Simulating the impact of transport interventions on physical activity and cardiovascular disease using agent-based models

重新平衡旅行系统，使更多的旅行使用主动模式，越来越成为英国和国外政府的优先事项。步行和骑自行车不仅有益于人们的身心健康，减少对汽车的依赖也将对噪音和空气污染产生积极影响，降低道路交通伤害的风险，预防心血管疾病和肥胖。建筑环境和出行行为之间的因果关系已经建立，环境受到汽车的强烈影响。在最坏的情况下，以汽车为中心的设计切断了社区，道路成为行人流动的物理障碍，限制了互动。因此，通过改善建筑环境，可以消除这些对主动旅行的物理障碍。然而，试图改善建筑环境的干预措施并不总是考虑到个人机构的作用，以及潜在的社会规范。与交通行为相关的规范可能包括汽车所有权的可取性或环境的重要性等内容。基于代理的仿真是一种自下而上的建模过程，其中代理被赋予简单的行为规则，这些规则定义了它们在环境中的行为以及彼此之间的交互。这是一种人工智能方法，简单的行为规则可以导致紧急宏观行为的出现。使用基于代理的模拟允许一种廉价的方法来实验公共卫生干预措施，而不必实施它们，以及更深入地了解发生的潜在相互作用，这可能对干预产生影响。本项目探讨以下研究问题：围绕旅行行为的社会规范如何最大限度地减少或最大限度地提高结构性主动旅行干预措施的有效性及其对个人健康的影响？要做到这一点，一个复杂的基于代理人的交通模型将被用来评估不同的社会规范如何影响干预措施的相对有效性，旨在增加积极的旅行将被开发。该模型将通过对大规模二手数据的统计分析来确定参数，并通过实施实际干预措施和比较结果来验证。一旦制作出经过适当校准的模型，将利用该模型评价一些假设的干预措施。该项目旨在通过分析大规模二手数据和应用尖端人工智能方法（基于代理的建模），从多边资源中心的跨领域主题中培养定量技能。这也有很强的跨学科元素，与计算机科学方法的应用，如基于代理的建模和网络科学方法。MRC学生迄今为止提供了具体的定量培训医学统计学（MSc-LSHTM）。

Rebalancing the travel system, so that more journeys are made using active modes, has increasingly become a priority for governments in the UK and abroad. Not only are walking and cycling good for people's physical and mental health, the reduced dependency on cars will have a positive effect on noise and air pollution, reduce the risk of road traffic injury, and prevent cardiovascular disease and obesity.A causal relationship between the built environment and travel behaviour has been established, with the environment being strongly influenced by the car. In the worst cases, car-centric design severs communities, with roads acting as physical barriers to pedestrian movement and limiting interaction. Consequently, by improving the built environment, it is possible to remove these physical barriers to active travel. However, interventions attempting to improve the built environment do not always consider the role of individual agency, and the underlying social norms.A social norm is an informal, unwritten understanding held collectively by members of a population as to the correct behaviour in given circumstances. Norms relating to transport behaviour, may include things such as the desirability of car ownership, or the importance of the environment.Agent-based simulation is a bottom-up modelling process in which agents are given simple behavioural rules that define how they act in an environment and interact with one another. This is an artificial intelligence approach where simple behavioural rules can cause emergent macro-level behaviour to arise. The use of agent-based simulation allows for an inexpensive method for experimenting with public health interventions, without them having to be implemented, as well as a greater insight to the underlying interactions that occur, which may have effects on the intervention.This project explores the following research question: how do the social norms surrounding travel behaviour minimise or maximise the effectiveness of structural active-travel interventions and their effect on personal health?To do this a sophisticated agent-based model of transport will be used to evaluate how differing social norms impact the relative effectiveness of interventions aimed at increasing active travel will be developed. This model will be parameterised through statistical analysis of large-scale secondary data.The model will be validated by implementing real-world interventions and comparing the results. Once a properly calibrated model has been produced, the model will be used to evaluate a number of hypothetical interventions. This allows them to be investigated without needing to be implemented, allowing for greater insight into what makes interventions successful.This project aims to target the quantitative skills from the MRC's cross-cutting themes by the analysis of large-scale secondary data and the application of cutting-edge artificial intelligence methods (agent-based modelling). This also has a strong interdisciplinary element, with the application of computer science methods such as agent-based modelling and network science methods.The MRC studentship has so far provided specific quantitative training in Medical Statistics (MSc-LSHTM).