Community-Aware Multi-Task Transportation Demand Prediction


  • Hao Liu Baidu Research, Beijing, China
  • Qiyu Wu Baidu Research, Beijing, China, Peking University, China
  • Fuzhen Zhuang Key Lab of IIP of Chinese Academy of Sciences (CAS), ICT, CAS, Beijing 100190, China University of Chinese Academy of Sciences, Beijing 100049, China
  • Xinjiang Lu Baidu Research, Beijing, China
  • Dejing Dou Baidu Research, Beijing, China
  • Hui Xiong Rutgers University, USA



Transportation, Applications, Graph Mining, Social Network Analysis & Community, Mining of Spatial, Temporal or Spatio-Temporal Da


Transportation demand prediction is of great importance to urban governance and has become an essential function in many online applications. While many efforts have been made for regional transportation demand prediction, predicting the diversified transportation demand for different communities (e.g., the aged, the juveniles) remains an unexplored problem. However, this task is challenging because of the joint influence of spatio-temporal correlation among regions and implicit correlation among different communities. To this end, in this paper, we propose the Multi-task Spatio-Temporal Network with Mutually-supervised Adaptive task grouping (Ada-MSTNet) for community-aware transportation demand prediction. Specifically, we first construct a sequence of multi-view graphs from both spatial and community perspectives, and devise a spatio-temporal neural network to simultaneously capture the sophisticated correlations between regions and communities, respectively. Then, we propose an adaptively clustered multi-task learning module, where the prediction of each region-community specific transportation demand is regarded as distinct task. Moreover, a mutually supervised adaptive task grouping strategy is introduced to softly cluster each task into different task groups, by leveraging the supervision signal from one another graph view. In such a way, Ada-MSTNet is not only able to share common knowledge among highly related communities and regions, but also shield the noise from unrelated tasks in an end-to-end fashion. Finally, extensive experiments on two real-world datasets demonstrate the effectiveness of our approach compared with seven baselines.




How to Cite

Liu, H., Wu, Q., Zhuang, F., Lu, X., Dou, D., & Xiong, H. (2021). Community-Aware Multi-Task Transportation Demand Prediction. Proceedings of the AAAI Conference on Artificial Intelligence, 35(1), 320-327.



AAAI Technical Track on Application Domains