industry, and government.
This book gathers together innovative research and practical findings relating to urban mobility transformation. It is especially intended to provide academicians, researchers, practitioners and decision makers with effective strategies and techniques that can support urban mobility in a sustainable way. The chapters, which report on contributions presented at the 5th Conference on Sustainable Urban Mobility, held virtually on June 17-19, 2020, from Greece, cover the thematic areas of: social networks and traveler behavior; applications of technologies in transportation and big data analytics; transport infrastructure and traffic management; and transportation modeling and impact assessment. Special attention is given to public transport and demand responsive systems, electromobility, micromobility and automated vehicles. The book addresses the challenges of the near future, highlighting the importance of knowledge transfer, and it is intended to foster communication among universities, industries and public administration.
The problems of urban traffic in the industrially developed countries have been at the top of the priority list for a long time. While making a critical contribution to the economic well being of those countries, transportation systems in general and highway traffic in particular, also have detrimental effects which are evident in excessive congestion, high rates of accidents and severe pollution problems. Scientists from different disciplines have played an important role in the development and refinement of the tools needed for the planning, analysis, and control of urban traffic networks. In the past several years, there were particularly rapid advances in two areas that affect urban traffic: 1. Modeling of traffic flows in urban networks and the prediction of the resulting equilibrium conditions; 2. Technology for communication with the driver and the ability to guide him, by providing him with useful, relevant and updated information, to his desired destination.
This book presents research advances in intelligent transportation and smart city in detail, mainly focusing on green traffic and urban utility tunnels, presented at the 5th International Symposium for Intelligent Transportation and Smart City (ITASC 2022) held at Tongji University, Shanghai, on May 20-21, 2022. It is also branch of the International Symposium on Autonomous Decentralized Systems (ISADS) 2023. Due to rapid development in the domain of intelligent transportation and smart city, many popular topics are included, such as the 2BMW system (Bus, Bike, Metro and Walking), transportation safety and environment protection, urban utility design and application, the application of BIM in the city design. This book collects papers with high quality, including some authoritative scholars and most experienced engineers’ latest achievements, which will provide guidance to those both in universities and entrepreneurs in the field of transportation and urban planning. The first conference in the ITASC series was held in 2013 as a workshop of the International Symposium on Autonomous Decentralized System (ISADS) in Mexico City. The second to fourth were held in May 2015, 2017 and 2019, respectively, in Tongji University, Shanghai.
This book starts from the relationship between urban built environment and travel behavior and focuses on analyzing the origin of traffic phenomena behind the data through multi-source traffic big data, which makes the book unique and different from the previous data-driven traffic big data analysis literature. This book focuses on understanding, estimating, predicting, and optimizing mobility patterns. Readers can find multi-source traffic big data processing methods, related statistical analysis models, and practical case applications from this book. This book bridges the gap between traffic big data, statistical analysis models, and mobility pattern analysis with a systematic investigation of traffic big data’s impact on mobility patterns and urban planning.
This is the third of a series of research volume of papers from the Business and Information Technologies global research network. The group includes 20 partners from 16 countries, who conduct studies on the impact of new information and communication technologies on business practice, industry structure, and economic change. The book presents a unique longitudinal and cross-sectional view of technology adoption and business practice across a diverse set of countries and economies. It appears that there are some commonalities with respect to patterns of technology adoption, but also significant differences across countries. Furthermore, innovative practices can arise in every country, and have the potential to be applied in other countries. The identical survey carried out in different countries enables benchmarking and accurate comparisons across those markets. It is also extremely broad in its coverage of business practice in terms of functions and performance.
These proceedings gather selected papers from the 9th International Conference on Green Intelligent Transportation Systems and Safety, held in Guilin, China on July 1-3, 2018. They feature cutting-edge studies on Green Intelligent Mobility Systems, the guiding motto being to achieve “green, intelligent, and safe transportation systems.” The contributions presented here can help promote the development of green mobility and intelligent transportation technologies to improve interconnectivity, resource sharing, flexibility and efficiency. Given its scope, the book will benefit researchers and engineers in the fields of Transportation Technology and Traffic Engineering, Automotive and Mechanical Engineering, Industrial and System Engineering, and Electrical Engineering alike.
Currently, Intelligent Transportation Systems (ITS), is revolutionizing the transportation industry. ITS incorporates advanced Internet of Things (IoT) technologies to implement "Smart City". These technologies produce tremendous amount of real time data from diverse sources that can be used to solve transportation problems. In this thesis, I focus on one such problem, traffic congestion in urban areas. A road segment affected by traffic affects the surrounding road segments. This is obvious. However, over a period of time, other roads not necessarily close in proximity to the congested road segment may also be affected. The congestion is not stationary. It is dynamic and it spreads. I address this issue by first formulating a similarity function using ideas from network theory. Using this similarity function, I then cluster the road points affected by traffic using affinity propagation clustering, a distributed message passing algorithm. Finally, I predict the effect of traffic on this cluster using long-short term memory neural network model. I evaluate and show the feasibility of my proposed clustering and prediction algorithm during peak and non-peak hours on open source traffic data set.
This book chiefly focuses on urban traffic, an area supported by massive amounts of data. The application of big data to urban traffic provides strategic and technical methods for the multi-directional and in-depth observation of complex adaptive systems, thus transforming conventional urban traffic planning and management methods. Sharing valuable insights into how big data can be applied to urban traffic, it offers a valuable asset for information technicians, traffic engineers and traffic data analysts alike.