Front Cover -- About Island Press -- Subscribe -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgments -- 1. Will the Transportation Revolutions Improve Our Lives-- or Make Them Worse? -- 2. Electric Vehicles: Approaching the Tipping Point -- 3. Shared Mobility: The Potential of Ridehailing and Pooling -- 4. Vehicle Automation: Our Best Shot at a Transportation Do-Over? -- 5. Upgrading Transit for the Twenty-First Century -- 6. Bridging the Gap between Mobility Haves and Have-Nots -- 7. Remaking the Auto Industry -- 8. The Dark Horse: Will China Win the Electric, Automated, Shared Mobility Race? -- Epilogue -- Notes -- About the Contributors -- Index -- IP Board of Directors

This book explores the opportunities and challenges of the sharing economy and innovative transportation technologies with regard to urban mobility. Written by government experts, social scientists, technologists and city planners from North America, Europe and Australia, the papers in this book address the impacts of demographic, societal and economic trends and the fundamental changes arising from the increasing automation and connectivity of vehicles, smart communication technologies, multimodal transit services, and urban design. The book is based on the Disrupting Mobility Summit held in Cambridge, MA (USA) in November 2015, organized by the City Science Initiative at MIT Media Lab, the Transportation Sustainability Research Center at the University of California at Berkeley, the LSE Cities at the London School of Economics and Politics and the Innovation Center for Mobility and Societal Change in Berlin.

As ride-hailing becomes more common in cities, and the effect of this increased demand on fixed infrastructure manifests itself, authorities increasingly seek transportation network company (TNC) service data to understand (and potentially regulate) demand and service response. Despite the increase in ride-hailing and subsequent research into its determinants, there remains little research on shared ride-hail trips and the spatial distribution of trip demand across demographic and land use variables. Using Chicago as a case study, shared TNC trip data from 2019 was used to estimate the count and ratio of shared ride services, based on built environment, demographic, location, time of day, and trip details. Findings reveal that trip length, day of week designation, density of pedestrian and multi-modal infrastructure, and underlying socioeconomic characteristics of the origin zones influence the proportion and count of shared ride-hail trips. Of concern is that those using transit or active modes may be taking more ride-hailing trips, but these Chicago-region results indicate that the provision of pedestrian infrastructure and remoteness to transit stops result in fewer shared trips

The various sharing initiatives seen in the Nordic countries over the last years within transportation, housing/accommodation, sharing/renting of smaller capital goods and personal services could yield considerable benefits for consumers due to better quality and/or lower prices of the services. They also have a potential for emissions reductions of CO2 and local pollutants. However, savings from lower prices could lead to increased emissions from increased demand of the services (particularly transport) and increased spending on other goods and services. Depending on how consumers spend their savings, these changes could partly, wholly or more than offset the initial emission reductions. The impacts on overall CO2 emissions depend on whether the emissions are taxed, part of the emissions trading system EU ETS or not regulated at all.

This book presents a remarkable collection of chapters covering a wide range of topics in the areas of Computer Vision, both from theoretical and application perspectives. It gathers the proceedings of the Computer Vision Conference (CVC 2019), held in Las Vegas, USA from May 2 to 3, 2019. The conference attracted a total of 371 submissions from pioneering researchers, scientists, industrial engineers, and students all around the world. These submissions underwent a double-blind peer review process, after which 120 (including 7 poster papers) were selected for inclusion in these proceedings. The book's goal is to reflect the intellectual breadth and depth of current research on computer vision, from classical to intelligent scope. Accordingly, its respective chapters address state-of-the-art intelligent methods and techniques for solving real-world problems, while also outlining future research directions. Topic areas covered include Machine Vision and Learning, Data Science, Image Processing, Deep Learning, and Computer Vision Applications.