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Deep graph convolutional networks for incident driven traffic speed prediction
1. Deep Graph Convolutional Networks for
Incident-Driven Traffic Speed Prediction
ACM Conference on Information and Knowledge Management, 2020
Qinge Xie, Tiancheng Guo, Yang Chen, Yu Xiao, Xin Wang, and Ben Y. Zhao
March 12, 2021
Presenter: KyungHwan Moon
2. Contents
โข Overview of the Paper
โข Background and Motivation
โข Introduction
โข Deep Incident-Aware Graph Convolutional Network
โข Experiment
โข Conclusion and Discussion
3. 2
Overview of the Paper
Spatio-temporal Learning
(Graph Convolutional Network + LSTM)
๏ฌ Deep Incident-Aware Graph Convolutional Network
โ Graph Convolutional Network (GCN)
: Capture spatial features of road networks
โ LSTM
: Capture the time evolution patterns of traffic speeds
โ RNN
: Contains loops that allow information to persist, so
previous incidents will affect the traffic conditions,
which may lead to the occurrence of future incidents
โ Fully Connected Layer (FC)
: Capture the long-term periodic features
Incident Learning
(RNN)
Periodic Learning
(Fully Connected Layer)
4. 3
Background and Motivation
๏ฌ Previous studies on traffic speed prediction predominately used
spatio-temporal and context features for prediction
โ They have not made good use of the impact of traffic incidents
๏ฌ Incident-driven prediction framework consists of three processes
โ Propose a critical incident discovery method to discover traffic incidents with
high impact on traffic speed
โ Design a binary classifier, which uses deep learning methods to extract the
latent incident impact features
โ Propose DIGC-Net to effectively incorporate traffic incident, spatio-temporal,
periodic and context features for traffic speed prediction
5. 4
Introduction
๏ฌ Traffic speed prediction has been a challenging problem for decades
โ Congestion control [17]
โ Vehicle routing planning [14]
โ Urban road planning [28]
โ Travel time estimation [9]
๏ฌ There are two main challenges for incident-driven traffic speed prediction problem
โ The impact of traffic incidents is complex and varies significantly across incidents
- It is unreasonable to treat all traffic incidents equally for traffic speed prediction,
which may even negatively impact the prediction performance
โ The impact of traffic incidents on adjacent roads will be affected by external factors
like incident occurrence time, incident type and the road topology structure
- Need to extract the latent impact features of traffic incidents to improve
the traffic prediction
6. 5
Introduction
๏ฌ Propose a critical incident discovery method to quantify the impact of urban traffic
incidents on traffic flows to tackle the first challenge
โ Consider both anomalous degree and speed variation of adjacent roads to discover the
critical traffic incidents
๏ฌ Propose a binary classifier which uses deep learning methods to extract the latent
impact features of incidents to tackle the second challenge
โ Extract the latent impact features from the middle layer of the classifier, where the latent
features are continuous and filtered
8. 7
Introduction
๏ฌ Datasets
โ San Francisco (SFO)
โ New York City (NYC)
๏ฌ Problem Formulation and Preprocessing
โ Reconstruction of the road network
- Use the road segment as the node, and use every flow as one node to build the road
network more specifically
- If two flows have points of intersection, add an edge to connect node and node
โ Problem formulation
- Use to represent the speed of flow at time slot t
- For every speed snapshot of the road network, get a vector of all flows
(N is the total number of flows)
- Given the re-build road graph and a T-length historical real-time speed sequence of all flows,
