TDOT 25-Year Long-Range Transportation Policy Plan, Mobility Policy Paper

https://digitalcommons.memphis.edu/govpubs-tn-dept-transportation-25-year-transportation-policy/1005/thumbnail.jp

Informal and shared mobility: A bibliometric analysis and researcher network mapping

This study was commissioned by the Volvo Research and Educational Foundations to inform the content of the next phase of its Future Urban Transport programme. The aim of the study was to define the subject field (provisionally described by VREF as ‘informal public transport’ and ‘shared mobility’), analyse its bibliometric attributes. The study method involved an analysis of the nomenclature and focus of seminal or consolidating in-field literature to delineate the scope of the study, a systematic search of multiple platforms for in-field English language publications between 2010 and the present, the creation and augmentation of a database for bibliometric analysis, and a survey of leading researchers across eight global regions. The subject field was divided into four main categories of passenger services (flexible transport, informal transport, shared mobility and for-hire transport), which sit between purely private transport and scheduled mass public transport, and can be found in various guises across the Global North and Global South. The timeline of innovation in the field reveals complex and multi-directional global diffusion of service innovations, triggered by changed operating environments and technology disruption. It was found that research in this field is growing fast (doubling every four years). The recent literature is dominated by authors affiliated to universities in Europe, Eastern Asia and Northern America at a regional scale, and to universities in China and the United States at a country scale. Shared mobility (and bike-sharing, car-sharing and ride hailing in particular) has received most attention (62%), followed by for-hire transport (17%), informal transport (11%), and flexible transport (10%). Most publications concerning shared mobility and for-hire transport were produced by lead authors in China (19,3% and 44,1%), followed by the United States (15,0% and 9,9%). Most publications concerning informal transport were produced in South Africa (18,2%) followed by India (9,8%), and concerning flexible transport were produced in the United States (13,1%) followed by Australia (9,6%). There has been extensive international research collaboration, with collaboration between research institutions in China and the United States found to be particularly strong, as was collaboration between China and other East Asian countries. Somewhat paradoxically, while the quantity of collaborations with universities in Africa, Latin America, and Western Asia was relatively small, authors from many countries within these regions are most likely to publish through international collaboration. Citation networks between institutions followed a similar pattern to collaboration networks. Geographical gaps in the literature were found, with heatmaps revealing countries, particularly in Sub-Saharan Africa, that received no dedicated research attention. While difficult to quantify, there were also indications of thematic gaps in the literature, or at least disparity between the prevalence of a service type and the number of publications about it. Most notably, compared to their global prevalence, bike-sharing, car-sharing and carpooling were well researched, compared to informal for-hire transport and informal public transport, which received significantly less attention. Given the multi-directional innovation diffusion in the subject field, and the disparity of research capacity and output across regions, it is a field of inquiry that presents rich possibilities for global research collaboration in the next phase of the FUT programme. The survey of leading researchers suggested that: integrating with mass public transport services; serving the needs of vulnerable passengers; regulating service providers; introducing electric vehicles into shared mobility and informal transport fleets; and digitalising aspects of informal transport operations; are priority future research needs

MOBILITY ANALYSIS AND PROFILING FOR SMART MOBILITY SERVICES: A BIG DATA DRIVEN APPROACH. An Integration of Data Science and Travel Behaviour Analytics

Smart mobility proved to be an important but challenging component of the smart cities paradigm. The increased urbanization and the advent of sharing economy require a complete digitalisation of the way travellers interact with the mobility services. New sharing mobility services and smart transportation models are emerging as partial solutions for solving some tra c problems, improve the resource e ciency and reduce the environmental impact. The high connectivity between travellers and the sharing services generates enormous quantity of data which can reveal valuable knowledge and help understanding complex travel behaviour. Advances in data science, embedded computing, sensing systems, and arti cial intelligence technologies make the development of a new generation of intelligent recommendation systems possible. These systems have the potential to act as intelligent transportation advisors that can o er recommendations for an e cient usage of the sharing services and in uence the travel behaviour towards a more sustainable mobility. However, their methodological and technological requirements will far exceed the capabilities of today's smart mobility systems. This dissertation presents a new data-driven approach for mobility analysis and travel behaviour pro ling for smart mobility services. The main objective of this thesis is to investigate how the latest technologies from data science can contribute to the development of the next generation of mobility recommendation systems. Therefore, the main contribution of this thesis is the development of new methodologies and tools for mobility analysis that aim at combining the domain of transportation engineering with the domain of data science. The addressed challenges are derived from speci c open issues and problems in the current state of the art from the smart mobility domain. First, an intelligent recommendation system for sharing services needs a general metric which can assess if a group of users are compatible for speci c sharing solutions. For this problem, this thesis presents a data driven indicator for collaborative mobility that can give an indication whether it is economically bene cial for a group of users to share the ride, a vehicle or a parking space. Secondly, the complex sharing mobility scenarios involve a high number of users and big data that must be handled by capable modelling frameworks and data analytic platforms. To tackle this problem, a suitable meta model for the transportation domain is created, using the state of the art multi-dimensional graph data models, technologies and analytic frameworks. Thirdly, the sharing mobility paradigm needs an user-centric approach for dynamic extraction of travel habits and mobility patterns. To address this challenge, this dissertation proposes a method capable of dynamically pro ling users and the visited locations in order to extract knowledge (mobility patterns and habits) from raw data that can be used for the implementation of shared mobility solutions. Fourthly, the entire process of data collection and extraction of the knowledge should be done with near no interaction from user side. To tackle this issue, this thesis presents practical applications such as classi cation of visited locations and learning of users' travel habits and mobility patterns using historical and external contextual data

Platform economy. Impact of the pricing strategy on the platform network effects.

The platform business model has brought the success in many of today’s biggest, fastest-growing and most powerfully disruptive companies. The experimental Master Thesis aims to investigate understanding the components of the platforms; their network models, how to build safe network effects and what are the possible applied pricing strategies, each one’s impacts on the user base. Another crucial point in research dedicated to chicken-or-egg problem