Attention mechanism‐based model for short‐term bus traffic passenger volume prediction
Abstract To explore the relevance between bus stops and make the real‐time prediction of bus passenger flow more accurate, this paper proposes a Traffic Forecast Model based on the Attention mechanism (TFMA). The model combines data preprocessing with bus stops’ information coding to predict short‐t...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-04-01
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| Series: | IET Intelligent Transport Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/itr2.12302 |
| _version_ | 1797852194794373120 |
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| author | Zhenyu Mei Wanting Yu Wei Tang Jiahao Yu Zhengyi Cai |
| author_facet | Zhenyu Mei Wanting Yu Wei Tang Jiahao Yu Zhengyi Cai |
| author_sort | Zhenyu Mei |
| collection | DOAJ |
| description | Abstract To explore the relevance between bus stops and make the real‐time prediction of bus passenger flow more accurate, this paper proposes a Traffic Forecast Model based on the Attention mechanism (TFMA). The model combines data preprocessing with bus stops’ information coding to predict short‐term bus passenger flow based on the real‐time relevance of the bus stops. First of all, the paper conducts a statistical analysis of the actual public transportation card data of Suzhou, China, and obtains the characteristics of real‐time relevance of different bus stops. Secondly, bus route and bus stop information, the passenger flow rate of change, weather, date, and other related factors are integrated into the coding information of the bus stops. Then the method relies on the Attention mechanism to calculate the real‐time relevance of the bus stops parallelly; the core algorithm also uses a multi‐headed mechanism to increase the connection of the channel and the residual error, further improving the prediction ability. Finally, this article uses actual data from Suzhou's public transport for verification. The results show that: In terms of accuracy, TFMA outperforms multiple linear regression, GRU, and LightGBM, reaching a very high accuracy of nearly 90%. |
| first_indexed | 2024-04-09T19:29:49Z |
| format | Article |
| id | doaj.art-4ddb83589cb34c7ea567baad5734e458 |
| institution | Directory Open Access Journal |
| issn | 1751-956X 1751-9578 |
| language | English |
| last_indexed | 2024-04-09T19:29:49Z |
| publishDate | 2023-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Intelligent Transport Systems |
| spelling | doaj.art-4ddb83589cb34c7ea567baad5734e4582023-04-05T04:22:13ZengWileyIET Intelligent Transport Systems1751-956X1751-95782023-04-0117476777910.1049/itr2.12302Attention mechanism‐based model for short‐term bus traffic passenger volume predictionZhenyu Mei0Wanting Yu1Wei Tang2Jiahao Yu3Zhengyi Cai4Balance Architecture Research Center Zhejiang University Hangzhou ChinaBalance Architecture Research Center Zhejiang University Hangzhou ChinaBalance Architecture Research Center Zhejiang University Hangzhou ChinaBalance Architecture Research Center Zhejiang University Hangzhou ChinaBalance Architecture Research Center Zhejiang University Hangzhou ChinaAbstract To explore the relevance between bus stops and make the real‐time prediction of bus passenger flow more accurate, this paper proposes a Traffic Forecast Model based on the Attention mechanism (TFMA). The model combines data preprocessing with bus stops’ information coding to predict short‐term bus passenger flow based on the real‐time relevance of the bus stops. First of all, the paper conducts a statistical analysis of the actual public transportation card data of Suzhou, China, and obtains the characteristics of real‐time relevance of different bus stops. Secondly, bus route and bus stop information, the passenger flow rate of change, weather, date, and other related factors are integrated into the coding information of the bus stops. Then the method relies on the Attention mechanism to calculate the real‐time relevance of the bus stops parallelly; the core algorithm also uses a multi‐headed mechanism to increase the connection of the channel and the residual error, further improving the prediction ability. Finally, this article uses actual data from Suzhou's public transport for verification. The results show that: In terms of accuracy, TFMA outperforms multiple linear regression, GRU, and LightGBM, reaching a very high accuracy of nearly 90%.https://doi.org/10.1049/itr2.12302attention mechanismbus stop information encodingintelligent transportationmulti‐headed mechanismreal‐time relevance of bus stopsshort‐term bus traffic passenger flow prediction |
| spellingShingle | Zhenyu Mei Wanting Yu Wei Tang Jiahao Yu Zhengyi Cai Attention mechanism‐based model for short‐term bus traffic passenger volume prediction IET Intelligent Transport Systems attention mechanism bus stop information encoding intelligent transportation multi‐headed mechanism real‐time relevance of bus stops short‐term bus traffic passenger flow prediction |
| title | Attention mechanism‐based model for short‐term bus traffic passenger volume prediction |
| title_full | Attention mechanism‐based model for short‐term bus traffic passenger volume prediction |
| title_fullStr | Attention mechanism‐based model for short‐term bus traffic passenger volume prediction |
| title_full_unstemmed | Attention mechanism‐based model for short‐term bus traffic passenger volume prediction |
| title_short | Attention mechanism‐based model for short‐term bus traffic passenger volume prediction |
| title_sort | attention mechanism based model for short term bus traffic passenger volume prediction |
| topic | attention mechanism bus stop information encoding intelligent transportation multi‐headed mechanism real‐time relevance of bus stops short‐term bus traffic passenger flow prediction |
| url | https://doi.org/10.1049/itr2.12302 |
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