"Large-scale modelling of transport and energy choices using emerging big data sources"

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Panagiotis' proposed project aims to make effective use of big data sources for modelling transportchoices (e.g. departure time, activity, mode, destination choices) by developing and deployingnovel modelling techniques.The handling, processing and analysis of the proposed big data sources will require usingquantitative skills above and beyond those required for normal doctoral research. The datasetsused will not only be large in nature, but will have complex interdependencies amongst individualcomponents. Additionally, the analysis of such data relies on making links with secondary datasets,a process which presents a high level of complexity itself.Panagiotis' proposed research consists of three methodological components:1. Combination of different big data sources (social media, smart card-credit card, mobile phoneand floating car data)2. Combination of big data sources with traditional data sources (e.g. surveys, counts)3. Development of a framework for real-time updating of the developed models with newly availabledataAll three components pose significant challenges and demand mastery of advanced quantitativetechniques. For example, for data combination (components 1 and 2), he proposes to investigateeconometric and simulation-based methods. On the econometric side, he proposes to investigatenovel techniques like Broad Choice Models (Brownstone et al. 2017), Latent demographic models(Bwambale et al. 2017), etc. to address the problems associated with coarse data resolution andmissing data. He also proposes applying Bayesian Inference techniques in this regard. Masteringthese methodologies will require advanced mathematics and statistics. Furthermore, it is likely thatmore methodological innovations will be required to address other issues of the big data sourceslike bias and data gaps.On the simulation side, Panagiotis proposes to use agent based modelling techniques (whereagents are generated based on household survey data) and their behaviour are calibrated with BigData sources. These are interesting ideas which can lead to novel methodological advancements inthe field of data fusion. Again, the links between individual agents will require advanced quantitativeskills.Developing a framework for real time updating of the models (component 3) can also be a verychallenging problem which may require borrowing techniques from other disciplines (e.g. weatherforecasting) where data assimilation techniques are more common. Bringing these techniques intotransport and mobility research (with potential modifications) can lead to interesting crossfertilization of ideas between disciplines