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

Panagiotis提出的项目旨在通过开发和部署新的建模技术，有效地利用大数据源来建模运输选择（例如出发时间、活动、模式、目的地选择）。对拟议的大数据源的处理、处理和分析将需要使用超出正常博士研究所需的定量技能。所使用的数据集不仅本质上很大，而且各个组件之间具有复杂的相互依赖关系。此外，对这些数据的分析依赖于与辅助数据集建立联系，这一过程本身就具有很高的复杂性。Panagiotis提出的研究包括三个方法学组成部分：1。2.结合不同大数据源（社交媒体、智能卡-信用卡、手机、浮车数据）大数据源与传统数据源（如调查、统计）的结合开发一个框架，用新的可用数据实时更新已开发的模型。所有这三个组成部分都构成了重大挑战，需要掌握先进的定量技术。例如，对于数据组合（组件1和组件2），他建议研究计量经济学和基于模拟的方法。在计量经济学方面，他建议研究新的技术，如广义选择模型（Brownstone et al. 2017）、潜在人口模型（Bwambale et al. 2017）等，以解决与粗数据分辨率和缺失数据相关的问题。他还建议在这方面应用贝叶斯推理技术。掌握这些方法需要高级数学和统计学。此外，可能需要更多的方法创新来解决大数据源的其他问题，如偏见和数据差距。在模拟方面，Panagiotis建议使用基于代理的建模技术（其中代理是基于家庭调查数据生成的），并使用大数据数据源校准其行为。这些都是有趣的想法，可以在数据融合领域带来新的方法进步。同样，个体代理之间的联系将需要高级的定量技巧。开发一个用于实时更新模型的框架（组件3）也可能是一个非常具有挑战性的问题，这可能需要从数据同化技术更常见的其他学科（例如天气预报）借鉴技术。将这些技术引入交通运输和移动研究（可能会有一些修改）可以在不同学科之间带来有趣的思想交流

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