Abstract:This article provides an empirical evaluation of a hierarchical approach to modeling commuting flows. As the gravity family of spatial interaction models represents a benchmark for empirical evaluation, we begin by reviewing basic aspects of these models. The hierarchical modeling framework is the same that Thorsen, Ube, and Naevdal (1999) used. However, because some modifications are required to construct a more workable model, we undertake a relatively detailed presentation of the model, rather than merely r… Show more
Massive amounts of data that characterize how people meet their economic needs, interact within social communities, and utilize shared resources are being produced by cities. Harnessing these ever-increasing data streams is crucial for understanding urban dynamics. Within the context of transportation modeling it still remains largely unknown whether or not these new data sources provide the opportunity to better understand spatial processes. Therefore, in this paper, the usefulness of a recently available big transport dataset-the New York City (NYC) taxi trip data-is evaluated within a spatial interaction modeling framework. This is done by first comparing parameter estimates from a model using the taxi data to parameter estimates from a model using a traditional commuting dataset. In addition, the high temporal resolution of the taxi data provide an exciting means to explore potential dynamics in movement behavior. It is demonstrated how parameter estimates can be obtained for temporal subsets of data and compared over time to investigate mobility dynamics. The results of this work indicate that a pitfall of big transport data is that it is less useful for modeling distinct phenomena; however, there is a strong potential for modeling high frequency temporal dynamics of diverse urban activities.
Massive amounts of data that characterize how people meet their economic needs, interact within social communities, and utilize shared resources are being produced by cities. Harnessing these ever-increasing data streams is crucial for understanding urban dynamics. Within the context of transportation modeling it still remains largely unknown whether or not these new data sources provide the opportunity to better understand spatial processes. Therefore, in this paper, the usefulness of a recently available big transport dataset-the New York City (NYC) taxi trip data-is evaluated within a spatial interaction modeling framework. This is done by first comparing parameter estimates from a model using the taxi data to parameter estimates from a model using a traditional commuting dataset. In addition, the high temporal resolution of the taxi data provide an exciting means to explore potential dynamics in movement behavior. It is demonstrated how parameter estimates can be obtained for temporal subsets of data and compared over time to investigate mobility dynamics. The results of this work indicate that a pitfall of big transport data is that it is less useful for modeling distinct phenomena; however, there is a strong potential for modeling high frequency temporal dynamics of diverse urban activities.
Spatial interaction models commonly use discrete zones to represent locations. The computational requirements of the models normally arise with the square of the number of zones or worse. For computationally intensive models, such as land usetransport interaction models and activity-based models for city regions, this dependency of zone size is a long-standing problem that has not disappeared even with increasing computation speed in PCsit still forces modelers to compromise on the spatial resolution and extent of model coverage as well as on the rigor and depth of model-based analysis. This article introduces a new type of discrete zone system, with the objective of reducing the time for estimating and applying spatial interaction models while maintaining their accuracy. The premise of the new system is that the appropriate size of destination zones depends on the distance to their origin zone: at short distances, spatial accuracy is important and destination zones must be small; at longer distances, knowing the precise location becomes less important and zones can be larger. The new method defines a specific zone map for every origin zone; each origin zone becomes the focus of its own map, surrounded by small zones nearby and large zones farther away. We present the theoretical formulation of the new method and test it with a model of commuting in England. The results of the new method are equivalent to those of the conventional model, despite reducing the number of zone pairs by 96% and the computation time by 70%
Spatial interaction and spatial structure are foundational geographical abstractions, though there is often variation in how they are conceptualized and deployed in quantitative models. In particular, the last five decades have produced an exceptional diversity regarding the role of spatial structure within spatial interaction models. This is explored by outlining the initiation and development of the notion of spatial structure within spatial interaction modeling and critically reviewing four methodological approaches that emerged from ongoing debate about the topic. The outcome is a comprehensive coverage of the past and a sketch of one potential path forward for advancing this long-standing inquiry.
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