Hi I am Yiwei Song, a Master student from Peking University.
It is my honor to present our paper, HERMAS: A Human Mobility Embedding Framework with Large-scale Cellular Signaling Data.
This is a joint work with Rutgers University and iFlytek.
Let’s see the outline of this presentation.
I will first begin with the introduction of this paper.
With the extension of cellular networks and cell phones, SD become valuable spatio-temporal data.
Signaling data are produced by the interactions between cell phones and cell towers,
such as phone calls, messages, and Internet surfing.
In general, the data format of the SD records contains time-stamp, tower ID,
user ID (anonymous ID), as well as the signaling type.
On the one hand, cell phones have become a necessity for citizens.
On the other hand, SD are recorded by all sorts of cellular activities that may happen anytime.
Subsequently, the high penetration rates of SD benefit the researchers to investigate human mobility applications.
In this paper, we focus on how to construct an
efficient representation learning framework by mining human mobility
using SD for many human mobility applications.
A lot of work has been done based on different models.
We use two criteria to divide them into two categories.
Based on the penetration rates, we divide them into
low penetration rates and high penetration rates.
And based on the number of applications,
we divide them into single applications and multiple applications.
However, these works have a common weakness that
they only focus either on low penetration rates or single application,
limiting the spatio-temporal coverage, which could be addressed by our work.
In addition, we have explored new opportunities to mine human mobility derived from high penetration rates of SD.
Due to the load balance strategies as well as environmental factors,
cell phones are not always connected to the closest cell tower,
which means that they may connect to any connectable cell towers based on real-world network loads.
We define this phenomenon as the Ping Pong Effect,
and the potentially connectable towers as neighbor towers.
We then investigate human mobility by mining the context information and neighbor information provided by PPE.
The contributions of our paper are listed below.
Considering PPE, we design a representation learning framework called HERMAS
for SD dedicated to urban-wide human mobility related applications.
And this framework is evaluated by a city-scale SD dataset with high spatio-temporal coverage.
Now we finish the introduction part. Let’s go to the system design.
An overview of our system is shown here.
It is composed of three main parts: preprocessing,
mobility embedding framework(noted as HERMAS), and applications.
Specifically, SD are transformed into trajectory data
as the input of HERMAS after a preprocessing part.
HERMAS is the kernel representation learning framework
to embed cell towers by a two-stage embedding algorithm.
Then, we employ a model of Recurrent Neural Network to embed the trajectory.
Finally, trajectory embedding is used as the input
to the downstream applications as an evaluation of HERMAS.
Now let’s put emphasis on embedding techniques in HERMAS.
In this section, we learn an embedding for each tower by two parts:
context tower embedding and neighbor tower embedding.
First, we extract sequential information from individual SD trajectories.
We borrow the idea of skip-gram here.
If several towers appear sequentially in an SD trajectory,
then we use the center tower to predict the towers that appear before and after it
and embed them in the same latent space.
Next, we extract neighbor tower information brought by PPE.
We argue that the connection patterns are perhaps irregular
but could be formulated via exploring the large-scale trajectories.
We here model connection patterns between the central tower
and the corresponding neighbor towers based on the attention mechanism.
And finally, we sum up the context tower embedding
and the neighbor tower embedding into the tower embedding.
Now we go to the evaluation part.
We first evaluate trajectory similarity measurement,
which is used to study whether our model efficiently extracts
discriminative information from the moving trajectory.
Specifically, three different distance measuring methods
are employed with common metrics: hitting rate and recall.
Here we can see HERAMS has a higher performance than other baselines
including different embedding techniques or HERMAS variants.
Then we also evaluate our model in the scenario of user profiling by two classification tasks.
Here we import an extra user dataset
containing transit user and traveler users for evaluation.
Followed by widely used classification methods (RF, Dtree, and XGBoost),
the embeddings derived from HERMAS outperform other baselines.
Now let’s see the conclusion part.
In this paper, we design a novel human mobility representation learning framework
called HERMAS to embed SD for large-scale human mobility related applications.
Specifically, we design a representation learning algorithm
for the characteristics of PPE of SD,
which distinguishes our framework from the previous works.
And we also evaluate our framework by a city-scale dataset on two human mobility related applications.
That’s all. Thanks for listening.