Addressing challenges in utilising smartphone-based travel survey data using choice modelling and machine learning techniques

使用选择建模和机器学习技术解决利用基于智能手机的旅行调查数据的挑战

• 批准号: 2601274
• 金额: --
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2601274
• 关键词: Addressing; challenges; utilising; smartphone; based

The Covid-19 pandemic has led to substantial changes in our activity and lifestyle preferences and triggered the need to rethink the design of cities. This requires sound understanding of the factors influencing our long, medium and short-term mobility needs (residential location, vehicle ownership and mode-switching behaviour for instance). The project aims to address this research gap by developing mathematical models to predict mobility decisions in different stages of the pandemic as well as in the post-pandemic world. The outputs are intended to enable urban planners and transport providers to quickly respond to changed mobility requirements in the event of a new pandemic. Behavioural responses in the event of a pandemic are complex and heterogeneous. The proposed research hence requires investigating alternative behavioural theories to enrich the traditional activity and mobility models. It also requires extensive data for model estimation. The project proposes to combine survey data with passively generated smart-phone app location data to calibrate these models. The methodological nature of the proposed research and its focus on big data sources makes it particularly suited for the AQM. The proposed methodological work on data fusion will have applications beyond transport.

新冠肺炎疫情导致我们的活动和生活方式偏好发生了实质性变化，并引发了重新思考城市设计的需要。这需要对影响我们长期、中期和短期机动性需求的因素(例如住宅位置、车辆拥有量和模式转换行为)有充分的了解。该项目旨在通过开发数学模型来预测大流行不同阶段以及大流行后世界的流动性决定，以弥补这一研究空白。这些产出旨在使城市规划者和运输提供者能够在发生新的流行病时迅速对变化的机动性要求作出反应。在大流行事件中的行为反应是复杂和多样的。因此，这项拟议的研究需要研究替代行为理论，以丰富传统的活动和流动性模型。它还需要大量的数据来进行模型估计。该项目建议将调查数据与被动生成的智能手机应用程序位置数据相结合，以校准这些模型。拟议研究的方法论性质及其对大数据来源的关注使其特别适合AQM。拟议的关于数据融合的方法学工作将在交通运输以外的领域应用。