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La 4ème de couverture indique : "Il suffit aujourd’hui d’un clic pour commander des sushis, donner son linge à laver ou faire une rencontre amoureuse. L’ère numérique nous fait miroiter un monde nouveau, plus rapide, plus simple, plus moderne. Des plateformes comme Uber ou Deliveroo brandissent leur ambition de révolutionner notre quotidien. Oui, mais à quel prix ? Chaque jour, à travers la planète, des millions de travailleurs œuvrent pour le compte de ces start-up devenues des multinationales. Jeunes, précaires, souvent migrants ou sans-papiers, ils font aujourd’hui le travail qu’un robot effectuera probablement demain à leur place. Tandis que ces entreprises se débattent pour atteindre la rentabilité, les travailleurs "ubérisés", privés de nombreux droits et toujours plus mal payés, forment une masse invisible, que l’on croyait jusqu’alors silencieuse. Cette enquête leur rend la parole et décortique les effets de l’ubérisation sur nos vies et notre modèle de société – une immersion édifiante dans les coulisses d’un mythe devenu, pour beaucoup, une cruelle réalité."

Express service --- Self-employed --- Internet --- Mobile commerce --- Information technology --- Ridesharing --- Employees --- Economic aspects --- Uber (Firm) --- Uberisation --- Travail précaire. --- Conditions sociales. --- Service industries --- Precarious employment --- Travail précaire. --- Economic aspects. --- Express service - Employees --- Internet - Economic aspects --- Information technology - Economic aspects

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"Adaptation Urbanism and Resilient Communities outlines and explains adaptation urbanism as a theoretical framework for understanding and evaluating resilience projects in cities and relates it to pressing contemporary policy issues related to urban climate change mitigation and adaption. Through a series of detailed case studies, this book uncovers the promise and tensions of a new wave of resilient communities in Europe (Copenhagen, Rotterdam, and London), and the U.S. (New Orleans and South Florida). In addition, best practice projects in Amsterdam, Barcelona, Delft, Utrecht, and Vancouver are also examined. The authors highlight how these communities are reinventing the role of streets and connecting public spaces in adapting to and mitigating climate change through green/blue infrastructure planning, maintaining and enhancing sustainable transportation options, and struggling to ensure equitable development for all residents. The case studies demonstrate that while there are some more universal aspects to encouraging adaptation urbanism, there are also important local characteristics that need to be both acknowledged and celebrated to help local communities thrive in the era of climate change. The book also provides key policy lessons and provides a roadmap for future research in adaptation urbanism. Advancing resilience policy discourse through multidisciplinary framework this work will be of great interest to students of urban planning, geography, transportation, landscape architecture, and environmental studies, as well as resilience practitioners around the world"--

Environmental planning --- Economic geography --- urban planning --- community development --- local transit --- City planning --- Local transit --- Community development. --- Environmental aspects. --- Planning. --- Community development --- Regional development --- Economic assistance, Domestic --- Social planning --- City transit --- Mass transit --- Municipal transit --- Public transit --- Rapid transit --- Transit systems --- Urban transit --- Transportation --- Ridesharing --- Citizen participation --- Government policy

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The purpose of this Special Issue is to create an an academic platform whereby high-quality research papers are published on the applications of innovative AI algorithms to transportation planning and operation. The authors present their original research articles related to the applications of AI or machine-learning techniques to transportation planning and operation. The topics of the articles encompass traffic surveillance, traffic safety, vehicle emission reduction, congestion management, traffic speed forecasting, and ride sharing strategy.

autoencoder --- deep learning --- traffic volume --- vehicle counting --- CycleGAN --- bottleneck and gridlock identification --- gridlock prediction --- urban road network --- long short-term memory --- link embedding --- traffic speed prediction --- traffic flow centrality --- reachability analysis --- spatio-temporal data --- artificial neural network --- context-awareness --- dynamic pricing --- reinforcement learning --- ridesharing --- supply improvement --- taxi --- preventive automated driving system --- automated vehicle --- traffic accidents --- deep neural networks --- vehicle GPS data --- driving cycle --- micro-level vehicle emission estimation --- link emission factors --- MOVES --- black ice --- CNN --- prevention