This document summarizes a research paper that presents DeepTP, a joint prediction model for travel demands and traffic flows. DeepTP uses four modules: 1) a future spatio-temporal encoding module, 2) a past traffic sequence encoding module, 3) a graph-based correlation encoding module, and 4) a final estimation module. It encodes three types of embeddings - past traffic data, region-level correlations, and temporal periodicity - to capture inter-traffic correlations, region-level similarities, and periodic patterns in demand and flow. The model was evaluated on real-world traffic datasets from two cities and was shown to outperform other baselines in joint demand and flow prediction.

1. An Effective Joint Prediction Model for Travel
Demands and Traffic Flows
ICDE International Conference on Data Engineering, 2021
Haitao Yuan, Guoliang Li, Zhifeng Bao, Ling Feng
July 9, 2021
Presenter: Kyunghwan Mun

2. Contents
• Overview of the Paper
• Background and Motivation
• Introduction
• Methodology
• Experiment
• Conclusion and Discussion

3. Overview of the Paper
• Model Framework of DeepTP
• Four Modules
▪ 𝑀𝑔 : Future spatio-temporal information encoding module
▪ 𝑀𝑝 : Past traffic sequence encoding module
▪ 𝑀𝑔 : Graph-based correlation encoding module
▪ 𝑀𝑒 : Final estimation module
2

4. Background and Motivation
• Many transportation services
▪ Taxi dispatching
▪ Route planning
▪ Congestion avoidance
⟹ Traffic flow prediction, Travel demand prediction
• Only a single type of traffic data
▪ Traffic flow prediction or Travel demand prediction
⟹ Estimate both the traffic flows and travel demand (Joint prediction)
3

5. Introduction
• Inter-traffic Correlations
▪ Later outflows are influenced by earlier demands
▪ Earlier inflows can influence later demands
• Region-level Correlations
▪ Similar location-wise
▪ Similar at dimensions other than location
4

6. Introduction
• Temporal Periodicity
▪ Day-in-week
• 1 to 7 for Monday-Sunday
▪ Time-in-day
• 1 to
24×60
∆𝑡
where ∆𝑡 is size of time interval (minutes)
▪ Example 1)
• Tuesday 8:55 ~ 9:00 AM → Day-in-week (2), Time-in-day (
9×60
5
)
5

7. Three embeddings - 1
• Past traffic data encoding
▪ To capture inter-traffic correlations
 Correlation between past and future traffic data
 Inter-traffic correlations
6
Past traffic data encoding
Region embedding
Future time interval
embedding

8. Three embeddings - 2
• Region embedding
▪ To capture region-level correlations
 Connectivity-aware graph
 Neighbor-aware graph
 Similarity-aware graph
7
Past traffic data encoding
Region embedding
Future time interval
embedding

9. Three embeddings - 3
• Future time interval embedding
▪ To capture temporal periodicity
 Two Connection graphs (𝑔𝑤, 𝑔𝑑)
 Unsupervised graph embedding methods (node2vec)
8
Past traffic data encoding
Region embedding
Future time interval
embedding

10. Definition of graphs - 1
• Connectivity-aware graph
▪ 𝒎 × 𝒏 regions 🚌…
▪ Adjacency matrix
• Each element means the weight the edge
• The weight of an edge : The number of edges linking 𝒓𝒊 and 𝒓𝒋 🚌…
9
Connectivity-aware graph
Neighbor-aware graph
Similarity-aware graph

11. Definition of graphs - 2
• Neighbor-aware graph
▪ 𝑚 × 𝑛 regions
▪ Adjacency matrix
• 1 if there exists an edge, otherwise 0
10
Connectivity-aware graph
Neighbor-aware graph
Similarity-aware graph

12. Definition of graphs - 3
• Similarity-aware graph
▪ 𝑚 × 𝑛 regions
▪ Adjacency matrix
• A vector correspoding each region
• Number of POIs, number of roads, and type of regions 🚌…
• Calculate the cosine similarity
11
Connectivity-aware graph
Neighbor-aware graph
Similarity-aware graph

13. 12
Methodology
• DeepTP (model)
▪ Four modules
• Future spatio-temporal information encoding (𝑀𝑐)
• Past traffic sequence encoding (𝑀𝑝)
• Graph-based correlation encoding (𝑀𝑔)
• Final estimation (𝑀𝑒)

14. 13
Methodology
• DeepTP (model)

15. 14
Experiment
• Datasets
▪ Road Network & Regions & Time Intervals
• Chengdu (CD) & Xi’an (XA) Road Network
▪ Region-based Traffic Flows and Travel Demands
▪ External Data for Context Features
• Event features
(holiday : 1, otherwise : 0 / season indicator 1-4 )
• Meterological fetures (weather properties → one-hot codes)
▪ Training, Validation and Test Data
• 70% : 10% : 20%

