Multi-Modal Data-Driven Solutions for Validating Policies in Transportation Systems

This PhD project will be focused on the following aim:

develop machine learning techniques that can integrate and deal with large diverse multi-modal complex datasets in transportation for validating policies and for designing novel efficient policies with road pricing as a case study. In order to achieve the aim of this PhD project, we identified the following research questions:

1. Given the large volume of data gathered from the transportation network, what data types are relevant to a policy or intervention?

2. What machine learning techniques are suitable for combining multi-modal datasets, processing the data, and validating an intervention?

3. Could these multi-modal datasets and machine learning techniques be used for designing novel effective road pricing policies and/or validating current policies?