task is to predict future speeds of every flow in the city where k is the prediction length
9. 8
Deep Incident-Aware Graph Convolutional Network
(DIGC-Net)
๏ฌ Methodology
โ Congestion Incident
- M : center point of the incident
- r : the radius of the impact range
- The circle with the center M and
radius r stands for the region
affected by the incident
- Define that if the center of flows is in the circle, then
the flows might be affected by the incident
- The blue, red and green lines represent three flows which
might be affected by the incident, respectively
Urban Critical Incident Discovery
Extract The Latent Incident
Impact Features
Incident-driven
traffic speed prediction
10. 9
Deep Incident-Aware Graph Convolutional Network
(DIGC-Net)
๏ฌ Methodology
โ Congestion Incident
- Analyze each candidate flow that whether it will truly be
affected by the incident
- Use a variant of the method proposed in [41] to compute
the anomalous degree of each flow
- To compute the anomalous degree of a region is based on
its historically similar regions in the city
- The sudden drop of speed similarity of a region and its
historically similar regions indicates the occurrence of
urban anomalies
Urban Critical Incident Discovery
Extract The Latent Incident
Impact Features
Incident-driven
traffic speed prediction
11. 10
Deep Incident-Aware Graph Convolutional Network
(DIGC-Net)
๏ฌ Methodology
โ Pair-wise Similarity of Flows
- The pair-wise similarity is calculated by
: ๐ ๐๐,๐๐
[๐กโ๐+1:๐ก]
= ๐(๐ฃ๐๐
๐กโ๐+1:๐ก
, ๐ฃ๐๐
[๐กโ๐+1:๐ก]
)
where P is to calculate Pearson correlation coefficient
[20] of two speed sequences
- The similarity matrix S of all flows at t is calculated by
the following equation:
๐๐ก
=
๐ ๐0,๐0
[๐กโ๐+1:๐ก]
โฏ ๐ ๐0,๐๐โ1
[๐กโ๐+1:๐ก]
โฎ โฑ โฎ
๐ ๐๐โ1,๐0
[๐กโ๐+1:๐ก]
โฏ ๐ ๐๐โ1,๐๐โ1
[๐กโ๐+1:๐ก]
where N is the total number of flows in the city
Urban Critical Incident Discovery
Extract The Latent Incident
Impact Features
Incident-driven
traffic speed prediction
12. 11
Deep Incident-Aware Graph Convolutional Network
(DIGC-Net)
๏ฌ Methodology
โ Similarity Decrease Matrix D
- The decreased similarity of each flow pair from time slot
t-1 to t
- D at time slot t is calculated by:
๐ท๐ก = max(0, ๐๐กโ1 โ ๐ ๐ก)
โ Anomalous Degree A
- Use similarity matrix S and similarity decrease matrix D
- Use a threshold parameter ๐ฟ to capture the historically similar flows
- When the similarity of two flows is greater than or equal
to ๐ฟ, define that they are historically similar
Urban Critical Incident Discovery
Extract The Latent Incident
Impact Features
Incident-driven
traffic speed prediction
13. 12
Deep Incident-Aware Graph Convolutional Network
(DIGC-Net)
๏ฌ Methodology
โ Anomalous Degree A
- Given a flow ๐๐ at time slot t, the historically similar flow
sets of ๐๐ is denoted as
๐ป๐๐
๐ก
= ๐๐ ๐ โ ๐ ๐๐๐ ๐๐,๐
๐ก
= ๐๐,๐
๐ก
โฅ ๐ฟ}
- Anomalous degree of flow ๐๐ at time slot t is calculated by
the following equation:
๐ด๐๐
๐ก
=
๐๐โ๐ป๐๐
๐ก ๐๐,๐
๐กโ1
โ ๐ท๐,๐
๐ก
๐๐โ๐ป๐๐
๐ก ๐๐,๐
๐กโ1
where A is the decrease degree in speed similarity of ๐๐
and its historically similar flows
Urban Critical Incident Discovery
Extract The Latent Incident
Impact Features
Incident-driven
traffic speed prediction
14. 13
Deep Incident-Aware Graph Convolutional Network
(DIGC-Net)
๏ฌ Methodology
โ Anomalous Degree A
Urban Critical Incident Discovery
Extract The Latent Incident
Impact Features
Incident-driven
traffic speed prediction
15. 14
Deep Incident-Aware Graph Convolutional Network
(DIGC-Net)
๏ฌ Methodology
โ Local Anomalous Degree Algorithm
- It will cost a lot to compute the similarity matrix S, the
similarity decrease matrix D and the anomalous degree A
- Propose a local anomalous degree algorithm to speed up
our method based on the spectral clustering algorithm [39]
