Master theses

Personalized and privacy-aware route planning on the Web

Keywords: mobility, solid, Linked Data, open data, AI , GDPR, artificial intelligence, route planning, semantic web, smart cities

Supervision: Pieter Colpaert

Students: max 1

Mobility is a fundamental aspect of our daily life. The level of comfort of the daily journeys we take, have a significant impact on our quality of life. For this reason, considerable efforts have been dedicated to develop route planning applications that provide information about the best alternatives to go from one place to another. However, every person has her/his own unique preferences and requirements when travelling, and current route planning applications are not able to consider all the different aspects that comprise a person’s mobility when calculating possible journey plans. Achieving truly personalized route planning requires applications to access and integrate data about mobility preferences (e.g. preferred modes of transport, public transport subscriptions, favorite routes, etc) and needs (e.g. wheelchair access, secure parking, vehicle restrictions, etc), while complying with data privacy requirements and policies such as GDPR.

In this project we aim to design and implement a privacy-aware solution for truly personalized route planning. Using the Web as our platform, we aim to provide the users with tailored route planning advice while allowing them to keep ownership and control over their personal data. This requires the definition of a flexible and interoperable data model that can be integrated into decentralized and privacy-aware data architectures such as Solid, and also the understanding and implementation of state-of-the-art route planning algorithms into Web-based journey planners such as Planner.js. Join us for an exciting project and help people to have a comfortable and happy journey.

Example of client-side route planning by downloading the data on the fly:

Contraction hierarchies on Web data