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Sharing mobilities : questioning our right to the city in the collaborative economy
\"This book examines contemporary urban sharing mobilities, such as shared and public forms of urban mobility. Tracing the social and economic history of sharing mobilities and examining contemporary case studies of mobility sharing services, it raises questions about what these changes mean for access to public transport in the city\"-- Provided by publisher.
Book
Uberland : how algorithms are rewriting the rules of work
\"A silent cultural revolution is reshaping how we will work for generations to come--and Uber is leading it. The Silicon Valley start-up has become a juggernaut of the sharing economy, promising drivers the opportunity to be entrepreneurs but managing them with algorithms and treating them like consumers. The billion-dollar global behemoth has upended our expectations about what it means to work in a society mediated by digital circuitry. Technology ethnographer Alex Rosenblat shares her award-winning research on how algorithm managers are creating profound social and cultural shifts. Uber is now affecting everything from family life, management practices, and urban planning to racial equality campaigns and labor rights initiatives. Based on Rosenblat's firsthand experience of riding 5,000 miles with Uber drivers, daily visits to online forums from 2014 to 2018, and face-to-face discussions with senior Uber employees, Uberland goes beyond the headlines and deciphers the complex relationship between algorithms and workers. Technology enables Uber to call labor 'consumption' and thereby skirt regulations, experiment with working conditions, and mislead the public about driver earnings. Using algorithms and rhetoric, Uber and other big tech companies are blurring the line between worker and consumer and rewriting the rules of law and society\"-- Provided by publisher.
Book
Inside the sharing economy
Purpose This paper aims to investigate what motivates consumers to adopt one of the emerging mobile applications of the sharing economy, ridesharing application. Using social cognitive theory as the theoretical framework, this study develops a value adoption model to illustrate important factors that influence adoption of ridesharing applications. Design/methodology/approach Based on prior literature, a quantitative methodology was adopted using a survey questionnaire that allows for the measurement of the nine constructs contained in the hypothesized theoretical model. Data collected from a sample of 314 respondents in Beijing, China provided the foundation for the examination of the proposed relationships in the model. Findings First, the results indicate that self-efficacy is a fundamental factor that has a direct effect on consumers' perceptions of value and an indirect effect on behavioral intentions. Second, the study demonstrates that functional value, emotional value and social value are critical antecedents of overall perceived value of ridesharing applications. On the other hand, learning effort and risk perception are not significant perceived costs for consumers in adopting ridesharing applications. Research limitations/implications Although typical adopters of internet applications constitute a significant portion of younger consumers, the use of a college student sample in this study may affect the generalizability of the results. Practical implications The findings provide critical insight into consumer motivations behind adoption of ridesharing applications specifically, and for sharing economy platforms in general. Originality/value This study provides important theoretical implications for innovation adoption research through an empirical examination of the relationship between personal, environmental and behavioral factors in a framework of social cognitive theory.
Journal Article
Super pumped : the battle for Uber
\"A New York Times technology correspondent presents the dramatic rise and fall of Uber, set against the rapid upheaval in Silicon Valley during the mobile era. In June 2017, Travis Kalanick, the hard-charging CEO of Uber, was ousted in a boardroom coup that capped a brutal year for the transportation giant. Uber had catapulted to the top of the tech world yet for many came to symbolize everything wrong with Silicon Valley. In the tradition of Brad Stone's Everything Store and John Carreyrou's Bad Blood, award-winning investigative reporter Mike Isaac's Super Pumped delivers a gripping account of Uber's rapid rise, its pitched battles with taxi unions and drivers, the company's toxic internal culture and the bare-knuckle tactics it devised to overcome obstacles in its quest for dominance. Based on hundreds of interviews with current and former Uber employees, along with previously unpublished documents, Super Pumped is a page-turning story of ambition and deception, obscene wealth and bad behavior, that explores how blistering technological and financial innovation culminated in one of the most catastrophic twelve-month periods in American corporate history\"-- Provided by publisher.
Book
Global scenarios of resource and emission savings from material efficiency in residential buildings and cars
Material production accounts for a quarter of global greenhouse gas (GHG) emissions. Resource-efficiency and circular-economy strategies, both industry and demand-focused, promise emission reductions through reducing material use, but detailed assessments of their GHG reduction potential are lacking. We present a global-scale analysis of material efficiency for passenger vehicles and residential buildings. We estimate future changes in material flows and energy use due to increased yields, light design, material substitution, extended service life, and increased service efficiency, reuse, and recycling. Together, these strategies can reduce cumulative global GHG emissions until 2050 by 20–52 Gt CO
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-eq (residential buildings) and 13–26 Gt CO
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e-eq (passenger vehicles), depending on policy assumptions. Next to energy efficiency and low-carbon energy supply, material efficiency is the third pillar of deep decarbonization for these sectors. For residential buildings, wood construction and reduced floorspace show the highest potential. For passenger vehicles, it is ride sharing and car sharing.
