Diagnosing the performance of human mobility models at small spatial scales using volunteered geographical information.
Diagnosing the performance of human mobility models at small spatial scales using volunteered geographical information.
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使用自愿提供的地理信息诊断小空间尺度上的人员流动模型的性能。
DOI：
10.1098/rsos.191034
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发表时间：
2019
影响因子：
3.5
通讯作者：
Camargo CQ
中科院分区：
文献类型：
--
作者：
Camargo CQ
对当地人口流动模式进行准确建模是城市决策者当前的一个核心关切，它既影响到公共交通资源的短期部署，也影响到交通基础设施的长期规划。然而，虽然宏观层面的人口流动模型（如重力和辐射模型）已经发展完善，但微观层面的替代模型供应却少得多，已知大多数宏观模型在较小的地理尺度上表现不佳。在本文中，我们采取了第一步来弥补这一缺陷，利用两个新的数据集来分析宏观层面的人类流动模型在哪里以及为什么会崩溃。我们展示了如何从OpenStreetMap免费提供的数据有关的土地使用组成的不同地区的英国牛津郡周围的县可以用来诊断移动模型和了解他们的旅行类型-和低估时，与经验量来自汇总，匿名的智能手机位置数据。我们主张新的建模策略，超越粗糙的地理学，如距离和人口，对不同地区的机会进行详细的，粒度的理解。
Accurate modelling of local population movement patterns is a core, contemporary concern for urban policymakers, affecting both the short-term deployment of public transport resources and the longer-term planning of transport infrastructure. Yet, while macro-level population movement models (such as the gravity and radiation models) are well developed, micro-level alternatives are in much shorter supply, with most macro-models known to perform poorly at smaller geographical scales. In this paper, we take a first step to remedy this deficit, by leveraging two novel datasets to analyse where and why macro-level models of human mobility break down. We show how freely available data from OpenStreetMap concerning land use composition of different areas around the county of Oxfordshire in the UK can be used to diagnose mobility models and understand the types of trips they over- and underestimate when compared with empirical volumes derived from aggregated, anonymous smartphone location data. We argue for new modelling strategies that move beyond rough heuristics such as distance and population towards a detailed, granular understanding of the opportunities presented in different regions.