Volume 42 Issue 2
Apr.  2024
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Article Navigation >  Journal of Transport Information and Safety  > 2024  >  42(2): 158-165
CHEN Xiqun, SHEN Loutao, LI Junyi, LI Chuanjia. A Short-term Prediction for OD Passenger Flow in Urban Rail Transit Based on Heterogeneous Data Feature Extraction[J]. Journal of Transport Information and Safety, 2024, 42(2): 158-165. doi: 10.3963/j.jssn.1674-4861.2024.02.016
Citation: CHEN Xiqun, SHEN Loutao, LI Junyi, LI Chuanjia. A Short-term Prediction for OD Passenger Flow in Urban Rail Transit Based on Heterogeneous Data Feature Extraction[J]. Journal of Transport Information and Safety, 2024, 42(2): 158-165. doi: 10.3963/j.jssn.1674-4861.2024.02.016
shu

A Short-term Prediction for OD Passenger Flow in Urban Rail Transit Based on Heterogeneous Data Feature Extraction

doi: 10.3963/j.jssn.1674-4861.2024.02.016
  • 1.

    Institute of Intelligent Transportation Systems, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China

  • 2.

    Polytechnic Institute, Zhejiang University, Hangzhou 310015, China

  • Received Date: 2023-05-29
    Available Online: 2024-09-14
    Abstract
  • As an important basis for rail transit operations and travel choices, prediction for origin-destination (OD) passenger flow in urban rail transit is of great significance in intelligent transportation systems. The conventional convolutional neural network (CNN) mostly focuses on local OD features due to their translation invariance and local sensitivity. To improve its global perception capacity in OD matrix modeling, a heterogeneous data feature extraction machine (HDFEM) model is proposed based on attention mechanism. The model constructs a heterogeneous data OD spatio-temporal tensor and a geographic information tensor from the perspective of spatio-temporal characteristics and land use attributes. It segments and encodes heterogeneous data tensors via a tensor coding layer to obtain the features of tensor blocks in heterogeneous data tensors. Then, it connects the features of each tensor block through the attention mechanism to extract the spatial correlation among various OD matrix parts. This approach not only realizes multi-source heterogeneous data fusion, but also extracts remote features of OD matrix. Meanwhile, the model uses long short-term memory (LSTM) network to deal with the OD temporal feature. Compared with the convolutional neural network-based prediction model, the results on the Hangzhou metro auto fare collection (AFC) dataset show that the mean square error, mean absolute error, and normalized root mean square error of the HDFEM model decreases by 4.1%, 2.5%, and 2%, respectively. The importance of extracting whole spatial features for OD passenger flow prediction of urban rail transit is verified.

     

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