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

重新平衡出行系统，使更多的旅行使用主动模式，已越来越成为英国和海外政府的优先事项。步行和骑自行车不仅有益于人们的身心健康，减少对汽车的依赖还会对噪音和空气污染产生积极影响，减少道路交通伤害的风险，预防心血管疾病和肥胖。建成环境和出行行为之间已经建立起因果关系，环境受到汽车的强烈影响。在最糟糕的情况下，以汽车为中心的设计割裂了社区，道路成为行人移动的物理障碍，限制了互动。因此，通过改善建筑环境，就有可能消除这些对积极旅行的物理障碍。然而，试图改善建筑环境的干预措施并不总是考虑单个机构的作用和潜在的社会规范。社会规范是一个群体成员对特定情况下的正确行为所持有的非正式、不成文的理解。与交通行为相关的规范，可能包括诸如汽车拥有权的可取性或环境的重要性之类的东西。基于代理的模拟是一个自下而上的建模过程，在该过程中，代理被给予简单的行为规则，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，在这种方法中，简单的行为规则可以导致出现宏观层面的行为。使用基于智能体的模拟可以提供一种廉价的方法来试验公共卫生干预措施，而不需要实施它们，并且可以更好地了解发生的潜在交互作用，这可能会对干预产生影响。本项目探索以下研究问题：围绕旅行行为的社会规范如何最小化或最大化结构性主动旅行干预的有效性及其对个人健康的影响？为此，将使用一个复杂的基于智能体的交通模型来评估不同的社会规范如何影响旨在增加积极旅行的干预措施的相对有效性。该模型将通过对大规模二次数据的统计分析进行参数化，并通过实施真实世界干预和比较结果来验证该模型。一旦产生了一个适当校准的模型，该模型将被用来评估一些假设的干预措施。这使他们能够在不需要实施的情况下进行调查，从而更好地了解是什么使干预成功。该项目旨在通过分析大规模二级数据和应用尖端人工智能方法(基于代理的建模)，从MRC的横向主题中瞄准量化技能。这也有很强的跨学科因素，应用了计算机科学方法，如基于代理的建模和网络科学方法。到目前为止，MRC的学生提供了医学统计学(MSc-LSHTM)的具体量化培训。

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).