TITLE: CityNet: A Multi-city Multi-modal Dataset for Smart City Applications 

ABSTRACT: Data-driven approaches have been applied to many problems in urban computing.
However, in the research community, such approaches are commonly studied under
data from limited sources, and are thus unable to characterize the complexity
of urban data coming from multiple entities and the correlations among them.
Consequently, an inclusive and multifaceted dataset is necessary to facilitate
more extensive studies on urban computing. In this paper, we present CityNet, a
multi-modal urban dataset containing data from 7 cities, each of which coming
from 3 data sources. We first present the generation process of CityNet as well
as its basic properties. In addition, to facilitate the use of CityNet, we
carry out extensive machine learning experiments, including spatio-temporal
predictions, transfer learning, and reinforcement learning. The experimental
results not only provide benchmarks for a wide range of tasks and methods, but
also uncover internal correlations among cities and tasks within CityNet that,
with adequate leverage, can improve performances on various tasks. With the
benchmarking results and the correlations uncovered, we believe that CityNet
can contribute to the field of urban computing by supporting research on many
advanced topics.
Data-driven approaches have been applied to many problems in urban computing.
However, in the research community, such approaches are commonly studied under
data from limited sources, and are thus unable to characterize the complexity
of urban data coming from multiple entities and the correlations among them.
Consequently, an inclusive and multifaceted dataset is necessary to facilitate
more extensive studies on urban computing. In this paper, we present CityNet, a
multi-modal urban dataset containing data from 7 cities, each of which coming
from 3 data sources. We first present the generation process of CityNet as well
as its basic properties. In addition, to facilitate the use of CityNet, we
carry out extensive machine learning experiments, including spatio-temporal
predictions, transfer learning, and reinforcement learning. The experimental
results not only provide benchmarks for a wide range of tasks and methods, but
also uncover internal correlations among cities and tasks within CityNet that,
with adequate leverage, can improve performances on various tasks. With the
benchmarking results and the correlations uncovered, we believe that CityNet
can contribute to the field of urban computing by supporting research on many
advanced topics.
CityNet: A Multi-city Multi-modal Dataset for Smart City Applications
http://arxiv.org/abs/2106.15802v1