An Improved Deep Spatial-Temporal Hybrid Model for Bus Speed Prediction
For resolving or alleviating the transportation problems, it is necessary to efficiently manage the public transportation and provide public transport services with high quality and advocate green travel, which rely on accurate traffic data. In order to obtain more accurate bus speed in the future,...
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2020-01-01
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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2020/2143921 |
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doaj-14c52e10dda74adb8de81c1312ed6b412020-11-25T01:59:26ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472020-01-01202010.1155/2020/21439212143921An Improved Deep Spatial-Temporal Hybrid Model for Bus Speed PredictionHuawei Zhai0Licheng Cui1Weishi Zhang2Xiaowei Xu3Ruijie Tian4Information Science and Technology School, Dalian Maritime University, Dalian 116026, ChinaPublic Security Information Department, Liaoning Police College, Dalian 116036, ChinaInformation Science and Technology School, Dalian Maritime University, Dalian 116026, ChinaUniversity of Arkansas at Little Rock, Little Rock, AR, USAInformation Science and Technology School, Dalian Maritime University, Dalian 116026, ChinaFor resolving or alleviating the transportation problems, it is necessary to efficiently manage the public transportation and provide public transport services with high quality and advocate green travel, which rely on accurate traffic data. In order to obtain more accurate bus speed in the future, this paper proposed a novel dynamic hierarchical spatial-temporal network model based on Grey Relation Analysis (EGRA), the convolutional neural network (CNN), and the gated recurrent unit (GRU). The proposed model is named the DHSTN; it exploited EGRA to analyze and choose the suitable candidate line sections with high impacts on the target section and, then, construct a multilayer structure based on the CNN, GRU, and attention mechanism to analyze and capture the spatial and temporal dependency, and finally, the extreme learning machine (ELM) is exploited for the fusion of the long-term and short-term dependency to predict the bus speed variation in the next time interval. Comparative experiments indicate that the DHSTN has better performances, the mean absolute error is around 2.6, and it meets the real requirements.http://dx.doi.org/10.1155/2020/2143921 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Huawei Zhai Licheng Cui Weishi Zhang Xiaowei Xu Ruijie Tian |
spellingShingle |
Huawei Zhai Licheng Cui Weishi Zhang Xiaowei Xu Ruijie Tian An Improved Deep Spatial-Temporal Hybrid Model for Bus Speed Prediction Mathematical Problems in Engineering |
author_facet |
Huawei Zhai Licheng Cui Weishi Zhang Xiaowei Xu Ruijie Tian |
author_sort |
Huawei Zhai |
title |
An Improved Deep Spatial-Temporal Hybrid Model for Bus Speed Prediction |
title_short |
An Improved Deep Spatial-Temporal Hybrid Model for Bus Speed Prediction |
title_full |
An Improved Deep Spatial-Temporal Hybrid Model for Bus Speed Prediction |
title_fullStr |
An Improved Deep Spatial-Temporal Hybrid Model for Bus Speed Prediction |
title_full_unstemmed |
An Improved Deep Spatial-Temporal Hybrid Model for Bus Speed Prediction |
title_sort |
improved deep spatial-temporal hybrid model for bus speed prediction |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2020-01-01 |
description |
For resolving or alleviating the transportation problems, it is necessary to efficiently manage the public transportation and provide public transport services with high quality and advocate green travel, which rely on accurate traffic data. In order to obtain more accurate bus speed in the future, this paper proposed a novel dynamic hierarchical spatial-temporal network model based on Grey Relation Analysis (EGRA), the convolutional neural network (CNN), and the gated recurrent unit (GRU). The proposed model is named the DHSTN; it exploited EGRA to analyze and choose the suitable candidate line sections with high impacts on the target section and, then, construct a multilayer structure based on the CNN, GRU, and attention mechanism to analyze and capture the spatial and temporal dependency, and finally, the extreme learning machine (ELM) is exploited for the fusion of the long-term and short-term dependency to predict the bus speed variation in the next time interval. Comparative experiments indicate that the DHSTN has better performances, the mean absolute error is around 2.6, and it meets the real requirements. |
url |
http://dx.doi.org/10.1155/2020/2143921 |
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