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

使用基于代理的模型模拟交通干预对身体活动和心血管疾病的影响

• 批准号: 2242969
• 金额: --
• 依托单位: London School of Hygiene & Tropical Medicine
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2242969
• 关键词: Simulating; impact; transport; interventions; physical

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).

重新平衡出行系统，让更多的人使用主动出行模式，已日益成为英国和其他国家政府的首要任务。步行和骑自行车不仅有利于人们的身心健康，而且减少对汽车的依赖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确立，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切断了社区，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，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个人机构的作用和潜在的社会规范。社会规范是一种非正式的、不成文的共识，由一个群体的成员共同持有，关于在特定情况下的正确行为。与交通行为有关的规范可能包括诸如拥有汽车的可取性或环境的重要性。基于代理的模拟是一个自下而上的建模过程，在这个过程中，代理被赋予简单的行为规则，这些规则定义了它们在一个环境中如何行动以及如何相互作用。这是一种人工智能方法，其中简单的行为规则可以导致紧急宏观层面的行为出现。使用基于主体的模拟，可以采用一种廉价的方法来试验公共卫生干预措施，而不必实施这些干预措施，并且可以更深入地了解可能对干预措施产生影响的潜在相互作用。本项目探讨了以下研究问题：围绕旅行行为的社会规范如何使结构性积极旅行干预的有效性最小化或最大化及其对个人健康的影响？为此，将使用一个复杂的基于主体的运输模型来评估不同的社会规范如何影响旨在增加主动旅行的干预措施的相对有效性。该模型将通过大规模二手数据的统计分析进行参数化。该模型将通过实施现实世界的干预措施和比较结果来验证。一旦产生了一个适当校准的模型，该模型将用于评估一些假设的干预措施。这样就可以在不实施干预措施的情况下对其进行调查，从而更深入地了解干预措施成功的原因。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MRC的跨领域主题中获得定量技能。这也有很强的跨学科元素，应用计算机科学方法，如基于代理的建模和网络科学方法。到目前为止，MRC奖学金提供了医学统计（MSc-LSHTM）方面的具体定量培训。