Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-Graph Convolutional-Recurrent Neural Network (MGC-RNN) for
  Short-Term Forecasting of Transit Passenger Flow   Short-term forecasting of passenger flow is critical for transit management
and crowd regulation. Spatial dependencies, temporal dependencies,
inter-station correlations driven by other latent factors, and exogenous
factors bring challenges to the short-term forecasts of passenger flow of urban
rail transit networks. An innovative deep learning approach, Multi-Graph
Convolutional-Recurrent Neural Network (MGC-RNN) is proposed to forecast
passenger flow in urban rail transit systems to incorporate these complex
factors. We propose to use multiple graphs to encode the spatial and other
heterogenous inter-station correlations. The temporal dynamics of the
inter-station correlations are also modeled via the proposed multi-graph
convolutional-recurrent neural network structure. Inflow and outflow of all
stations can be collectively predicted with multiple time steps ahead via a
sequence to sequence(seq2seq) architecture. The proposed method is applied to
the short-term forecasts of passenger flow in Shenzhen Metro, China. The
experimental results show that MGC-RNN outperforms the benchmark algorithms in
terms of forecasting accuracy. Besides, it is found that the inter-station
driven by network distance, network structure, and recent flow patterns are
significant factors for passenger flow forecasting. Moreover, the architecture
of LSTM-encoder-decoder can capture the temporal dependencies well. In general,
the proposed framework could provide multiple views of passenger flow dynamics
for fine prediction and exhibit a possibility for multi-source heterogeneous
data fusion in the spatiotemporal forecast tasks.

24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
10
The categories of this paper are: q-bio
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph
16
The categories of this paper are: stat
13
The categories of this paper are: quant-ph
19
The categories of this paper are: eess
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
37
The categories of this paper are: supr-con