Material production accounts for a quarter of global greenhouse gas emissions. Here, the authors show that resource efficiency and circular-economy strategies can allow for cumulative emission reductions of 20–52 Gt CO2-eq from residential buildings and 13–26 Gt CO2e-eq from cars by 2050.
Journal Article
The sharing economy and the relevance for transport
\"The Sharing Economy and the Relevance for Transport, Volume Four in the Advances in Transport Policy and Planning series, assesses both successful and unsuccessful practices and policies from around the world. Individual chapters in this new release include Cars and cities in the sharing economy, The future of public transport within the sharing economy, Sharing vehicles and sharing rides in real time: opportunities for self-driving fleets, Car parking in the future, Car share's impact and future, Bike Share, and much more.\"-- Publisher's website.
Book
Modeling an enhanced ridesharing system with meet points and time windows
With the rising of e-hailing services in urban areas, ride sharing is becoming a common mode of transportation. This paper presents a mathematical model to design an enhanced ridesharing system with meet points and users' preferable time windows. The introduction of meet points allows ridesharing operators to trade off the benefits of saving en-route delays and the cost of additional walking for some passengers to be collectively picked up or dropped off. This extension to the traditional door-to-door ridesharing problem brings more operation flexibility in urban areas (where potential requests may be densely distributed in neighborhood), and thus could achieve better system performance in terms of reducing the total travel time and increasing the served passengers. We design and implement a Tabu-based meta-heuristic algorithm to solve the proposed mixed integer linear program (MILP). To evaluate the validation and effectiveness of the proposed model and solution algorithm, several scenarios are designed and also resolved to optimality by CPLEX. Results demonstrate that (i) detailed route plan associated with passenger assignment to meet points can be obtained with en-route delay savings; (ii) as compared to CPLEX, the meta-heuristic algorithm bears the advantage of higher computation efficiency and produces good quality solutions with 8%~15% difference from the global optima; and (iii) introducing meet points to ridesharing system saves the total travel time by 2.7%-3.8% for small-scale ridesharing systems. More benefits are expected for ridesharing systems with large size of fleet. This study provides a new tool to efficiently operate the ridesharing system, particularly when the ride sharing vehicles are in short supply during peak hours. Traffic congestion mitigation will also be expected.
Journal Article
Wild ride : inside Uber's quest for world domination
\"Traces the story of Uber's rapid growth from its murky origins to its plans for expansion into radically different industries. The company is fighting local competitors and lawmakers for markets around the world; it has already faced riots and protests in cities like Paris, Rio de Janeiro, and Mumbai. It fought, and lost, an expensive and grueling battle against rival Didi in China. Uber has also poached entire departments from top research universities in a push to build the first self-driving car and possibly replace the very drivers it's worked so hard to recruit. Uber is in the headlines every day, but so much about its past and its future plans are still unknown to the public\"-- Provided by publisher.
Book
Addressing the minimum fleet problem in on-demand urban mobility
Information and communication technologies have opened the way to new solutions for urban mobility that provide better ways to match individuals with on-demand vehicles. However, a fundamental unsolved problem is how best to size and operate a fleet of vehicles, given a certain demand for personal mobility. Previous studies
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either do not provide a scalable solution or require changes in human attitudes towards mobility. Here we provide a network-based solution to the following ‘minimum fleet problem’, given a collection of trips (specified by origin, destination and start time), of how to determine the minimum number of vehicles needed to serve all the trips without incurring any delay to the passengers. By introducing the notion of a ‘vehicle-sharing network’, we present an optimal computationally efficient solution to the problem, as well as a nearly optimal solution amenable to real-time implementation. We test both solutions on a dataset of 150 million taxi trips taken in the city of New York over one year
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. The real-time implementation of the method with near-optimal service levels allows a 30 per cent reduction in fleet size compared to current taxi operation. Although constraints on driver availability and the existence of abnormal trip demands may lead to a relatively larger optimal value for the fleet size than that predicted here, the fleet size remains robust for a wide range of variations in historical trip demand. These predicted reductions in fleet size follow directly from a reorganization of taxi dispatching that could be implemented with a simple urban app; they do not assume ride sharing
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, nor require changes to regulations, business models, or human attitudes towards mobility to become effective. Our results could become even more relevant in the years ahead as fleets of networked, self-driving cars become commonplace
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An optimal computationally efficient solution to the problem of finding the minimum taxi fleet size using a vehicle-sharing network is presented.
Journal Article