  Given a large collection of urban datasets, how can we find their hidden
correlations? For example, New York City (NYC) provides open access to taxi
data from year 2012 to 2015 with about half million taxi trips generated per
day. In the meantime, we have a rich set of urban data in NYC including
points-of-interest (POIs), geo-tagged tweets, weather, vehicle collisions, etc.
Is it possible that these ubiquitous datasets can be used to explain the city
traffic? Understanding the hidden correlation between external data and traffic
data would allow us to answer many important questions in urban computing such
as: If we observe a high traffic volume at Madison Square Garden (MSG) in NYC,
is it because of the regular peak hour or a big event being held at MSG? If a
disaster weather such as a hurricane or a snow storm hits the city, how would
the traffic be affected?
  While existing studies may utilize external datasets for prediction task,
they do not explicitly seek for direct explanations from the external datasets.
In this paper, we present our results in attempts to understand taxi traffic
dynamics in NYC from multiple external data sources. We use four real-world
ubiquitous urban datasets, including POI, weather, geo-tagged tweet, and
collision records. To address the heterogeneity of ubiquitous urban data, we
present carefully-designed feature representations for various datasets.
Extensive experiments on real data demonstrate the explanatory power on taxi
traffic by using external datasets. More specifically, our analysis suggests
that POIs can well describe the regular traffic patterns. At the same time,
geo-tagged tweets can explain irregular traffic caused by big events and
weather can explain the abnormal traffic drop.
