MOT: Motoring and car Ownership Trends in the UK

由于缺乏关于汽车拥有和使用的具体信息，减少汽车旅行排放的努力受到阻碍。2010年，运输部发布了一个数据集，其中包含2005年以来汽车的年度MOT测试记录，并承诺定期更新。通过提供有关英国汽车拥有和使用的相对全面的信息，该数据集为解决交通和能源辩论中的一些问题提供了关键机会。第一次可以在汽车使用和汽车类型之间建立精确的联系，可以在相对完整的基础上研究空间和时间上的使用变化。当与大量其他现有数据集相结合时（尤其是2011年人口普查的新信息），一系列新的重要见解应该会出现。2011年，我们已经作为一个项目团队在EPSRC资助的一项小型研究中合作，确定了这些数据的使用范围，我们现在建议将其作为一个平台，利用多个车辆特定和区域数据来源，开发一套相互关联的建模和分析任务。一套相互依存的工作包将历时三年，以调查汽车拥有和使用方面的时空差异、这些差异的决定因素以及水平如何随着时间的推移和对各种政策措施的反应而变化。将使用空间统计、回归模型和情景分析，对汽车拥有量、汽车使用、燃料使用和汽车排放量之间的关系以及影响这些关系的人口、经济、基础设施和社会文化因素进行数学测试。还将与家庭天然气和电力使用的空间格局建立联系，以了解这些最终用户能源需求内部和之间的关系。新的分析能力将通过对地方运输政策的个案研究评价加以检验。通过在相对精细的空间和时间尺度上对汽车拥有和使用情况进行审查，并通过查明具有重要“背景”特征的地区的技术，应该有可能回答可持续运输政策的关键问题，例如，特定政策对汽车拥有和使用情况产生了什么影响（与没有这些政策的地区相比）？它还将能够计算燃料使用和排放的数字，以帮助制定专门针对能源密集或污染最严重的司机或地方的政策。我们还将能够将汽车的能源使用与通过家庭电力和天然气的家庭能源使用联系起来。这将使我们能够更好地了解全国的能源和碳足迹。如果将其与收入模式、多重贫困和其他社会经济因素联系起来，将有助于制定更加有效的气候和能源政策，并确保公平地承担这些负担。该项目将得到一个应用统计专家小组的支持，并包括与主要利益攸关方举办讲习班，以帮助开展工作。该项目还将帮助开发一个可能的基于网络的工具的规格，使广大社区的用户能够利用我们开发的数据和工具对这些问题进行自己的分析。为了实现我们的目标，我们将开发方法来克服合并一系列重要但不同的数据集的挑战，这些数据集基于不同的空间，时间和其他特征，并受到各种数据保护和敏感性问题的影响。我们的范围研究表明，在使用这种规模和性质的数据集时，需要克服非常重大的技术挑战，并且广泛的学科可以从这项工作中学习。所提供的分析框架和新的科学认识将是该项目的重要遗产。

Efforts to reduce the emissions from car travel have been hampered by a lack of specific information on car ownership and use. In 2010, the Department for Transport released a dataset containing annual MOT test records for cars from 2005 onwards, with regular updates promised. By providing relatively comprehensive information about British car ownership and use, this dataset provides a key opportunity to address a number of issues in transport and energy debates. For the first time precise links can be made between car use and car type, and changes in use over space and time can be examined on a relatively complete basis. When combined with a wealth of other existing data sets (not least the new information from the 2011 Census), a range of new and important insights should emerge.Having already worked together as a project team to scope the use of this data in a small EPSRC-funded study in 2011, we now propose to use it as a platform upon which to develop a set of interlinked modelling and analysis tasks using multiple sources of vehicle-specific and area-based data. A set of interdependent workpackages will span three years to investigate spatial and temporal differences in car ownership and use, the determinants of those differences, and how levels may change over time and in response to various policy measures. The relationships between car ownership, car use, fuel use and vehicle emissions, and the demographic, economic, infrastructural and socio-cultural factors influencing these will be tested mathematically using spatial statistics, regression modelling and scenario analyses. Linkages will also be made with spatial patterns of domestic gas and electricity usage in order to understand relationships within and between these end-user energy demands. The new analysis capability will be tested through case study evaluation of local transport policies. By enabling car ownership and use to be examined at relatively fine spatial and temporal scales, and via techniques to identify areas sharing important 'background' characteristics, it should be possible to answer key questions for sustainable transport policies such as, what difference to car ownership and use have particular policies achieved (compared with areas where these policies were not in place)? It will also be able to calculate figures for fuel use and emissions to contribute to the development of policies specifically targeted at the most energy intensive or polluting drivers or localities. We will also be able to link energy use from cars, with domestic energy usage through household electricity and gas. This will allow us to build up a much better picture of energy and carbon footprints across the country. When linked to patterns of income, multiple deprivation and other socio-economic factors, there will be insights for the design of much more effective climate and energy policies, and to ensure that the burden of these is borne equitably. The project will be supported by an Applied Statistics Expert Panel, and includes provision for workshops with key stakeholders to help shape the work. The project will also help develop a specification for a possible web-based tool to enable a wide community of users to undertake their own analysis on these sorts of issues, using the data and tools that we develop.In order to achieve our goals, we will develop methods to overcome the challenges of merging a range of important but disparate datasets, based on varying spatial, temporal and other characteristics, and subject to varying issues of data protection and sensitivity. Our scoping study demonstrated that there are very significant technical challenges to be overcome in working with datasets of this size and nature, and a wide range of disciplines may be able to learn from this work. The analysis frameworks and the new scientific understanding delivered will be the important legacies of this project.