Predicting Urban Traffic Congestion with VANET Data
The purpose of this study lies in developing a comparison of neural network-based models for vehicular congestion prediction, with the aim of improving urban mobility and mitigating the negative effects associated with traffic, such as accidents and congestion. This research focuses on evaluating th...
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| Format: | Article |
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MDPI AG
2025-04-01
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| Series: | Computation |
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| Online Access: | https://www.mdpi.com/2079-3197/13/4/92 |
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| author | Wilson Chango Pamela Buñay Juan Erazo Pedro Aguilar Jaime Sayago Angel Flores Geovanny Silva |
| author_facet | Wilson Chango Pamela Buñay Juan Erazo Pedro Aguilar Jaime Sayago Angel Flores Geovanny Silva |
| author_sort | Wilson Chango |
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| description | The purpose of this study lies in developing a comparison of neural network-based models for vehicular congestion prediction, with the aim of improving urban mobility and mitigating the negative effects associated with traffic, such as accidents and congestion. This research focuses on evaluating the effectiveness of different neural network architectures, specifically Transformer and LSTM, in order to achieve accurate and reliable predictions of vehicular congestion. To carry out this research, a rigorous methodology was employed that included a systematic literature review based on the PRISMA methodology, which allowed for the identification and synthesis of the most relevant advances in the field. Likewise, the Design Science Research (DSR) methodology was applied to guide the development and validation of the models, and the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology was used to structure the process, from understanding the problem to implementing the solutions. The dataset used in this study included key variables related to traffic, such as vehicle speed, vehicular flow, and weather conditions. These variables were processed and normalized to train and evaluate various neural network architectures, highlighting LSTM and Transformer networks. The results obtained demonstrated that the LSTM-based model outperformed the Transformer model in the task of congestion prediction. Specifically, the LSTM model achieved an accuracy of 0.9463, with additional metrics such as a loss of 0.21, an accuracy of 0.93, a precision of 0.29, a recall of 0.71, an F1-score of 0.42, an MSE of 0.07, and an RMSE of 0.26. In conclusion, this study demonstrates that the LSTM-based model is highly effective for predicting vehicular congestion, surpassing other architectures such as Transformer. The integration of this model into a simulation environment showed that real-time traffic information can significantly improve urban mobility management. These findings support the utility of neural network architectures in sustainable urban planning and intelligent traffic management, opening new perspectives for future research in this field. |
| format | Article |
| id | doaj-art-852bc5bef29d45ef8b459b535fd13b39 |
| institution | OA Journals |
| issn | 2079-3197 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computation |
| spelling | doaj-art-852bc5bef29d45ef8b459b535fd13b392025-08-20T02:28:12ZengMDPI AGComputation2079-31972025-04-011349210.3390/computation13040092Predicting Urban Traffic Congestion with VANET DataWilson Chango0Pamela Buñay1Juan Erazo2Pedro Aguilar3Jaime Sayago4Angel Flores5Geovanny Silva6Department of Systems and Computation, Pontifical Catholic University of Ecuador, Esmeraldas Campus PUCESE, Esmeraldas 080101, EcuadorFaculty of Engineering, University of Chimborazo UNACH, Riobamba 060101, EcuadorFaculty of Mechanical Engineering, Escuela Superior Politécnica de Chimborazo ESPOCH, Riobamba 060155, EcuadorFaculty of Informatics and Electronics, Escuela Superior Politécnica de Chimborazo ESPOCH, Riobamba 060155, EcuadorDepartment of Systems and Computation, Pontifical Catholic University of Ecuador, Esmeraldas Campus PUCESE, Esmeraldas 080101, EcuadorFaculty of Informatics and Electronics, Escuela Superior Politécnica de Chimborazo ESPOCH, Riobamba 060155, EcuadorFaculty of Informatics and Electronics, Escuela Superior Politécnica de Chimborazo ESPOCH, Riobamba 060155, EcuadorThe purpose of this study lies in developing a comparison of neural network-based models for vehicular congestion prediction, with the aim of improving urban mobility and mitigating the negative effects associated with traffic, such as accidents and congestion. This research focuses on evaluating the effectiveness of different neural network architectures, specifically Transformer and LSTM, in order to achieve accurate and reliable predictions of vehicular congestion. To carry out this research, a rigorous methodology was employed that included a systematic literature review based on the PRISMA methodology, which allowed for the identification and synthesis of the most relevant advances in the field. Likewise, the Design Science Research (DSR) methodology was applied to guide the development and validation of the models, and the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology was used to structure the process, from understanding the problem to implementing the solutions. The dataset used in this study included key variables related to traffic, such as vehicle speed, vehicular flow, and weather conditions. These variables were processed and normalized to train and evaluate various neural network architectures, highlighting LSTM and Transformer networks. The results obtained demonstrated that the LSTM-based model outperformed the Transformer model in the task of congestion prediction. Specifically, the LSTM model achieved an accuracy of 0.9463, with additional metrics such as a loss of 0.21, an accuracy of 0.93, a precision of 0.29, a recall of 0.71, an F1-score of 0.42, an MSE of 0.07, and an RMSE of 0.26. In conclusion, this study demonstrates that the LSTM-based model is highly effective for predicting vehicular congestion, surpassing other architectures such as Transformer. The integration of this model into a simulation environment showed that real-time traffic information can significantly improve urban mobility management. These findings support the utility of neural network architectures in sustainable urban planning and intelligent traffic management, opening new perspectives for future research in this field.https://www.mdpi.com/2079-3197/13/4/92congestion predictionneural networksurban mobilitymachine learningtraffic management |
| spellingShingle | Wilson Chango Pamela Buñay Juan Erazo Pedro Aguilar Jaime Sayago Angel Flores Geovanny Silva Predicting Urban Traffic Congestion with VANET Data Computation congestion prediction neural networks urban mobility machine learning traffic management |
| title | Predicting Urban Traffic Congestion with VANET Data |
| title_full | Predicting Urban Traffic Congestion with VANET Data |
| title_fullStr | Predicting Urban Traffic Congestion with VANET Data |
| title_full_unstemmed | Predicting Urban Traffic Congestion with VANET Data |
| title_short | Predicting Urban Traffic Congestion with VANET Data |
| title_sort | predicting urban traffic congestion with vanet data |
| topic | congestion prediction neural networks urban mobility machine learning traffic management |
| url | https://www.mdpi.com/2079-3197/13/4/92 |
| work_keys_str_mv | AT wilsonchango predictingurbantrafficcongestionwithvanetdata AT pamelabunay predictingurbantrafficcongestionwithvanetdata AT juanerazo predictingurbantrafficcongestionwithvanetdata AT pedroaguilar predictingurbantrafficcongestionwithvanetdata AT jaimesayago predictingurbantrafficcongestionwithvanetdata AT angelflores predictingurbantrafficcongestionwithvanetdata AT geovannysilva predictingurbantrafficcongestionwithvanetdata |