71 research outputs found

    Spread like a virus. A model to assess the diffusion of dynamic ridesharing services

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    Dynamic ridesharing is a derivative of regular carpooling, which enables the formation of carpools on an as-needed basis, usually on very short notice the shared travel purpose also extends to a broad range of activities, beyond work or school. In this paper we propose a model to monitor the adoption of a dynamic ridesharing service, intended here as a mobile service that needs to achieve a critical mass to survive. Our theoretical model is inspired from the SIR model used in epidemiology to control the spread of an infectious virus. We test our model using real-data from two firms offering dynamic ridesharing services. Our model complements the view that innovative services evolve following an S-shaped curve, and it has practical relevance for managers and investors, who want to monitor and compare the evolution of competing firms in the field

    SOCIAL NETWORK INFLUENCE ON RIDESHARING, DISASTER COMMUNICATIONS, AND COMMUNITY INTERACTIONS

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    The complex topology of real networks allows network agents to change their functional behavior. Conceptual and methodological developments in network analysis have furthered our understanding of the effects of interpersonal environment on normative social influence and social engagement. Social influence occurs when network agents change behavior being influenced by others in the social network and this takes place in a multitude of varying disciplines. The overarching goal of this thesis is to provide a holistic understanding and develop novel techniques to explore how individuals are socially influenced, both on-line and off-line, while making shared-trips, communicating risk during extreme weather, and interacting in respective communities. The notion of influence is captured by quantifying the network effects on such decision-making and characterizing how information is exchanged between network agents. The methodologies and findings presented in this thesis will benefit different stakeholders and practitioners to determine and implement targeted policies for various user groups in regular, special, and extreme events based on their social network characteristics, properties, activities, and interactions

    An Interdisciplinary Survey on Origin-destination Flows Modeling: Theory and Techniques

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    Origin-destination~(OD) flow modeling is an extensively researched subject across multiple disciplines, such as the investigation of travel demand in transportation and spatial interaction modeling in geography. However, researchers from different fields tend to employ their own unique research paradigms and lack interdisciplinary communication, preventing the cross-fertilization of knowledge and the development of novel solutions to challenges. This article presents a systematic interdisciplinary survey that comprehensively and holistically scrutinizes OD flows from utilizing fundamental theory to studying the mechanism of population mobility and solving practical problems with engineering techniques, such as computational models. Specifically, regional economics, urban geography, and sociophysics are adept at employing theoretical research methods to explore the underlying mechanisms of OD flows. They have developed three influential theoretical models: the gravity model, the intervening opportunities model, and the radiation model. These models specifically focus on examining the fundamental influences of distance, opportunities, and population on OD flows, respectively. In the meantime, fields such as transportation, urban planning, and computer science primarily focus on addressing four practical problems: OD prediction, OD construction, OD estimation, and OD forecasting. Advanced computational models, such as deep learning models, have gradually been introduced to address these problems more effectively. Finally, based on the existing research, this survey summarizes current challenges and outlines future directions for this topic. Through this survey, we aim to break down the barriers between disciplines in OD flow-related research, fostering interdisciplinary perspectives and modes of thinking.Comment: 49 pages, 6 figure

    Sharing Economy Business Models : Addressing the design-implementation gap

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    Despite sharing being a long-practiced form of consumption, the concept ‘sharing economy’ has emerged only recently. New business models have proliferated, utilising technology to reduce transaction costs and facilitate shared access. Societal actors have taken interest in the sharing economy, to reduce resource consumption, foster social cohesion, and support the economy. However, sharing economy business models facilitate a wide array of consumption practices, including sharing, renting, borrowing, lending, bartering, swapping, trading, exchanging, gifting, buying second-hand, and even buying new goods. Past academic research and media attention tend to focus on unicorns such as Airbnb and Uber. There is greater need to explore the diverse permutations of business models within the sharing economy, especially considering sustainability.However, a gap exists between the design and successful implementation of sharing economy business models. This research aims to advance and structure knowledge about the sharing economy and sustainable business models, by using business modelling methods to study the design and implementation of sharing economy business models. Inspired by design science, this research engages in prescriptive theory-building and design- oriented research to construct and evaluate design artefacts. Incorporating data materials from people, documents, and literature, the research strategies of grounded theory and desk research are utilised to support methods for data collection and data analysis.The research proposes a prescriptive definition of the sharing economy as a socio-economic system that leverages technology to mediate two-sided markets, which facilitate temporary access to goods that are under- utilised, tangible, and rivalrous. From this, four design principles guide the formation of the sharing economy business model framework, which capture three value dimensions, sixteen business model attributes, and eighty- nine configuration options. This research proposes a coherent design theory to support the conceptualisation of sharing economy business models for sustainability.Additional artefacts are developed to support the successful implementation of these business models. First, business model patterns provide the justificatory knowledge to select relevant business model attributes in specific contexts. Then, a systematic framework measures the social impact of sharing platforms across four aspects – trust, empowerment, social justice, and inclusivity. Finally, organisational response strategies to COVID-19 are established in the sharing economy.The primary contribution of this research is conceptual, with additional modest methodological and empirical contributions. Furthermore, the artefacts are intended to be useful for research and practice, including scholars, entrepreneurs, managers, policymakers, investors, users, and concerned citizens

    Applications of biased-randomized algorithms and simheuristics in integrated logistics

