Short-term Bus Passenger Flow Prediction Based on Convolutional Long-short-term Memory Network

doi:10.16182/j.issn1004731x.joss.22-1168

Abstract

Abstract:

To address the problem that the traditional short-time passenger flow prediction method does not consider the temporal characteristics similarity between the inter-temporal passenger flows, a short-time passenger flow prediction model k-CNN-LSTM is proposed by combining the improved k-means clustering algorithm with the CNN and the LSTM. The k-means is used to cluster the intertemporal time-series data, the k-value is determined by using the gap-statistic, and a traffic flow matrix model is constructed. A CNN-LSTM network is used to process the short-time passenger flows with spatial and temporal characteristics. The model is tested and parameter tuned by the real dataset. The test results show the model is able to predict the spatially correlated data more accurately.

Key words: CNN, LSTM, spatiotemporal data prediction, k-means clustering, passenger flow prediction

CLC Number:

TP181

Chen Jing, Zhang Zhaochong, Wang Linkai, An Mai, Wang Wei. Short-term Bus Passenger Flow Prediction Based on Convolutional Long-short-term Memory Network[J]. Journal of System Simulation, 2024, 36(2): 476-486.

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模型名称	参数名称	参数值
k-CNN-LSTM	B/E/OPT/LR/A/LOSS	128/300/Adam/0.01/ReLU/MSE
CNN-LSTM	B/E/OPT/LR/A/LOSS	128/300/Adam/0.01/ReLU/MSE
BiLSTM	B/E/OPT/LR/LOSS	128/300/Adam/0.01/MSE
LSTM	B/E/OPT/LR/LOSS	128/300/Adam/0.01/MSE
SVR	Kernel/CV/LOSS	Rbf/10/MSE

模型	商业街			服务中心			第三社区中心
模型	MSE	MAE	SMAPE/%	MSE	MAE	SMAPE/%	MSE	MAE	SMAPE/%
k-CNN-LSTM	227.48	11.30	45.44	164.60	9.77	45.16	150.44	9.37	47.26
CNN-LSTM	280.55	12.15	48.39	217.99	10.79	49.94	186.23	10.34	50.87
Bi-LSTM	271.56	12.10	48.13	191.76	11.18	54.49	168.11	10.19	48.00
LSTM	311.93	12.67	47.85	198.88	10.85	50.98	283.36	13.68	55.68
SVR	252.74	12.90	50.77	164.93	9.82	45.80	160.00	10.27	49.57

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