| Reference : MOBILITY ANALYSIS AND PROFILING FOR SMART MOBILITY SERVICES: A BIG DATA DRIVEN APPROA... |
| Dissertations and theses : Doctoral thesis | |||
| Engineering, computing & technology : Computer science | |||
| Computational Sciences | |||
| http://hdl.handle.net/10993/40724 | |||
| MOBILITY ANALYSIS AND PROFILING FOR SMART MOBILITY SERVICES: A BIG DATA DRIVEN APPROACH. An Integration of Data Science and Travel Behaviour Analytics | |
| English | |
Toader, Bogdan  [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >] | |
| 11-Mar-2019 | |
| University of Luxembourg, Luxembourg, Luxembourg | |
| DOCTEUR DE L’UNIVERSITÉ DU LUXEMBOURG EN SCIENCES DE L'INGÉNIEUR | |
| 181 | |
| Viti, Francesco | |
| Koenig, Vincent | |
| Mc Call, Roderick | |
| Le Traon, Yves | |
| Francisco Camara, Pereira | |
| [en] 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. | |
| http://hdl.handle.net/10993/40724 | |
| FnR ; FNR9220491 > Bogdan Toader > > PLanning and Activity-travel analYtics for future Mobility Optimisation in BelvaL > 15/03/2015 > 14/03/2019 > 2014 |
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