ETA Prediction with Graph Neural Networks in Google Maps

1. ETA Prediction with Graph Neural Networks in
Google Maps
Austin Derrow-Pinion, Jennifer She, David Wong, Oliver Lange,
Todd Hester, Luis Perez
Archived
2021. 09. 10
Hyunwook Lee

2. Contents
• Overview
• Preliminaries
• Methods
• Experiment
• Conclusion

3. 2
Overview: What is ETA?
• ETA: Estimated time of arrival  Equivalent to travel time estimation

4. 3
Overview

5. 4
Preliminaries
• Goal: ETA for the supersegment S and segment s in variety of horizon T
 Supersegment: 𝑺 = 𝑆, 𝐸 , 𝑠 ∈ 𝑆 & 𝑒𝑖𝑗 ∈ 𝐸
• Input features
 speed, supersegment travel time
• Real-time information is provided for 17 2-minutes windows
• Historical data is provided for 5 8-minutes windows (average across the past 17 weeks)
 Segment-wise features – including road classifications, length, and priority
• Embeddings for supersegment/segment are given

6. 5
Methods: What is Graph Network?
• φ: update function
• ρ: aggregation function
• u: global attributes

7. 6
Methods: What is Graph Network?
• Well-known GN block configurations
• In this work, they utilize 3 Full GN blocks – each of
them operates as encoder, processor, and decoder
• Simple Spatial Attention, GCNs can be seen as one
kind of (d)

8. 7
Methods: MetaGradients
• First used in Reinforcement Learning
• Set learning rate as hyperparameter
 update both learning rate and model parameter simultaneously
• Symbols
 𝜏: training examples
 𝜃: model parameters
 η: meta-parameter (in this work, learning rate)

9. 8
Methods: Others
• To reduce the variance
• Huber Loss
 More robust for the outlier than MSE
 More sensitive/exact in range(strongly convex)
• Exponential Moving Average(EMA) of Parameters
 𝜃EMA = α 𝜃EMA + (1 - α) 𝜃
 Can utilize more stable parameters
 Note: not applied directly in training

10. 9
Methods: Model Training
• Note: We should train individual model for each h-minutes prediction
• Utilize combination of various loss functions
 Loss functions in supersegment/segment/cumulative-segment-level
 𝐿 = 𝑙𝑠𝑠 + 𝜆𝑠𝑙𝑠 + 𝜆𝑠𝑐𝑙𝑠𝑐
 𝑦 𝑐,𝑗,𝑡+ℎ
(𝑖)
= 𝑗
𝑦𝑗,𝑡+ℎ
𝑖
 𝑓𝑘
(𝑖)
is free flow time

11. 10
Experiment Results

12. 11
Experiment Results: Variance

13. 12
Experiment Results: Analysis

14. 13
Conclusion & Discussion
• Simple GNN w/ other methods can achieve remarkable performance
• Embedding & other costs can be reduced
 Meta-Learning approaches
• Pre-defined supersegment can be helpful for the ETA
 Prediction for N routes(or segments) is much expansive than prediction for M
supersegment – M << N
• Where they utilize updated edge representation?

15. 14
Appendix
• Graph Networks
 https://arxiv.org/pdf/1806.01261.pdf
• MetaGradients
 https://arxiv.org/pdf/1805.09801.pdf
• Author’s Explanation Video
 https://www.youtube.com/watch?v=a6WCZn7kOhk