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

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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?