The rise of research into shared mobility systems reflects emerging challenges, such as rising traffic congestion, rising oil prices and rising environmental concern. The operations research community has turned towards more sharable and sustainable systems of transportation. Shared mobility systems can be collapsed into two main streams: Those where people share rides and those where parcel transportation and people transportation are combined. This survey sets out to review recent research in this area, including different optimization approaches, and to provide guidelines and promising directions for future research. It makes a distinction between prearranged and real-time problem settings and their methods of solution, and also gives an overview of real-case applications relevant to the research area.

https://hal.archives-ouvertes.fr/hal-02014342/document

A survey of models and algorithms for optimizing shared mobility

In this paper, we present an ensemble learning approach for better understanding ridesplitting behavior of passengers of ridesourcing companies who provide prearranged and on-demand transportation services. An ensemble learning model is a weighted combination of multiple classification models or week classifiers to form a strong classification model. The goal of ensemble learning is to combine decisions or predictions of several base classifiers to improve prediction, generalizability, and robustness over a single classifier. This paper employs the Boosting ensemble by growing individual decision trees sequentially and then assembling these trees to produce a powerful classification model. To improve the prediction accuracy of ridesplitting choices, we explored real-world individual level data extracted from the on-demand ride service platform of DiDi in Hangzhou, China. Over one million trips of the four service types, i.e., Taxi Hailing Service, Express, Private Car Service, and Hitch, are explored with descriptive statistics. A variety of features that may impact ridesplitting behavior are ranked and selected by using the ReliefF algorithm, such as trip travel time, trip costs, trip length, waiting time fee, travel time reliability of origins/destinations and so on. The Boosting ensemble trees with full features and selected features are trained and validated using two independent datasets. This paper also verifies that ensemble learning is particularly useful and powerful in the ridesplitting analysis and outperforms three other widely used classifiers. This paper is one of the first quantitative studies that empirically reveal the real-world demand and supply pattern by exploring the city-wide data of an on-demand ride service platform.

Understanding ridesplitting behavior of on-demand ride services: An ensemble learning approach

(1) Background: The sharing economy has emerged as a phenomenon widely described by academic literature to promote more sustainable consumption practices such as access over ownership. However, there exists great semantic confusion within academic literature surrounding the term “sharing economy,” which threatens the realisation of its purported sustainability potential. (2) Objective: The aim of this paper is to synthesise the existing academic definitions and propose a definition of the sharing economy from the perspective of sustainability science in order to indicate sharing practices that are consistent with the sustainability claims attributed to the sharing economy. (3) Methods: We conduct a database search to collect relevant academic articles. Then, we leverage qualitative content analysis in order to analyse the authors’ definitions and to synthesise the broad dimensions of the sharing economy in the discourse. (4) Results: We propose the following characteristics, or semantic properties, of the sharing economy for sustainability: ICT-mediated, non-pecuniary motivation for ownership, temporary access, rivalrous and tangible goods. (5) Conclusion: The semantic properties that inform our definition of the sharing economy for sustainability indicate those sharing practices that promote sustainable consumption compared to purely market-based exchanges. This definition is relevant for academics studying the sustainability impacts of the sharing economy in order to promote comparability and compatibility in research. Furthermore, the definition is useful for policy-makers, entrepreneurs, managers and consumers that have the sharing economy on the agenda in order to promote social enterprise and support sustainable consumption.

https://www.mdpi.com/2071-1050/11/3/567/pdf

Defining the Sharing Economy for Sustainability

A multi-phase QFD-based hybrid fuzzy MCDM approach for performance evaluation: A case of smart bike-sharing programs in Changsha

This paper proposes new objective functions for the matching problem arising in ride-sharing systems based on trips’ spatial attributes. Novel dynamic matching policies are then proposed to solve the problem dynamically in a rolling horizon framework. Finally, we present a new clustering heuristic to tackle instances with a large number of participants efficiently. We find that the proposed models maximize the matching rate while maintaining distance-savings at an acceptable level, which is an appealing achievement for ride-sharing systems. Further, our solution method is capable of solving large-scale instances in real-time.

Novel dynamic formulations for real-time ride-sharing systems

Creating a set of Personas requires a considerable effort. Socio-psychological characteristic must be very punctually defined, needs and goals must also be well investigated and related to the service that will be designed. The accurate collection and processing of this large amount of qualitative and quantitative data represents often a huge cost. In our research area, which deals mainly with medium to long term telco projects, we developed a Personas layering model that allows us to adapt them over different contexts. The model consists in two main elements; the basic "Persona" and the "external" layer. In this paper we gave a practical application of our model within different projects developed in our research area. The benefits that arise from this model are the "durability" of Personas, their re-use in different contexts, the modularity of components and their possible recombination, thus reducing costs while maintaining excellent design insights.

/pdf/personas-layering-a-cost-effective-model-for-service-design-bkk19hrpil.pdf

Personas Layering: A Cost Effective Model for Service Design in Medium-Long Term Telco Research Projects

WantEat: interacting with “social” networks of intelligent things and people in the world of enogastronomy

The current market of television services adopts several broadcast technologies (e.g. IPTV, DVBH, DTT), delivering different ranges of contents. These services may be extremely heterogeneous, but they're all affected by the continuous increase in quantity of contents and this trend is becoming more and more complicated to manage. Hence, future television services must respond to an emerging question: in what way could the navigation among this increasing volume of multimedia contents be facilitated? To answer this question, a research study was conducted, resulting in a set of guidelines for Interactive TV development. At first, the current scenario was portrayed through a functional analysis of existing TV systems and a survey of actual and potential users. Subsequently, interaction models which could possibly be applied to Interactive TV (e.g.: peer-to-peer programs) were assessed. Guidelines were eventually defined as a synthesis of current best practices and new interactive features.

/pdf/innovative-tv-from-an-old-standard-to-a-new-concept-of-11ekh8ybmw.pdf

Innovative TV: from an old standard to a new concept of interactive TV-an Italian job

Traditional TV was based on a time-dependent and passive paradigm of use: the availability of audiovisual contents was pre-defined by a rigid scheduling, and user's role was to choose among limited alternatives. A potential for active TV experience is now available: users should be allowed to access contents that fit their attitudes at any time, while being encouraged to discover new domains of interest. This paper outlines the basic elements of the new Inter-tainment paradigm, which builds on an active role of the user and on attitude-centred fruition. Successful applications of the new paradigm require flexible and reconfigurable structures of navigation: the development stages of an Inter-tainment system are reported here.

https://link.springer.com/content/pdf/10.1007%2F978-3-540-69478-6_32.pdf

Where Have You Ended Up Today? Dynamic TV and the Inter-tainment Paradigm

adopted within an internal Telecomitalia project aimed to identify, on a user centred base, the potential interest towards a technological scenario aimed to extend on a personal bubble the typical communication and media fruition home environment. The problem is that involving user in the early stage of the development of such disruptive technology scenario asking users opinions on something that users actually do not manage even in a rough manner could lead to wrong or distorted results. For that reason we chose an approach that indirectly aim to understand users hidden needs in order to obtain a meaningful picture of the possible interest for a technological proposition non yet easily understandable.

How to connect user Research and not so Forthcoming Technology Scenarios – The Extended Home Environment Case Study

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Elena Guercio

Author Tools

Chat about Author