hello everyone my name is johan van from rodgers university today uh it's my great honor to present
our work named CellSense: Human Mobility Recovery via Cellular Network Data Enhancement
this is a joint work of Rutgers and SIAT Shenzhen institute of advanced technology in
china this is the online on my present presentation first i will give you a short introduction
as we know human mobility modeling is important for many real-world applications
like mobile computing urban planning and location-based services cellular billing records
is one of the most important data sources for human mobility modelling due to its
high penetration rate and low collection cost however there is one major problem in cellular
billing data which is data sparsity because the data is collected with human activities
like calls in a phone or connecting for surfing internet in this figure
we want to provide a method for data recovery on cbr data for a fine-grained human mobility modeling to
achieve this goal there are two major challenges first as the left figure shows more than half
cellphone users visit more than one new location every week and around 20 percent of users visited more than
eight new locations this will introduce challenges for mobility estimation and data recovery because
we don't have historical data for the new locations for that specific user second
at the right figure shows there is a high uncertainty in the sensing granularity among users
the sensing interval differs significantly among users around
73 percent of users has a sensing granularity that is larger than one minute while 26 percent of users
the time window the sensing granularity is larger than 10 minutes
many works has been done on human mobility study on cellular billing data so we can use a two
dimension table like here to categorize all the works one dimension can be
continuity for achieving either continuous or discontinuous mobility the other dimension is
at the level of mobility either collective or individual level mobility how do we fit
all the works into this table uh we find there is empty space which is a combination of continuous
and individual level of mobility that's where we can uniquely position our work so to summarize
our target problem is to recover missing observations of individual
human mobility in sparse cellular billing record data
so that's the short induction then next is a design this is the data set that we use
CBR data collected by a major network operator in the city and some open source data like open road
map data to solve uncertainty and data sparsity on individual user data we designed a system with two
major components one is the left side collective mobility modeling the other is on the right side
context-aware individual mobility modeling in this first step we estimate the average travel time on the mobility graph
based on collective user mobility patterns in the second step we estimate the location
and modality of individual users based on machine learning solution for the collective
mobility inference first we have this route and status inference we create a mobility graph then
select the road candidates then apply the pruning methods to reduce number of candidates we also
have this status estimation for individual users with the status of this individuals based on his
data or historical observations the travel time inference is
built on the routing and status inference we estimate the travel time by a Gaussian
distribution the second step then apply a expectation-maximization method by
joining optimize the travel time and the routes of individual users for the individual
mobility inference we use a bi-directional GRU network to integrate both past and future
individual mobility records also the collective mobility we got from the collective mobility
inference module the travel time especially and also if we integrate the contextual data
like we mentioned in the previous slides the road network data the next is our evaluation
the metric we use is a geographic distance
to estimate the estimation error we have three base lines one is
hidden markov model second one is this deep transport based on a lstm model third one is a trip-gen it is
a trip generation method based on the statistical features of the user mobility
so that's the evaluation result we find our model can always achieve a lower estimated error
in conclusion we start with the sparse CBR data and design data recovery system with two components
to achieve the dense mobility data thank you that's my presentation