Hello, everyone, I'm Zhang Wen. It's my pleasure to share with you my paper,
which named "Real-time Travel Time Estimation with Sparse Reliable Surveillance Information".
My presentation is divided into five parts, including background, introduction, methods,
experiments and conclusion. First, let's look at the background.
Real-time travel time estimation is important for intelligent transportation.
Existing methods have dependencies on the coverage and quality of historical travel
information and ignore the real-time traffic features.
Therefore, how to use real-time traffic information? The surveillance system captures accurate
and reliable traffic information. However, camera-equipped intersections are
sparse. To address such technical challenges, we propose
a novel method for the real-time OD travel time estimation.
Our main contributions are as follows. We devise a multi-layer GCN to infer the real-time
traffic statuses based on surveillance information. We learn the representation of road networks
with graph-embedding techniques. We enhance the representation of road networks
by extracting traffic patterns with an encoder-decoder network.
We utilize a multi-task learning framework to improve the model generalization.
As shown in Figure 2, the framework consists of three major components:
a multi-layer GCN-based network for inferring the real-time traffic statuses
an embedding layer for static raw features and dynamic traffic patterns
and a multi-task representation learning framework. We infer traffic statuses in three steps.
First, supplement historical traﬀic volumes for camera-free intersections
Second, abstract traﬀic volumes feature map
Third, infer real-time traﬀic statuses. A multi-layer modified GCN is used for the
traffic volumes inference, which is shown in Figure 3.
We construct the Laplacian matrix by traffic volumes of adjacent intersections.
Then, we obtain high-dimensional correlations of traffic, as illustrated in Figure 4.
In order to infer real-time traﬀic statuses, we reform generated feature maps and employ
a GCN-based network. Then how to embed the static raw features
and dynamic road traﬀic patterns? In this paper, static raw features include
road network, time interval and weather. And we learn dynamic traffic patterns with
an encoder-decoder-based method. We also utilize a multi-task representation
learning framework to improve our model. First, we analyze the correlations.
The result is demonstrated in Figure 5. Travel distance and number of road segments
are both positively correlated with travel time.
So, we construct the multi-task learning framework, by setting the main task as the travel time,
and by setting two auxiliary tasks as the travel distance and the number of road segments.
For experiments, both taxicab trajectories and surveillance information are three months
in SIP and Shezhen. We first compare the performance of our method
with or without occasional events. Figure 10 shows that RTTE achieves the best
performance. We investigate the performance of our model
with diﬀerent camera density. Result shows that the performance decreased
with the decrease of the density of camera. We also evaluate the proposed framework on
diﬀerent time periods in Figure 11. As shown in this figure, the RTTE method always
outperforms all baselines. We evaluate the performance of our method
on diﬀerent OD distances in Figure 12. As illustrated in this figure, the performance
of RTTE is the most stable. In general, we proposed a novel real-time
travel time estimation framework with sparse surveillance information.
We propose a novel multi-layer modified GCN for traffic status inference.
We learn the dynamic traffic pattern features by an encoder-decoder network.
We build a fused framework to estimate the OD travel time.
Experiments on real datasets show that our method is better than baseline solutions.
That’s all. Thanks.