IEEE Systems, Man and Cybernetics Magazine - April 2020 - 41

urban population, ensuring fast, efficient, reliable, and
cheap mobility to urban residents is a challenge for city
authorities. However, with advances in the digital IoT
infrastructure being deployed across cities and the data
collected from them, novel techniques and technologies
are being developed to improve urban mobility.
The clustering of taxi GPS mobility data helps with
understanding the spatiotemporal dynamics for the applications of urban planning and transportation. Kumar
et  al. [147] clustered the origin-destination pairs of the
passenger taxi rides using a hybrid algorithm consisting
of clusiVAT sampling and DBSCAN to provide useful
insights about city mobility patterns, urban hot spots, roadnetwork usage, and general patterns of the crowd movement in the city of Singapore.
There is a growing interest in the problem of extracting
useful information from massive trajectory data sets
derived by various sensing methods. Understanding patterns of pedestrian movement is useful in applications,
such as pedestrian-flow management, public security, and
safety. Extracting pedestrian movement patterns and determining anomalous regions/periods is a useful data-mining
task to be performed on the massive trajectory data sets
generated by the smart city IoT infrastructure.
Li and Leckie [148] applied contour maps and iVAT to
visually identify and analyze areas/periods with anomalous distributions of pedestrian flows. Contour maps are
adopted as the visualization method of the origin-destination flow matrix to describe the distribution of pedestrian
movement in terms of entry/exit areas. By transforming
the origin-destination flow matrix into a dissimilarity
matrix, the iVAT algorithm is used to visually cluster the
most popular and related areas.
Kumar et al. [149] proposed a novel application of VATbased clustering algorithms (VAT, iVAT, and clusiVAT) for
trajectory analysis. They introduced a new clusteringbased anomaly detection framework named iVAT+ and clusiVAT+ and used it for trajectory anomaly detection. Their
approach is based on partitioning the VAT-generated minimum spanning tree using an efficient thresholding
scheme. Trajectories are classified as normal or anomalous based on the number of paths in the clusters. Experiments on the trajectories of vehicles and pedestrians from
a parking lot scene from the real-life Massachusetts Institute of Technology trajectories data set showcase the ability of the proposed method to find natural and informative
trajectory clusters and anomalies.
Another important type of trajectory data collected in
a smart city framework is vehicular trajectories, especially public transport, such as buses, taxis, and so on, Analysis of large-scale vehicle trajectories is important for
understanding urban traffic patterns, particularly for
optimizing public transport routes and frequencies and
improving the decisions made by authorities. Cluster analysis is a fundamental challenge in trajectory mining, but
existing trajectory clustering algorithms are not well
	

suited to large numbers of trajectories in a city road network because of inadequate distance measures between
two trajectories.
Kumar et al. [85], [150] proposed a novel Dijkstra-based
dynamic time warping (DTW) distance measure called
trajDTW, which is suitable for large numbers of overlapping trajectories in a dense road network. They also developed a novel fast-clusiVAT algorithm that can suggest the
number of clusters in a trajectory data set and identify and
visualize the trajectories belonging to each cluster much
faster than clusiVAT. Empirical experiments conducted on
a large-scale taxi trajectory data set consisting of 3.28 million trajectories obtained from the GPS traces of 15,061
taxis in Singapore for one month suggest several trajectory clusters spanning the major expressways of Singapore.
For each cluster, this scheme provides a time-based distribution of trajectories that affords insights into how urban
mobility patterns change with the time of day.
Taking this trajectory-analysis task a step further toward
prediction, Rathore et al. [151] proposed a scalable-clustering and Markov-chain-based hybrid framework, called TrajclusiVAT-based trajectory prediction, for both short- and
long-term trajectories that can handle a large number of
overlapping trajectories in a dense road network.
Smart Grid
The rollout of electricity grid assets with advanced communications capabilities enables new ways to steal energy,
such as false data attacks and remote meter disconnection.
On the other hand, data communicated by these devices
have the potential to improve utility companies' abilities to
combat fraud through computational intelligence techniques. Viegas and Vieira [152] proposed a clustering-based
novelty detection scheme to uncover electricity theft. The
scheme starts by extracting easily interpreted consumption indicators from data collected by smart meters. Fuzzy
clustering is then used to capture the structure of the data
that consists of indicators from benign consumers. The
VAT algorithm is used before clustering to analyze the possibility of data structure characterized by multiple clusters.
The extracted clusters provide the basis for a distancebased novelty-detection model to uncover abnormal data
sent by consumers.
Time Series Data Analysis
Time series data (a measurement of sensor values
obtained from a physical process over some time) is a common form of data. To study the temporal behavior of an
environmental system, scientists need to detect positions
with similar temporal dynamics in large sets of time
series. A well-established approach to quantifying the
number and duration of recurrent states is recurrence
quantification analysis (RQA) [153].
Sips et al. [154] address the scientific question of whether the clustering of time series based on their RQA measures produces a clustering structure that is interpretable
Ap ri l 2020

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IEEE Systems, Man and Cybernetics Magazine - April 2020

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