- Assume that traffic in nearby locations should be similar[32, 36, 45]
- Assume that flows in the same community and in the
spatially nearby regions will be historically similar
- Given, a graph G, perform spectral decomposition and
obtain k graph spatial features of each flow
- Use K-means [8], a common unsupervised clustering
method, to cluster flows into k classes
Urban Critical Incident Discovery
Extract The Latent Incident
Impact Features
Incident-driven
traffic speed prediction
16. 15
Deep Incident-Aware Graph Convolutional Network
(DIGC-Net)
๏ฌ Methodology
โ Local Anomalous Degree Algorithm
Urban Critical Incident Discovery
Extract The Latent Incident
Impact Features
Incident-driven
traffic speed prediction
17. 16
Deep Incident-Aware Graph Convolutional Network
(DIGC-Net)
๏ฌ Methodology
โ Validation of Local Algorithm
- Eigenvectors can effectively capture spatial graph features
- Only need to compute the local values of the similarity matrix ๐,
the similarity decrease matrix ๐ท and the anomalous degree ๐ด
in the same district
- Explore the impact on traffic flows of different urban traffic incidents
Urban Critical Incident Discovery
Extract The Latent Incident
Impact Features
Incident-driven
traffic speed prediction
18. 17
Deep Incident-Aware Graph Convolutional Network
(DIGC-Net)
๏ฌ Methodology
โ Validation of Local Algorithm
Urban Critical Incident Discovery
Extract The Latent Incident
Impact Features
Incident-driven
traffic speed prediction
19. 18
Deep Incident-Aware Graph Convolutional Network
(DIGC-Net)
๏ฌ Methodology
โ Relative Speed Variation ๐
- Given a flow ๐๐ at time t, and the historical speed sequence
[๐ฃ๐๐
๐กโ๐+1
, ๐ฃ๐๐
๐กโ๐+2
, โฏ , ๐ฃ๐๐
๐ก
] of ๐๐ in a T-length time window
- Define the relative speed variation of ๐๐ as follow:
๐ ๐๐
๐ก
=
๐กโฒ=๐กโ๐+1
๐กโฒ=๐ก
๐ฃ๐๐
๐กโฒ
๐
โ ๐ฃ๐๐
๐ก
max(๐ฃ๐๐
๐ก๐
, ๐ฃ๐๐
๐ก๐ +1
, โฏ , ๐ฃ๐๐
๐ก๐
)
use 24 hours (288 intervals) as the normalization window length,
i.e., ๐ก๐ = ๐ก โ 144 ๐๐๐ ๐ก๐ = ๐ก + 144, ๐๐๐ ๐ = 10 ๐๐๐ก๐๐๐ฃ๐๐๐
Urban Critical Incident Discovery
Extract The Latent Incident
Impact Features
Incident-driven
traffic speed prediction
20. 19
Deep Incident-Aware Graph Convolutional Network
(DIGC-Net)
๏ฌ Methodology
โ Relative Speed Variation ๐
Urban Critical Incident Discovery
Extract The Latent Incident
Impact Features
Incident-driven
traffic speed prediction
21. 20
Deep Incident-Aware Graph Convolutional Network
(DIGC-Net)
๏ฌ Methodology
โ Validation of Relative Speed Variation
- Consider three related features:
- Slope of speed variation ๐ [33]
- Recent speed ๐ฃ๐กโ1 [2]
- Historical average speed (๐ฃ) [2]
corresponding to three candidate computing methods of
Relative Speed Variation ๐
1) ๐ ๐+๐ฃ๐กโ1+๐ฃ = ๐ฃ โ ๐ฃ๐ก ร ๐ ร ๐ + ๐ฃ๐กโ1 โ ๐ฃ๐ก ร ๐๐กโ1 ร ๐
2) ๐ ๐ฃ๐กโ1+๐ฃ = ๐ฃ โ ๐ฃ๐ก
ร ๐ + ๐ฃ๐กโ1
โ ๐ฃ๐ก
ร ๐
3) ๐ ๐ฃ = |๐ฃ โ ๐ฃ๐ก|
where p and q set to 0.5
Urban Critical Incident Discovery
Extract The Latent Incident
Impact Features
Incident-driven
traffic speed prediction
22. 21
Deep Incident-Aware Graph Convolutional Network
(DIGC-Net)
๏ฌ Methodology
โ Incident Effect Score ๐
- Due to the complementarity of anomalous degree and relative
speed variation, combine both of them to compute the incident
effect score
Given a flow ๐๐ at time slot t, the incident effect score is calculated by:
๐๐๐
๐ก
= ๐ โ ๐ด๐๐
๐ก
+ (1 โ ๐) โ ๐ ๐๐
๐ก
where ๐ is a parameter to control the ratio of ๐ด and ๐
Urban Critical Incident Discovery
Extract The Latent Incident
Impact Features
Incident-driven
traffic speed prediction
23. 22
Deep Incident-Aware Graph Convolutional Network
(DIGC-Net)
๏ฌ Methodology
โ Critical Incidents
- For incidents like mega-events, the traffic flows might be affected
before incidents begin. On the contrary, incidents like traffic collisions
will begin to affect traffic flows after they occurred
