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

“使用新兴大数据源对运输和能源选择进行大规模建模”

基本信息

批准号：
2114183
负责人：
金额：
--
依托单位：
University of Leeds
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2018
资助国家：
英国
起止时间：
2018 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2114183
关键词：
Large scale modelling transport energy

项目摘要

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

Panagiotis 提出的项目旨在通过开发和部署新颖的建模技术，有效利用大数据源对交通选择（例如出发时间、活动、模式、目的地选择）进行建模。处理、处理和分析拟议的大数据源将需要使用超出正常博士研究所需的定量技能。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性。此外，此类数据的分析依赖于与二级数据集的链接，这一过程本身就呈现出高度的复杂性。Panagiotis 提出的研究由三个方法组成部分组成：1．不同大数据源的结合（社交媒体、智能卡信用卡、手机和流动汽车数据）2。大数据源与传统数据源（例如调查、统计）的结合3。开发一个框架，用于使用新的可用数据实时更新已开发的模型。所有这三个组件都提出了重大挑战，需要掌握先进的定量技术。例如，对于数据组合（第 1 部分和第 2 部分），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，他建议研究广泛选择模型（Brownstone et al. 2017）、潜在人口统计模型（Bwambale et al. 2017）等新技术，以解决与粗略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等数学和统计学。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，例如偏差和数据差距。在模拟方面，Panagiotis 提出使用基于代理的建模技术（其中代理是根据家庭调查数据生成的），并且它们的行为通过大数据源进行校准。这些都是有趣的想法，可以在数据融合领域带来新颖的方法论进步。同样，个体代理之间的联系需要先进的定量技能。开发模型实时更新的框架（组件 3）也可能是一个非常具有挑战性的问题，可能需要借用数据同化技术更常见的其他学科（例如天气预报）的技术。将这些技术引入交通和出行研究（可能进行修改）可以导致学科之间有趣的思想交叉融合