Massive amounts of data that characterize how people meet their economic needs, interact within social communities, and use 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 article, the usefulness of a recently available big transport data set—the New York City 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 data set. In addition, the high temporal resolution of the taxi data provides 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.

中文翻译：

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大交通数据在城市交通空间交互模型中的潜力和缺陷

城市正在生成大量数据，这些数据表征人们如何满足经济需求、在社会社区内互动以及使用共享资源。利用这些不断增加的数据流对于理解城市动态至关重要。在交通建模的背景下，这些新数据源是否提供了更好地理解空间过程的机会，在很大程度上仍然未知。因此，在本文中，在空间交互建模框架内评估了最近可用的大型交通数据集（纽约市出租车行程数据）的有用性。这是通过首先将使用出租车数据的模型的参数估计与使用传统通勤数据集的模型的参数估计进行比较来完成的。此外，出租车数据的高时间分辨率提供了一种令人兴奋的方式来探索运动行为的潜在动态。演示了如何获得数据的时间子集的参数估计并随着时间的推移进行比较以研究移动性动态。这项工作的结果表明，大交通数据的一个缺陷是它对不同现象的建模不太有用；然而，建模不同城市活动的高频时间动态的潜力很大。