Social Cost Guarantees in Smart Route Guidance

We model and study the problem of assigning traffic in an urban road network infrastructure. In our model, each driver submits their intended destination and is assigned a route to follow that minimizes the social cost (i.e., travel distance of all the drivers). We assume drivers are strategic and try to manipulate the system (i.e., misreport their intended destination and/or deviate from the assigned route) if they can reduce their travel distance by doing so. Such strategic behavior is highly undesirable as it can lead to an overall suboptimal traffic assignment and cause congestion. To alleviate this problem, we develop moneyless mechanisms that are resilient to manipulation by the agents and offer provable approximation guarantees on the social cost obtained by the solution. We then empirically test the mechanisms studied in the paper, showing that they can be effectively used in practice in order to compute manipulation resistant traffic allocations

What’s in it for me? Incentive-compatible route coordination of crowdsourced resources

With the recent trend in crowdsourcing, i.e., using the power of crowds to assist in satisfying demand, the pool of resources suitable for GeoPresence-capable systems has expanded to include already roaming devices, such as mobile phones, and moving vehicles. We envision an environment, in which the motion of these crowdsourced mobile resources is coordinated, according to their preexisting schedules to satisfy geo-temporal demand on a mobility field. In this paper, we propose an incentive compatible route coordination mechanism for crowdsourced resources, in which participating mobile agents satisfy geo-temporal requests in return for monetary rewards. We define the Flexible Route Coordination (FRC) problem, in which an agent’s flexibility is exploited to maximize the coverage of a mobility field, with an objective to maximize the revenue collected from satisfied paying requests. Given that the FRC problem is NP-hard, we define an optimal algorithm to plan the route of a single agent on a graph with evolving labels, then we use that algorithm to define a 1/2-approximation algorithm to solve the problem in its general model, with multiple agents. Moreover, we define an incentive compatible, rational, and cash-positive payment mechanism, which guarantees that an agent’s truthfulness about its flexibility is an ex-post Nash equilibrium strategy. Finally, we analyze the proposed mechanisms theoretically, and evaluate their performance experimentally using real mobility traces from urban environments.Supported in part by NSF Grants, #1430145, #1414119, #1347522, #1239021, and #1012798

Incentive-compatible route coordination of crowdsourced resources

Technical ReportWith the recent trend in crowdsourcing, i.e., using the power of crowds to assist in satisfying demand, the pool of resources suitable for GeoPresen-ce-capable systems has expanded to include already roaming devices, such as mobile phones, and moving vehicles. We envision an environment, in which the motion of these crowdsourced mobile resources is coordinated, according to their preexisting schedules to satisfy geo-temporal demand on a mobility field. In this paper, we propose an incentive compatible route coordination mechanism for crowdsourced resources, in which participating mobile agents satisfy geo-temporal requests in return for monetary rewards. We define the Flexible Route Coordination (FRC) problem, in which an agent’s flexibility is exploited to maximize the coverage of a mobility field, with an objective to maximize the revenue collected from satisfied paying requests. Given that the FRC problem is NP-hard, we define an optimal algorithm to plan the route of a single agent on a graph with evolving labels, then we use that algorithm to define a 1-approximation algorithm to solve the 2 problem in its general model, with multiple agents. Moreover, we define an incentive compatible, rational, and cash-positive payment mechanism, which guarantees that an agent’s truthfulness about its flexibility is an ex-post Nash equilibrium strategy. Finally, we analyze the proposed mechanisms theoretically, and evaluate their performance experimentally using real mobility traces from urban environments

Incentive compatible route coordination of crowdsourced resources and its application to GeoPresence-as-a-Service

With the recent trend in crowdsourcing, i.e., using the power of crowds to assist in satisfying demand, the pool of resources suitable for GeoPresen- ce-capable systems has expanded to include already roaming devices, such as mobile phones, and moving vehicles. We envision an environment, in which the motion of these crowdsourced mobile resources is coordinated, according to their preexisting schedules to satisfy geo-temporal demand on a mobility field. In this paper, we propose an incentive compatible route coordination mechanism for crowdsourced resources, in which participating mobile agents satisfy geo-temporal requests in return for monetary rewards. We define the Flexible Route Coordination (FRC) problem, in which an agent's exibility is exploited to maximize the coverage of a mo- bility field, with an objective to maximize the revenue collected from sat- isfied paying requests. Given that the FRC problem is NP-hard, we define an optimal algorithm to plan the route of a single agent on a graph with evolving labels, then we use that algorithm to define a 1 2 -approximation algorithm to solve the problem in its general model, with multiple agents. Moreover, we define an incentive compatible, rational, and cash-positive payment mechanism, which guarantees that an agent's truthfulness about its exibility is an ex-post Nash equilibrium strategy. Finally, we analyze the proposed mechanisms theoretically, and evaluate their performance experimentally using real mobility traces from urban environments.Supported in part by NSF Grants, #1430145, #1414119, #1347522, #1239021, and #1012798

The Green Investment Report: The Ways and Means to Unlock Private Finance for Green Growth

This report is a first step by the Green Growth Action Alliance to deliver on the G20 Leaders' request. It aims to provide a common point of reference to guide policy-makers, financial institutions and investors as they seek to better understand, and address, the global gap in green investment. This report documents and synthesizes the best available green investment data, research and case studies from a number of leading organizations, including Bloomberg New Energy Finance, the Climate Policy Initiative, the International Energy Agency, the Organization of Economic Cooperation and Development, the United Nations Environment Programme, the World Bank Group and the World Resources Institute, and provides important messages for different groups of stakeholders. New analysis is also presented on clean-energy asset finance flows, the findings of which can be used to guide investment decisions and priorities in other sectors

D3.3 Business models report

RECIPROCITY aims to transform European cities into climate-resilient and connected, multimodal nodes for smart and clean mobility. The project's innovative four-stage replication approach is designed to showcase and disseminate best practices for sustainable urban development and mobility. As part of this project, the present business model report (D3.3) provides an overview of innovative urban mobility business models that could be tailored to cities in the RECIPROCITY replication ecosystem. The work developed was based upon the work carried-out in WP1-2-4, and aimed to collect and derive the business model patterns for urban mobility and propose a business model portfolio that encourage cross-sector collaboration and create an integrated mobility system. This report is therefore addressed to cities and local authorities that have to meet mobility challenges (i.e. high costs and low margin, broad set of partners, competing with private car) by providing new services to activate and accelerate a sustainable modal shift. It also targets other stakeholders interested in business model concepts applied to cities

Smart Cities and M³: Rapid Research, Meaningful Metrics and Co-Design

The research described in this paper is undertaken under the banner of the smart city, a concept that captures the way urban spaces are re-made by the incursion of new technology. Much of smart is centred on converting everyday activities into data, and using this data to generate knowledge mediated by technology. Ordinary citizens, those that may have their lives impacted by the technology, usually are not properly involved in the ‘smartification’ process. Their perceptions, concerns and expectations should inform the conception and development of smart technologies at the same extent. How to engage general public with smart cities research is the central challenge for the Making Metrics Meaningful (MMM) project. Applying a rapid participatory method, ‘Imagine’ over a five-month period (March – July) the research sought to gain insights from the general public into novel forms of information system innovation. This brief paper describes the nature of the accelerated research undertaken and explores some of the themes which emerged in the analysis. Generic themes, beyond the remit of an explicit transport focus, are developed and pointers towards further research directions are discussed. Participatory methods, including engaging with self- selected transport users actively through both picture creation and programmatically specific musical ‘signatures’ as well as group discussion, were found to be effective in eliciting users’ own concerns, needs and ideas for novel information systems