Hi everyone. I'm Yu Yang from Rutgers University
Today, it's my great honor to present our work: extending coverage of stationary
sensing systems with mobile sensing systems for human mobility modeling
This is a joint work with Peking University and SIAT
Our work is set in the background of human mobility modeling
which is an important topic because it enables many applications and services
For example, for the city administrators
they can optimize urban planning according to where the crowd is
For the public, predicting the future mobility helps to prevent
possible public safety issues And individual users could also get benefits
from many location-based services
To model human mobility
previous works generally achieve this from two aspects
stationary sensing and mobile sensing according to the data collection approaches
In stationary sensing, the sensing
systems are generally stationary such as cameras, wi-fi access points, and
smart card swiping stations When users are nearby, we can know the
locations of users In mobile sensing, the sensing systems
generally move with the user such as a mobile app and car navigation
systems These systems continue collecting users' locations
We found that we can actually put these
two approaches in a coordination where the x-axis is the spatial coverage
and the y-axis is the population detection rate
So why is it? let's see a simple example
For example, there is a stationary sensing system
consisting of two cameras A and B Basically all the users
in the camera's view could be captured, which means it has a high population
detection rate. However, considering its limited
deployment, after leaving the coverage, we cannot capture them anymore
For mobile sensing such as users installing a navigation app, since it
moves with users, it has high spatial coverage.
However, for a specific mobile app or a navigation system
it generally only covers limited users which leads to a low population detection rate.
So far, I hope I convince you that two
sensing approaches have complementary features
Now, we introduce our goal can we have an ideal sensing approach
with both features Suppose we have a user u*, which is
only captured by the stationary sensing system
then our goal is to inform the mobility of u* after leaving the coverage of the
stationary sensing system without any mobile sensing system
To achieve this our key idea is to utilize user
similarity For example, we have a small number of
users u1, u2, and u3. We have their complete
mobility traces provided by a mobile sensing system
we find both u3 and u* are
detected by A and B at a closed time Therefore, we guess
u* may have similar mobility as
u3 after leaving A and B However, it's not trivial to measure
the similarity In our work, we design a framework called
Mohen to do it Our goal is to find users with similar
mobility in the mobile sensing system
However, the fact is that we can only measure them in the
stationary sensor system since only the the stationary sensing system can capture
all the individual users
To establish this bridge we design a mobility sequence encoding
model to make the measurable similarity approach to the
to the objective similarity Then based on the found similar users
we design a mobility sequence prediction model
to infer the next location of users after leaving the coverage of stationary
sensing In this way, we virtually extend the
sensing coverage of the stationary sensing system
In our evaluation, we are based on two real-world systems
in a city-scale testbed a highway toll collection systems
as a stationary sensing system and a vehicle GPS logging system
as a mobile sensor system For example, this figure shows the coverage of our systems
these red points are the toll collection stations
blue and green points are the GPS locations of vehicles
We compare our model with three baselines two of them are based on single sensing systems
and one is based on the fusion of two systems
the result shows that our model is
better than the baselines of using stationary sensing system alone
and the naive fusion of two systems
and also we find the performance is competitive to the
oracle model based on the mobile sensing system
Finally, I would like to summarize this presentation with two take-away messages
First, by measuring similarity across different sensing systems
we could extend the sensing ability of a single sensing system
Second, naive similarity metrics cannot capture the inherent relationship
between two sensing systems while dedicated measurements are needed
for more information please check our
paper and websites Thanks