DeepUrbanMomentum: An Online Deep-Learning System for Short-Term Urban Mobility Prediction

Authors

  • Renhe Jiang The University of Tokyo
  • Xuan Song The University of Tokyo, National Institute of Advanced Industrial Science and Technology
  • Zipei Fan The University of Tokyo
  • Tianqi Xia The University of Tokyo
  • Quanjun Chen The University of Tokyo
  • Satoshi Miyazawa The University of Tokyo
  • Ryosuke Shibasaki The University of Tokyo

Keywords:

Information systems applications, Human mobility

Abstract

Big human mobility data are being continuously generated through a variety of sources, some of which can be treated and used as streaming data for understanding and predicting urban dynamics. With such streaming mobility data, the online prediction of short-term human mobility at the city level can be of great significance for transportation scheduling, urban regulation, and emergency management. In particular, when big rare events or disasters happen, such as large earthquakes or severe traffic accidents, people change their behaviors from their routine activities. This means people's movements will almost be uncorrelated with their past movements. Therefore, in this study, we build an online system called DeepUrbanMomentum to conduct the next short-term mobility predictions by using (the limited steps of) currently observed human mobility data. A deep-learning architecture built with recurrent neural networks is designed to effectively model these highly complex sequential data for a huge urban area. Experimental results demonstrate the superior performance of our proposed model as compared to the existing approaches. Lastly, we apply our system to a real emergency scenario and demonstrate that our system is applicable in the real world.

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Published

2018-04-25

How to Cite

Jiang, R., Song, X., Fan, Z., Xia, T., Chen, Q., Miyazawa, S., & Shibasaki, R. (2018). DeepUrbanMomentum: An Online Deep-Learning System for Short-Term Urban Mobility Prediction. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11338

Issue

Section

Computational Sustainability and Artificial Intelligence