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，减少汽车排放的努力受到了阻碍。2010年，英国交通部（Department for Transport）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量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许多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，并且可以在相对完整的基础上检查使用随空间和时间的变化。当与其他丰富的现有数据集（尤其是2011年人口普查的新信息）结合在一起时，应该会出现一系列新的和重要的见解。作为一个项目团队，我们已经在2011年epsrc资助的一项小型研究中使用了这些数据，现在我们建议将其作为一个平台，在此基础上开发一套相互关联的建模和分析任务，使用多个特定车辆和基于区域的数据来源。一套相互依存的工作包将跨越三年，调查汽车拥有量和使用的时空差异，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。汽车所有权、汽车使用、燃料使用和车辆排放以及影响这些因素的人口、经济、基础设施和社会文化因素之间的关系将通过空间统计、回归模型和情景分析进行数学测试。还将与家庭燃气和电力使用的空间格局建立联系，以便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当地运输政策的案例研究评估来测试。通过在相对精细的空间和时间尺度上对汽车的拥有和使用进行检查，并通过技术来识别具有重要“背景”特征的地区，应该可以回答可持续交通政策的关键问题，例如，特定政策对汽车的拥有和使用有什么不同（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。我们还将能够把汽车的能源使用与家庭用电和燃气的能源使用联系起来。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、多重剥夺和其他社会经济因素联系起来，将有助于设计更有效的气候和能源政策，并确保公平地承担这些政策的负担。该项目将得到应用统计专家小组的支持，并包括提供与主要利益攸关方的讲习班，以帮助制定工作。该项目还将帮助开发一种可能的基于网络的工具的规范，使广泛的用户社区能够使用我们开发的数据和工具对这类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，并受到不同的数据保护和敏感性问题的影响。我们的范围界定研究表明，在处理这种规模和性质的数据集时，有非常重大的技术挑战需要克服，并且广泛的学科可能能够从这项工作中学习。所提供的分析框架和新的科学理解将是这个项目的重要遗产。

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.