16. 15
Experiment
• Baselines
▪ Historical Average (HA)
▪ Autoregressive Integrated Moving Average (ARIMA)
▪ Gradient Boosting Regression Tree (GBRT)
▪ Deep Sequence Model (Seq)
▪ Deep Multi-View Spatio-temporal Network (DMVST)
▪ Multi-Graph Convolution Network (MGCN)
▪ Deep Meta Learning (ST-Meta)

17. 16
Conclusion and Discussion
• Demand prediction
▪ Grid-based Embedding
• Yan, An, and Bill Howe. "Fairness-Aware Demand Prediction for New Mobility." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 34. No. 01. 2020.
• Bai, Lei, et al. "Spatio-temporal graph convolutional and recurrent networks for citywide passenger demand prediction." Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 2019.
• Geng, Xu, et al. "Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting." Proceedings of the AAAI conference on artificial intelligence. Vol. 33. No. 01. 2019.
• Yao, Huaxiu, et al. "Deep multi-view spatial-temporal network for taxi demand prediction." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 32. No. 1. 2018.
• Bai, Lei, et al. "Stg2seq: Spatial-temporal graph to sequence model for multi-step passenger demand forecasting." arXiv preprint arXiv:1905.10069 (2019).
• Liu, Lingbo, et al. "Contextualized spatial–temporal network for taxi origin-destination demand prediction." IEEE Transactions on Intelligent Transportation Systems 20.10 (2019): 3875-3887.
▪ Station-based Embedding
• Li, Can, et al. "Knowledge adaption for demand prediction based on multi-task memory neural network." Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 2020.
• Li, Youru, et al. "Learning heterogeneous spatial-temporal representation for bike-sharing demand prediction." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 33. No. 01. 2019.
▪ Station-based & Station-less Embedding
• Ye, Junchen, et al. "Coupled layer-wise graph convolution for transportation demand prediction." arXiv preprint arXiv:2012.08080 (2020).
▪ Hexagon Regions Embedding
• Zheng, Bolong, et al. "SOUP: Spatial-Temporal Demand Forecasting and Competitive Supply." arXiv preprint arXiv:2009.12157 (2020).

18. 17
Conclusion and Discussion
• Demand prediction
▪ Hexagon Regions Embedding
• Zheng, Bolong, et al. "SOUP: Spatial-Temporal Demand Forecasting and Competitive Supply." arXiv preprint arXiv:2009.12157 (2020).

19. 18
Conclusion and Discussion
• Flow / Volume Prediction
▪ Grid-based Embedding
• Wang, Senzhang, et al. "Multi-task adversarial spatial-temporal networks for crowd flow prediction." Proceedings of the 29th ACM international conference on information & knowledge management. 2020.
• Pan, Zheyi, et al. "Urban traffic prediction from spatio-temporal data using deep meta learning." Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019.
• Yao, Huaxiu, et al. "Revisiting spatial-temporal similarity: A deep learning framework for traffic prediction." Proceedings of the AAAI conference on artificial intelligence. Vol. 33. No. 01. 2019.
• Lin, Ziqian, et al. "Deepstn+: Context-aware spatial-temporal neural network for crowd flow prediction in metropolis." Proceedings of the AAAI conference on artificial intelligence. Vol. 33. No. 01. 2019.
• Liang, Yuxuan, et al. "Fine-Grained Urban Flow Prediction." Proceedings of the Web Conference 2021. 2021.
• He, Zhixiang, Chi-Yin Chow, and Jia-Dong Zhang. "STCNN: A spatio-temporal convolutional neural network for long-term traffic prediction." 2019 20th IEEE International Conference on Mobile Data Management (MDM). IEEE, 2019.
▪ Road-based Embedding
• Li, Mingqian, et al. "Traffic Flow Prediction with Vehicle Trajectories." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 35. No. 1. 2021.
▪ Graph-based Embedding
• Fang, Shen, et al. "GSTNet: Global Spatial-Temporal Network for Traffic Flow Prediction." IJCAI. 2019.
• Guo, Shengnan, et al. "Attention based spatial-temporal graph convolutional networks for traffic flow forecasting." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 33. No. 01. 2019.

20. 19
Conclusion and Discussion
• 𝒎 × 𝒏 regions 🚌…
→ Grids / Polygon / Station / Station-less / Graph / Graph to grids using longest longitude & latitude
• The weight of an edge : The number of edges linking 𝒓𝒊 and 𝒓𝒋 🚌…
→ The diverse methods of weighting an edge
• Number of POIs, number of roads, and type of regions 🚌…
→ Detailed analysis corresponding to categories of POI
• Cluster-based Demand, Flow / Volume, and Origin-Destination Prediction ?

21. Thank you
Any questions?