Abstract
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.
Highlights
Predicting human mobility is important for urban planning, traffic control, and for the general management of a city
We presented eight classes of human mobility models, including the gravity, radiation and intervening opportunity models
We showed that typical modifications applied to mobility models, such as exchanging travel distance for travel time or adding extra parameters to correct for poor performance at small spatial scales, do not generally result in any significant improvement in the model fits, which we measured using the Common Part of Commuters (CPC) and the Common Fraction of Commuters (CFC)
Summary
Predicting human mobility is important for urban planning, traffic control, and for the general management of a city. The diversity of new data sources for traffic prediction described above becomes a promising resource once one considers that human mobility at smaller scales is less likely to behave according to laws as simple as the ones proposed by gravity or radiation models. While there is a large increase in the availability of urban data, such data remain unevenly distributed: there is a lot of data about major cities such as London, New York, Paris and Tokyo, but much less data about smaller or poorer places [29,31,45] This suggests that the failure of small-scale traffic prediction will be dominant in smaller cities, which often have smaller budgets. Ridesharing services and navigation applications are not available in smaller cities All these considerations make a point for models using open data, such as the OpenStreetMap described above, as an affordable way to improve traffic prediction at small spatial scales. We discuss the applicability of our method to different locations and its relation to traffic modelling at different spatial scales
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