Hey everyone, my name is Man Luo. Today I'm gonna present our
work.
The data driven demand prediction for fast expanding
electric vehicle sharing systems.
As we all know, mobility is the lifeblood of modern cities. And
recently, it is often said that the mobility solution for future
Smart Cities should be electric and shared: because it offers
clean, reliable, and affordable ways to get from location A to
B, and back again. It has been increasingly popular all over
the world, from London to Singapore, Shanghai, and
attracts lots of research interests from government,
industry and academia.
So how do they work in practice? Basically, it contains a number
of stations with charging facilities scattered around the
urban space. And the users can pick up vehicles from them,
drive around and return to the same or any other stations. And
then they plug in vehicles for charging and pay based on the
rental time. So a transaction has been completed.
As in other urban systems, for shared e-mobility, a key problem
is how to predict the mobility demand across space and time. It
is an essential ability to answer many what-if questions.
For instance, if a new station were to be deployed to a
particular location at a specific time, how popular it
will be, and how an existing station will perform at a future
time.
This may be straightforward if the system is static, but in
practice, the mobility system can be highly dynamic. For
instance, the shared e-mobility system we worked with, was
continuously expanding at an impressive speed, having two
times more stations, and four times more vehicles within 12
months.
And we can see that the system also expanded to new areas
without previous coverage. So we need to embrace such dynamics
and make predictions for locations with limited or no
historical data.
In addition, we also observe that the impact of such dynamics
is often non trivial, which makes prediction even more
challenging. For example, we see that the same event of deploying
a new station may have opposite effects:
on the left this new stations steals demand from the existing
ones, because it was deployed in a large shopping centre, so
everyone tends to use it. On the other hand, this new station
boosts the demand of his neighbours since it was deployed
at the other terminal of the airport, which creates lots of
short transits.
This means we need to design an approach that can capture all
those complex dynamics in spatial temporal domain.
To do that, we use two layers of encoders. Where the first layer
in the form of LSTMs captures local temporal dependencies, for
example, the historical mobility at particular points, and the
weather history. And on top of that, we use a graph
convolutional neural network to encode the spatial correlations
from various data modalities, such as point of interests and
the road network. And our model can also do multi-scale
prediction. In that it can predict both long term expected
demand and short term instant demand over spatial temporal
domain by plugging in different final layers to the network.
We evaluated over approach with data collected in Shanghai over
one year, which has roughly 4000 charging stations, 16,000
vehicles, 0.4 million users and the 7 million records. We also
use road network data, points of interests data, and weather
records in Shanghai.
Results have shown that our approach could achieve much
better prediction performance, up to three times gain over the
state-of-the-art. More importantly, it is also more
resilient to perturbations induced by different levels of
dynamics. For example, how fast the events of deploying or
closing stations happen as shown in the right figures.
Many thanks for listening