Detecting heavy trucks from mobile phone trajectories using image-based behavioral representations and deep learning models
Abstract This paper proposes an innovative methodology for detecting heavy trucks utilizing mobile phone data, addressing significant limitations inherent in traditional tracking methods, often characterized by high costs, intrusiveness, and incomplete data capture. By employing Call Detail Records...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-06711-5 |
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| author | Franco Basso Félix des Rotours Tomás Maldonado Raúl Pezoa Mauricio Varas |
| author_facet | Franco Basso Félix des Rotours Tomás Maldonado Raúl Pezoa Mauricio Varas |
| author_sort | Franco Basso |
| collection | DOAJ |
| description | Abstract This paper proposes an innovative methodology for detecting heavy trucks utilizing mobile phone data, addressing significant limitations inherent in traditional tracking methods, often characterized by high costs, intrusiveness, and incomplete data capture. By employing Call Detail Records (CDR) and introducing an image-inspired architecture, the study uses Convolutional Neural Networks (CNN) to model the microscopic behavioral patterns of mobile devices. Our numerical results show that our proposed approach outperforms more classical machine learning methods that rely only on aggregated features. This novel approach offers a scalable and cost-effective alternative to conventional methods, representing a pioneering application of image-based analytical techniques to mobile phone data within freight transport research. This work provides a robust tool for analyzing freight transport patterns, thereby supporting the development of strategies to mitigate the negative externalities of freight transportation while preserving its economic benefits. |
| format | Article |
| id | doaj-art-1e6dabe2708d40a2879970e1fd0dcf0b |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-1e6dabe2708d40a2879970e1fd0dcf0b2025-08-20T03:45:24ZengNature PortfolioScientific Reports2045-23222025-07-0115111410.1038/s41598-025-06711-5Detecting heavy trucks from mobile phone trajectories using image-based behavioral representations and deep learning modelsFranco Basso0Félix des Rotours1Tomás Maldonado2Raúl Pezoa3Mauricio Varas4School of Industrial Engineering, Pontificia Universidad Católica de ValparaísoEcole PolytechniqueDepartment of Industrial Engineering, Universidad Diego PortalesDepartment of Industrial Engineering, Universidad Diego PortalesCentro de Investigación en Sustentabilidad y Gestión Estratégica de Recursos, Facultad de Ingeniería, Universidad del DesarrolloAbstract This paper proposes an innovative methodology for detecting heavy trucks utilizing mobile phone data, addressing significant limitations inherent in traditional tracking methods, often characterized by high costs, intrusiveness, and incomplete data capture. By employing Call Detail Records (CDR) and introducing an image-inspired architecture, the study uses Convolutional Neural Networks (CNN) to model the microscopic behavioral patterns of mobile devices. Our numerical results show that our proposed approach outperforms more classical machine learning methods that rely only on aggregated features. This novel approach offers a scalable and cost-effective alternative to conventional methods, representing a pioneering application of image-based analytical techniques to mobile phone data within freight transport research. This work provides a robust tool for analyzing freight transport patterns, thereby supporting the development of strategies to mitigate the negative externalities of freight transportation while preserving its economic benefits.https://doi.org/10.1038/s41598-025-06711-5Deep learningLogisticsCall detail records |
| spellingShingle | Franco Basso Félix des Rotours Tomás Maldonado Raúl Pezoa Mauricio Varas Detecting heavy trucks from mobile phone trajectories using image-based behavioral representations and deep learning models Scientific Reports Deep learning Logistics Call detail records |
| title | Detecting heavy trucks from mobile phone trajectories using image-based behavioral representations and deep learning models |
| title_full | Detecting heavy trucks from mobile phone trajectories using image-based behavioral representations and deep learning models |
| title_fullStr | Detecting heavy trucks from mobile phone trajectories using image-based behavioral representations and deep learning models |
| title_full_unstemmed | Detecting heavy trucks from mobile phone trajectories using image-based behavioral representations and deep learning models |
| title_short | Detecting heavy trucks from mobile phone trajectories using image-based behavioral representations and deep learning models |
| title_sort | detecting heavy trucks from mobile phone trajectories using image based behavioral representations and deep learning models |
| topic | Deep learning Logistics Call detail records |
| url | https://doi.org/10.1038/s41598-025-06711-5 |
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