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Open AccessArticle

Short-Term Prediction of Bus Passenger Flow Based on a Hybrid Optimized LSTM Network

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College of Information Science and Engineering, Ocean University of China, No. 238, Songling Road, Qingdao 266100, China
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Laboratory for Regional Oceanography and Numerical Modeling, Qingdao National Laboratory for Marine Science and Technology, No. 1, Wenhai Road, Qingdao 266237, China
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CAS Key Laboratary of Ocean Circulation and Waves, Institute of Oceanology, Center for Ocean Mega-Science, Chinese Academy of Sciences, No. 7 Nanhai Road, Qingdao 266071, China
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Pilot National Laboratory for Marine Science and Technology, Qingdao National Laboratory for Marine, No. 1, Wenhai Road, Qingdao 266237, China
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Qingdao Surveying & Mapping Institute, No. 189 Shandong Road, Qingdao 266000, China
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Ant Financial Services Group, Z Space No. 556 Xixi Road, Hangzhou 310000, China
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Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(9), 366; https://doi.org/10.3390/ijgi8090366
Received: 6 May 2019 / Revised: 10 August 2019 / Accepted: 21 August 2019 / Published: 22 August 2019
(This article belongs to the Special Issue Deep Learning and Computer Vision for GeoInformation Sciences)
The accurate prediction of bus passenger flow is the key to public transport management and the smart city. A long short-term memory network, a deep learning method for modeling sequences, is an efficient way to capture the time dependency of passenger flow. In recent years, an increasing number of researchers have sought to apply the LSTM model to passenger flow prediction. However, few of them pay attention to the optimization procedure during model training. In this article, we propose a hybrid, optimized LSTM network based on Nesterov accelerated adaptive moment estimation (Nadam) and the stochastic gradient descent algorithm (SGD). This method trains the model with high efficiency and accuracy, solving the problems of inefficient training and misconvergence that exist in complex models. We employ a hybrid optimized LSTM network to predict the actual passenger flow in Qingdao, China and compare the prediction results with those obtained by non-hybrid LSTM models and conventional methods. In particular, the proposed model brings about a 4%–20% extra performance improvements compared with those of non-hybrid LSTM models. We have also tried combinations of other optimization algorithms and applications in different models, finding that optimizing LSTM by switching Nadam to SGD is the best choice. The sensitivity of the model to its parameters is also explored, which provides guidance for applying this model to bus passenger flow data modelling. The good performance of the proposed model in different temporal and spatial scales shows that it is more robust and effective, which can provide insightful support and guidance for dynamic bus scheduling and regional coordination scheduling. View Full-Text
Keywords: passenger flow; short-term prediction; long short-term memory network; hybrid optimization algorithm passenger flow; short-term prediction; long short-term memory network; hybrid optimization algorithm
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Han, Y.; Wang, C.; Ren, Y.; Wang, S.; Zheng, H.; Chen, G. Short-Term Prediction of Bus Passenger Flow Based on a Hybrid Optimized LSTM Network. ISPRS Int. J. Geo-Inf. 2019, 8, 366.

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