Thesis Code: 21002
Thesis Type: M.Sc. thesis in Computer Science
Research Area: Urban Mobility & Logistic Systems
Description
For a technology to be widely accepted by the public, it is not enough that it brings benefits. The public needs also to be highly involved in order to understand whether using a new technology. According to several research studies, at present the level of awareness and understanding of UAM (Unmanned Air Mobility) / UAV (Unmanned Air Vehicles) is very low among people.
The objective of this thesis is to assess the public acceptance of UAV/UAM by mining social data with text mining / machine learning algorithms related to natural language processing.
Firstly, the candidate will carry out a review of public acceptance evaluation methods with respect to UAV/UAM, defining: domains of application of UAV/UAM, main constraints recognised by the general public, and effective strategies to foster the public acceptance.
Then, the candidate will assess public acceptance of UAV/UAM by mining social data with proper text mining techniques. The candidate will have both the task of collecting the data and evaluating the best algorithms to apply for the case of study. Such data-driven approach could be helpful to measure the community engagement around the UAM/UAV-related topics. Findings can constitute insights from which public policy makers may draw for enhancing the community involvement around these topics and for becoming far more reactive to the citizenry’s needs.
The thesis will be carried out in collaboration between Links and the University of Turin.
Requirements
- Experience with Python
- Basic concepts on data science, concerning data analysis and data processing
- Ability to represent data
- Interest in Natural Language Processing
Contact: send a resume with attached the list of exams to maurizio.arnone@linksfoundation.com specifying the thesis code and title.