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    Transportation and logistics (T&L) activities play a vital role in the development of many businesses from different industries. With the increasing number of people living in urban areas, the expansion of on-demand economy and e-commerce activities, the number of services from transportation and delivery has considerably increased. Consequently, several urban problems have been potentialized, such as traffic congestion and pollution. Several related problems can be formulated as a combinatorial optimization problem (COP). Since most of them are NP-Hard, the finding of optimal solutions through exact solution methods is often impractical in a reasonable amount of time. In realistic settings, the increasing need for 'instant' decision-making further refutes their use in real life. Under these circumstances, this thesis aims at: (i) identifying realistic COPs from different industries; (ii) developing different classes of approximate solution approaches to solve the identified T&L problems; (iii) conducting a series of computational experiments to validate and measure the performance of the developed approaches. The novel concept of 'agile optimization' is introduced, which refers to the combination of biased-randomized heuristics with parallel computing to deal with real-time decision-making.Las actividades de transporte y logística (T&L) juegan un papel vital en el desarrollo de muchas empresas de diferentes industrias. Con el creciente número de personas que viven en áreas urbanas, la expansión de la economía a lacarta y las actividades de comercio electrónico, el número de servicios de transporte y entrega ha aumentado considerablemente. En consecuencia, se han potencializado varios problemas urbanos, como la congestión del tráfico y la contaminación. Varios problemas relacionados pueden formularse como un problema de optimización combinatoria (COP). Dado que la mayoría de ellos son NP-Hard, la búsqueda de soluciones óptimas a través de métodos de solución exactos a menudo no es práctico en un período de tiempo razonable. En entornos realistas, la creciente necesidad de una toma de decisiones "instantánea" refuta aún más su uso en la vida real. En estas circunstancias, esta tesis tiene como objetivo: (i) identificar COP realistas de diferentes industrias; (ii) desarrollar diferentes clases de enfoques de solución aproximada para resolver los problemas de T&L identificados; (iii) realizar una serie de experimentos computacionales para validar y medir el desempeño de los enfoques desarrollados. Se introduce el nuevo concepto de optimización ágil, que se refiere a la combinación de heurísticas aleatorias sesgadas con computación paralela para hacer frente a la toma de decisiones en tiempo real.Les activitats de transport i logística (T&L) tenen un paper vital en el desenvolupament de moltes empreses de diferents indústries. Amb l'augment del nombre de persones que viuen a les zones urbanes, l'expansió de l'economia a la carta i les activitats de comerç electrònic, el nombre de serveis del transport i el lliurament ha augmentat considerablement. En conseqüència, s'han potencialitzat diversos problemes urbans, com ara la congestió del trànsit i la contaminació. Es poden formular diversos problemes relacionats com a problema d'optimització combinatòria (COP). Com que la majoria són NP-Hard, la recerca de solucions òptimes mitjançant mètodes de solució exactes sovint no és pràctica en un temps raonable. En entorns realistes, la creixent necessitat de prendre decisions "instantànies" refuta encara més el seu ús a la vida real. En aquestes circumstàncies, aquesta tesi té com a objectiu: (i) identificar COP realistes de diferents indústries; (ii) desenvolupar diferents classes d'aproximacions aproximades a la solució per resoldre els problemes identificats de T&L; (iii) la realització d'una sèrie d'experiments computacionals per validar i mesurar el rendiment dels enfocaments desenvolupats. S'introdueix el nou concepte d'optimització àgil, que fa referència a la combinació d'heurístiques esbiaixades i aleatòries amb informàtica paral·lela per fer front a la presa de decisions en temps real.Tecnologies de la informació i de xarxe

    To Make the World Smarter and Safer

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    To Make the World Smarter and Safer: Матеріали XIII всеукраїнської науково-практичної конференції студентів, аспірантів та викладачів Лінгвістичного навчально-методичного центру кафедри іноземних мов СумДУ 28 березня 2019 р

    Evaluating Mobility as a Service for sustainable travel among young adults

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    Young adults delay obtaining their driver’s licence, make fewer trips and are more open to using different transport modes. The continuation of this trend as young adults transition from university education into the workforce is less certain. This thesis explores the potential of Mobility as a Service (MaaS) to shift university graduates away from cars and towards public transport and shared mobility services, using the metropolitan city of Birmingham, UK as a case study. MaaS is an app-based scheduling, booking and payment platform for multiple transport modes on a per trip or subscription basis. First, questionnaire survey data was analysed using Ajzen’s Theory of Planned Behaviour to understand multimodal travel behaviour. Second, a discrete choice experiment was used to test the attractiveness of a MaaS subscription relative to conventional transport modes. Third, semi-structured interviews explored the underlying factors influencing graduates’ travel choices as they transition from university education to the workforce using Michie’s Capability, Opportunity and Motivation Behaviour model. The results of the quantitative studies found cost, time, accessibility, and the opinions and behaviour of significant others influence participants’ choice of transport mode. The interviews revealed how students’ negative experiences of using public transport had motivated them to learn to drive, and the transition into the workforce provided the financial means to buy a car. Information and communication technologies were found to play a role in influencing young adults’ travel choices as shown by the reliance on smartphone travel apps. The uptake of MaaS in the current market is optimistic given the relative appeal of its cost, time, and flexibility. The adoption of MaaS among young adults depends on institutional incentives, location, and ease of use. Overall, the flexible multimodal characteristics of MaaS needs strengthening if it is to reduce car-based commuting among new graduate employee
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