Define the flows which are highly affected by accident as
๐๐ max(๐๐๐
๐กโ
๐
2
, ๐๐๐
๐กโ
๐
2+1
, โฏ , ๐๐๐
๐ก+
๐
2
) โฅ ๐}
where ๐ is a threshold parameter
When | ๐๐ max ๐๐๐
๐กโ
๐
2
, ๐๐๐
๐กโ
๐
2
+1
, โฏ , ๐๐๐
๐ก+
๐
2
โฅ ๐ |๐ผ๐
> 0
there is at least one flow is highly affected by the incident ๐ผ๐, we call
๐ผ๐ is a critical incident
Urban Critical Incident Discovery
Extract The Latent Incident
Impact Features
Incident-driven
traffic speed prediction
24. 23
Deep Incident-Aware Graph Convolutional Network
(DIGC-Net)
๏ฌ Methodology
Urban Critical Incident Discovery
Extract The Latent Incident
Impact Features
Incident-driven
traffic speed prediction
25. 24
Deep Incident-Aware Graph Convolutional Network
(DIGC-Net)
๏ฌ Methodology
โ Spatial Learning : GCN
- Adapt graph convolutional network (GCN) to learn the spatial
topology features.
(To capture the topology features in non-Euclidean structures, which
is suitable for road networks)
โ Temporal Learning : LSTM
- Adapt Long Short-Term Memory (LSTM) model as temporal learning
component
(To learn long-term dependency information of time related
sequences)
Urban Critical Incident Discovery
Extract The Latent Incident
Impact Features
Incident-driven
traffic speed prediction
26. 25
Deep Incident-Aware Graph Convolutional Network
(DIGC-Net)
๏ฌ Methodology
โ Context Learning
- Use the following features for context learning
1) Incident type
(Traffic collision and event)
2) Road status
(An incident leads to a road close or not)
3) Start and end hour
(Start time ๐ก๐ and an anticipative end time ๐ก๐ of an incident)
4) Incident duration
(The anticipative duration of an incident
5) Weekday, Saturday or Sunday
Urban Critical Incident Discovery
Extract The Latent Incident
Impact Features
Incident-driven
traffic speed prediction
27. 26
Deep Incident-Aware Graph Convolutional Network
(DIGC-Net)
๏ฌ Methodology
โ Latent incident impact features extraction
Urban Critical Incident Discovery
Extract The Latent Incident
Impact Features
Incident-driven
traffic speed prediction
28. 27
Deep Incident-Aware Graph Convolutional Network
(DIGC-Net)
๏ฌ Methodology
โ Spatio-temporal Learning
- Use the similar structure of spatial and temporal learning of binary
classifier. The output of spatio-temporal learning is ๐๐
โ Incident Learning
- Select all incidents occurred within [t-125min, t-5min] as the incident
learning inputs (the last two hours) and use the pre-trained binary
classifier to extract (๐๐ โ ๐
๐)๐น๐ถ๐ , i.e., the latent incident impact
features of each incident
โ Periodic Learning
- Use the same time slots in the last 5 days to learn the periodic
features. The output of periodic learning ๐๐
Urban Critical Incident Discovery
Extract The Latent Incident
Impact Features
Incident-driven
traffic speed prediction
29. 28
Deep Incident-Aware Graph Convolutional Network
(DIGC-Net)
๏ฌ Methodology
โ Spatio-temporal Learning
- Use the similar structure of spatial and temporal learning of binary
classifier
โ Incident Learning
- Select all incidents occurred within [t-125min, t-5min] as the incident
learning inputs (the last two hours)
โ Periodic Learning
- Use the same time slots in the last 5 days to learn the periodic
features
Urban Critical Incident Discovery
Extract The Latent Incident
Impact Features
Incident-driven
traffic speed prediction
34. 33
Experiment
๏ฌ Conclusion and Discussion
โ Propose a critical incident discovery method to identify urban crucial
incidents and their impact on traffic flows
โ Design a binary classifier to extract the latent incident impact features for
improving traffic speed prediction
โ Propose a Deep Incident-Aware Graph Convolutional Network (DIGC-Net)
to effectively incorporate traffic incident, spatio-temporal, periodical and
weather features for traffic speed prediction