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

2019冠状病毒病大流行导致我们的活动和生活方式偏好发生了重大变化，引发了重新思考城市设计的必要性。这需要充分了解影响我们长期、中期和短期移动需求的因素（例如，住宅位置、车辆所有权和模式转换行为）。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策，从而弥补这一研究缺口。产出的目的是使城市规划者和运输提供者能够在发生新的大流行病时迅速应对变化了的流动性需求。在发生大流行时的行为反应是复杂和异质的。因此，拟议的研究需要调查替代行为理论，以丰富传统的活动和流动模型。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源的关注使其特别适合AQM。拟议的数据融合方法学工作将应用于运输以外的领域。

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.