Application Research of Cross-Attention Mechanism for Traffic Prediction Based on Heterogeneous Data
Intelligent transportation systems need to be developed with precise traffic flow predictions to reduce traffic accidents, improve overall urban mobility, and mitigate congestion. The intricacy and variety of traffic conditions are often too complex and variable for traditional approaches to handlin...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_01004.pdf |
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| _version_ | 1825206563668754432 |
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| author | Feng Zhihao |
| author_facet | Feng Zhihao |
| author_sort | Feng Zhihao |
| collection | DOAJ |
| description | Intelligent transportation systems need to be developed with precise traffic flow predictions to reduce traffic accidents, improve overall urban mobility, and mitigate congestion. The intricacy and variety of traffic conditions are often too complex and variable for traditional approaches to handling, especially when dealing with heterogeneous event data from several sources like weather variations and traffic incidents. This review highlights the significance of cross-attention mechanisms by examining the developments in integrating multi-source heterogeneous event data for traffic prediction. Examining different approaches used in previous work, the study focuses on the Event-aware Graph Attention Fusion Network (EGAF-Net). This cutting-edge model efficiently integrates and analyzes complex spatial-temporal data. Through an analysis of these methods, the research demonstrates how applying advanced deep learning algorithms and cross-attention processes has significantly improved prediction robustness and accuracy. The results underscore the critical role of heterogeneous data integration in enhancing traffic prediction models, providing insights into current challenges and potential future developments in the field. This thorough analysis aims to direct future research endeavors and open the door for more dependable and effective intelligent transportation systems. |
| format | Article |
| id | doaj-art-609d17d85c1548ccad9eea24fe55e2fb |
| institution | Kabale University |
| issn | 2271-2097 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-609d17d85c1548ccad9eea24fe55e2fb2025-02-07T08:21:10ZengEDP SciencesITM Web of Conferences2271-20972025-01-01700100410.1051/itmconf/20257001004itmconf_dai2024_01004Application Research of Cross-Attention Mechanism for Traffic Prediction Based on Heterogeneous DataFeng Zhihao0Faculty of Data Science, City University of MacauIntelligent transportation systems need to be developed with precise traffic flow predictions to reduce traffic accidents, improve overall urban mobility, and mitigate congestion. The intricacy and variety of traffic conditions are often too complex and variable for traditional approaches to handling, especially when dealing with heterogeneous event data from several sources like weather variations and traffic incidents. This review highlights the significance of cross-attention mechanisms by examining the developments in integrating multi-source heterogeneous event data for traffic prediction. Examining different approaches used in previous work, the study focuses on the Event-aware Graph Attention Fusion Network (EGAF-Net). This cutting-edge model efficiently integrates and analyzes complex spatial-temporal data. Through an analysis of these methods, the research demonstrates how applying advanced deep learning algorithms and cross-attention processes has significantly improved prediction robustness and accuracy. The results underscore the critical role of heterogeneous data integration in enhancing traffic prediction models, providing insights into current challenges and potential future developments in the field. This thorough analysis aims to direct future research endeavors and open the door for more dependable and effective intelligent transportation systems.https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_01004.pdf |
| spellingShingle | Feng Zhihao Application Research of Cross-Attention Mechanism for Traffic Prediction Based on Heterogeneous Data ITM Web of Conferences |
| title | Application Research of Cross-Attention Mechanism for Traffic Prediction Based on Heterogeneous Data |
| title_full | Application Research of Cross-Attention Mechanism for Traffic Prediction Based on Heterogeneous Data |
| title_fullStr | Application Research of Cross-Attention Mechanism for Traffic Prediction Based on Heterogeneous Data |
| title_full_unstemmed | Application Research of Cross-Attention Mechanism for Traffic Prediction Based on Heterogeneous Data |
| title_short | Application Research of Cross-Attention Mechanism for Traffic Prediction Based on Heterogeneous Data |
| title_sort | application research of cross attention mechanism for traffic prediction based on heterogeneous data |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_01004.pdf |
| work_keys_str_mv | AT fengzhihao applicationresearchofcrossattentionmechanismfortrafficpredictionbasedonheterogeneousdata |