Hi, everyone. This is Huiqun Huang from the Department of Computer Science and
Engineering, University of Connecticut. In this presentation, I will present our work of
“Multi-head spatio-temporal attention mechanism for urban anomaly event prediction”.
This is the joint work with Xi Yang and my advisor Prof. Suining He. This project is supported,
in part, by the University of Connecticut Research Excellence Program Award
Urban anomaly events, such as vehicle collisions and noise incidents, are the unusual
events which may incur or be accompanied by the abnormal movement of crowd flows. The figure on
the screen shows an example distribution of crowd flows and anomaly events in the city.
When in the early stage or in the absence of anomaly events, the areas on the lower left and
upper right of the city are usually more crowded than other regions. However, people will move to
the surrounding safe or shelter areas during the process of anomaly events. Such sudden
change of crowd flow redistribution along with the occurrences of anomaly events will further
threaten the city safety, city management, traffic, city resource allocation and so on.
From the dynamic of anomaly events and crowd flows, we can find that the occurrences of
urban anomaly events are highly correlated with the abnormal movement of crowd flows.
But how they correlated with each other? The abnormal movements of crowd flows are always
accompanied with the occurrence of urban anomaly events. As shown in the figure on the left,
a deadly stampede occurred in Shanghai while 300,000 people gathered for the 2014 new year
celebration. Also, the occurrence of anomaly events can lead to crowd flow redistribution.
As shown in the figure on the right, a car crash in Florida blocked the movement of crowd flows.
We can learn from these examples that the occurrences of anomaly events always incur
or be accompanied with the redistributions of crowd flows. Therefore, considering the impacts
of crowd flows can improve the prediction of abnormal events. Inspired by the correlation
between abnormal events and crowd flows, in this paper, we aim at forecasting the citywide incoming
abnormal event distribution by utilizing data of both abnormal events and crowd flows.
There are three aspects of challenges in the problem of citywide anomaly event distribution
prediction. First, there are complex spatial and temporal correlations among anomaly events.
The occurrence of anomaly events in a region may be influenced by the anomaly events from
multiple spatial ranges to different degrees. Anomaly events with similar temporal contexts
tend to have similar occurrence frequencies. Second, the movement of crowd flows will also
impact the occurrence of anomaly events in the spatial and temporal dimensions. Last
but not least, the sparseness of the anomaly event and crowd flow data will significantly
degrade the prediction accuracy. To overcome the above three challenges, we
proposed M-STAP, for multi-region anomaly events prediction. It captures the spatial-temporal
correlations between anomaly events & crowd flows, and predicts multi-region anomaly
events by the novel multi-head spatio-temporal attention. To verify the effectiveness of M-STAP,
we conduct extensive experimental studies on real-world crowd flow data & anomaly events data,
and achieve high accuracy in prediction of anomaly event distributions.
In this study, we utilize the crowd flow data and anomaly event data of NYC,
Chicago and Melbourne to conduct our data analysis and experimental studies.
Figs (a) and (b) show the spatial distributions of crowd flows and the anomaly event of crime
complaints of NYC. The warmer the color in the map, the larger the number of the events.
We can find that the spatial distribution of crowd flows and anomaly events are highly
correlated. In addition, we also show temporal patterns of the hourly crowd flows and anomaly
events in three cities. It is shown in the figures that crowd flows and anomaly events
follow similar temporal patterns. To leverage the spatial and temporal
findings of anomaly events and crowd flows, we propose M-STAP to forecast the future citywide
distribution of anomaly events. The figure on the slide illustrates the main structures of
our proposed model. Within a period of time, the anomaly events and crowd flows interact closely to
different degrees between regions. So, capturing these spatial correlations between different
regions would help to understand the spatial dynamic trends of anomaly events and crowd flows.
Motivated by this, we design the Two-Dimensional Spatial Attention Mechanism, to capture both
the global and local spatial correlations between anomaly events and crowd flows. More specifically,
Two-Dimensional Spatial Attention Mechanism is composed of two sub-components.
One is Multi-Head Self-attention Mechanism (MHSAM) and another is Convolutional Operation.
We design the Multi-Head Self-attention Mechanism (MHSAM) to evaluate the pairwise spatial
correlations of crowd flows and anomaly events among all the input regions in each time step.
Next, we utilize the traditional convolution operation to capture the local spatial
correlations of anomaly events and crowd flows among nearby regions by using specific kernel
size and filters. Then we design the Crowd Flow Spatial Attention Mechanism (CFSAM) to capture
the correlations between the anomaly events and crowd flows in each time step.
The intuition is that crowd flows from different regions have varying impacts on the occurrences
of anomaly events. Finally, to differentiate the impact of crowd flows at different time steps on
the occurrence of anomaly events, we proposed the Temporal Attention Mechanism (TAM).
The details of the experimental settings and the evaluation metrics are listed on the screen.
This table shows the experimental results of our proposed model and all the baselines.
We further illustrate the ground-truth and predicted heatmaps of the anomaly events in
NYC of one selected time step in the testing data. We can observe from the heatmaps that the spatial
distributions and the frequencies of each anomaly event are accurately predicted by M-STAP.
In conclusion, in this study, we propose M-STAP, for multi-region anomaly events prediction.
We consider the impacts of crowd flow data from different regions on the anomaly events
in each time step, and the spatial correlations among anomaly events. We also differentiate
the impact of history anomaly events of different time steps on the anomaly event
prediction. We utilize the anomaly event and crowd flow data of NYC, Melbourne and
Chicago to evaluate the effectiveness of our proposed designs, and achieve high accuracy
in citywide anomaly events prediction. These are all of our presentation. Thank
you for your attention. Please visit our website for more information.