Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand Prediction-Reference-Cited by-同舟云学术

Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand Prediction

Published:2022-08-14 Issue: Volume: Page:
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Container-title:Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Han Liangzhe¹,Ma Xiaojian¹,Sun Leilei²,Du Bowen¹,Fu Yanjie³,Lv Weifeng¹,Xiong Hui⁴

Affiliation:

1. Beihang University, Beijing, China

2. Beihang University & Peng Cheng Laboratory, Beijing, China

3. University of Central Florida, Orlando, FL, USA

4. Hong Kong University of Science and Technology, Hong Kong, UNK, China

Funder

the National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3534678.3539273

Reference31 articles.

1. Lei Bai , Lina Yao , Can Li , Xianzhi Wang , and Can Wang . 2020 . Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020 , NeurIPS 2020, December 6--12, 2020, virtual. Lei Bai, Lina Yao, Can Li, Xianzhi Wang, and Can Wang. 2020. Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6--12, 2020, virtual.

2. XGBoost

3. Deep Multi-Scale Convolutional LSTM Network for Travel Demand and Origin-Destination Predictions

4. Prediction of City-Scale Dynamic Taxi Origin-Destination Flows Using a Hybrid Deep Neural Network Combined With Travel Time

5. Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting

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