SDCN: Sparsity and Diversity Driven Correlation Networks for Traffic Demand Forecasting

Traffic demand forecasting is essential to intelligent transportation systems and is widely used to support urban planning, traffic management and vehicle dispatching. One challenge of this problem is to model the complex spatial-temporal correlation. Although both factors have been studied, many of the existing works have strong limitations. They rely too heavily on the locality assumption (i.e., the local area is more relevant than the remote area) and only use a distance-based correlation measurement. However, the spatial correlation is also global (i.e., areas far away may also be relevant) and sparse. And it’s insufficient to measure the spatial correlation using only the distance measurement. In this paper, a sparsity and diversity driven correlation network is proposed to tackle these issues. Firstly, Multiple sparse correlation graphs are carefully generated to encode sparsity and diversity. Then a newly designed hybrid graph filtering module (HGFM) leverages them to learn a more expressive node representation. Finally, the HGFM-based recurrent filtering module (RFM) is introduced to handle the spatial-temporal correlation. Extensive experiments conducted on real-world datasets demonstrate the competitiveness of our model while showing the significance of sparsity and diversity.

[1]  Paolo Frasconi,et al.  Short-Term Traffic Flow Forecasting: An Experimental Comparison of Time-Series Analysis and Supervised Learning , 2013, IEEE Transactions on Intelligent Transportation Systems.

[2]  Qimai Li,et al.  Label Efficient Semi-Supervised Learning via Graph Filtering , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[4]  Yoshua Bengio,et al.  Gated Feedback Recurrent Neural Networks , 2015, ICML.

[5]  Dit-Yan Yeung,et al.  Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting , 2015, NIPS.

[6]  Qiang Yang,et al.  Bike flow prediction with multi-graph convolutional networks , 2018, SIGSPATIAL/GIS.

[7]  Yisheng Lv,et al.  Short-term traffic flow prediction with LSTM recurrent neural network , 2017, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC).

[8]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[9]  Jure Leskovec,et al.  Inductive Representation Learning on Large Graphs , 2017, NIPS.

[10]  Ugur Demiryurek,et al.  Deep Learning: A Generic Approach for Extreme Condition Traffic Forecasting , 2017, SDM.

[11]  Xiuwen Yi,et al.  DNN-based prediction model for spatio-temporal data , 2016, SIGSPATIAL/GIS.

[12]  Yu Zheng,et al.  Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction , 2016, AAAI.

[13]  Max Welling,et al.  Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.

[14]  Zhonghui Chen,et al.  Short-term traffic flow prediction with Conv-LSTM , 2017, 2017 9th International Conference on Wireless Communications and Signal Processing (WCSP).

[15]  Yinhai Wang,et al.  Deep Stacked Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction , 2017 .

[16]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  João Gama,et al.  Predicting Taxi–Passenger Demand Using Streaming Data , 2013, IEEE Transactions on Intelligent Transportation Systems.

[18]  W. Tobler.  A Computer Movie Simulating Urban Growth in the Detroit Region , 1970 .

[19]  Billy M. Williams,et al.  Adaptive Seasonal Time Series Models for Forecasting Short-Term Traffic Flow , 2007 .

[20]  Zhaohui Wu,et al.  Prediction of urban human mobility using large-scale taxi traces and its applications , 2012, Frontiers of Computer Science.

[21]  U. Feige,et al.  Spectral Graph Theory , 2015 .

[22]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[23]  Xavier Bresson,et al.  Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering , 2016, NIPS.

[24]  Ugur Demiryurek,et al.  Latent Space Model for Road Networks to Predict Time-Varying Traffic , 2016, KDD.

[25]  Jieping Ye,et al.  Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction , 2018, AAAI.

[26]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[27]  Yong Wang,et al.  Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction , 2017, Sensors.