Synthetic DAS signal Generation from PTV VISSIM transport models. [10.5281/zenodo.15144354]
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| Format: | Recurso digital |
| Langue: | anglais |
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2025
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| _version_ | 1866902255301558272 |
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| author | Robles-Urquijo, Ignacio |
| author_facet | Robles-Urquijo, Ignacio |
| contents | <p>-CODE: Python notebooks and script tools developed to generate Synthetic DAS signals from PTV VISSIM traffic simulations<br> Synthetic_DAS_VISSIM_functions.py : python methods/functions created for the methodology<br> EXAMPLE_case_introduction_v5.ipynb : Case introduction of a simple road in Navarra (selected due to the access to real measurements)<br> Traffic_VISSIM_to_Synthetic_DAS_Generation_v18.ipynb : Generation example of the Synthetic DAS signals from PTV VISSIM traffic simulations<br> Unet-DAS-EB_Training_v24.ipynb : Machine Learning construction and training of the East Bound (EB) model.<br> Unet-DAS-EB_Validation_Ploting_v25.ipynb : Validation EB model carried with Synthetic DAS signals not used during training.<br> Unet-DAS-WB_Training_v26.ipynb : Machine Learning construction and training of the West Bound (WB) model.<br> Unet-DAS-WB_Validation_Ploting_v27.ipynb : Validation of the WB model carried with Synthetic DAS signals not used during training.<br> Unet-DAS-MID_Training_v28.ipynb : Machine Learning construction and training of the Middle of the pavement cable layout (MID) model.<br> Unet-DAS-MID_Validation_Ploting_v29.ipynb : Validation of the MID model carried with Synthetic DAS signals not used during training.<br> Unet-DAS-MID_TESTING_v30.ipynb : Testing of all three models (EB, WB, MID) with Real DAS signals.<br> SUPPLEMENTAL_Flamant-Boussinesq.ipynb : Analsysis and comments of the Flamant-Boussinesq approximation.</p> <p><br>-DATA<br> -NA_DAS_REAL: Data from real DAS measurements and scaler features<br> -NA_DAS_SYNTHETIC:<br> -NA-ALL: Synthetic DAS signals (with L & L/2) from PTV VISSIM models [DOI: 10.5281/zenodo.15132983]<br> -NA-WB: the same as NA-ALL, but with traffic only going West Bound direction. [DOI: 10.5281/zenodo.15126927]<br> -NA_DAS_SYNTHETIC: NA-EB: the same as NA-ALL, but with traffic only going East Bound direction. [DOI: 10.5281/zenodo.15132046]<br> -NA_DAS_SYNTHETIC: NA-MID: the same as NA-ALL, with fiber optic cable on the middle. [DOI: 10.5281/zenodo.15132498]<br> -NA_TRAFFIC: Traffic data from PTV VISSIM simulations, including the links, vehicle model parameters, Fiber Optic Cable Layout coordinates (FOptic_xxx.csv)<br> -NA-ALL: Output results from PTV VISSIM simulations for all models (xxx.fzp files)<br> -NA-EB: Output results from PTV VISSIM simulations for East Bound models (xxx.fzp files)<br> -NA-WB: Output results from PTV VISSIM simulations for West Bound models (xxx.fzp files)</p> <p><br>-FIGURES: Figures generated from the scripts and/or used in the paper.</p> <p><br>-MODELS<br> -NA-TRAFFIC: PTV VISSIM traffic models for the NA-150 road section.<br> -NA-UNET: Machine Learning weights and traininig validation evidence files.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15144354 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Synthetic DAS signal Generation from PTV VISSIM transport models. [10.5281/zenodo.15144354] Robles-Urquijo, Ignacio Fiber Optic Technology Traffic monitoring Traffic infrastructure Road traffic Transport layer Computer Simulation Data processing <p>-CODE: Python notebooks and script tools developed to generate Synthetic DAS signals from PTV VISSIM traffic simulations<br> Synthetic_DAS_VISSIM_functions.py : python methods/functions created for the methodology<br> EXAMPLE_case_introduction_v5.ipynb : Case introduction of a simple road in Navarra (selected due to the access to real measurements)<br> Traffic_VISSIM_to_Synthetic_DAS_Generation_v18.ipynb : Generation example of the Synthetic DAS signals from PTV VISSIM traffic simulations<br> Unet-DAS-EB_Training_v24.ipynb : Machine Learning construction and training of the East Bound (EB) model.<br> Unet-DAS-EB_Validation_Ploting_v25.ipynb : Validation EB model carried with Synthetic DAS signals not used during training.<br> Unet-DAS-WB_Training_v26.ipynb : Machine Learning construction and training of the West Bound (WB) model.<br> Unet-DAS-WB_Validation_Ploting_v27.ipynb : Validation of the WB model carried with Synthetic DAS signals not used during training.<br> Unet-DAS-MID_Training_v28.ipynb : Machine Learning construction and training of the Middle of the pavement cable layout (MID) model.<br> Unet-DAS-MID_Validation_Ploting_v29.ipynb : Validation of the MID model carried with Synthetic DAS signals not used during training.<br> Unet-DAS-MID_TESTING_v30.ipynb : Testing of all three models (EB, WB, MID) with Real DAS signals.<br> SUPPLEMENTAL_Flamant-Boussinesq.ipynb : Analsysis and comments of the Flamant-Boussinesq approximation.</p> <p><br>-DATA<br> -NA_DAS_REAL: Data from real DAS measurements and scaler features<br> -NA_DAS_SYNTHETIC:<br> -NA-ALL: Synthetic DAS signals (with L & L/2) from PTV VISSIM models [DOI: 10.5281/zenodo.15132983]<br> -NA-WB: the same as NA-ALL, but with traffic only going West Bound direction. [DOI: 10.5281/zenodo.15126927]<br> -NA_DAS_SYNTHETIC: NA-EB: the same as NA-ALL, but with traffic only going East Bound direction. [DOI: 10.5281/zenodo.15132046]<br> -NA_DAS_SYNTHETIC: NA-MID: the same as NA-ALL, with fiber optic cable on the middle. [DOI: 10.5281/zenodo.15132498]<br> -NA_TRAFFIC: Traffic data from PTV VISSIM simulations, including the links, vehicle model parameters, Fiber Optic Cable Layout coordinates (FOptic_xxx.csv)<br> -NA-ALL: Output results from PTV VISSIM simulations for all models (xxx.fzp files)<br> -NA-EB: Output results from PTV VISSIM simulations for East Bound models (xxx.fzp files)<br> -NA-WB: Output results from PTV VISSIM simulations for West Bound models (xxx.fzp files)</p> <p><br>-FIGURES: Figures generated from the scripts and/or used in the paper.</p> <p><br>-MODELS<br> -NA-TRAFFIC: PTV VISSIM traffic models for the NA-150 road section.<br> -NA-UNET: Machine Learning weights and traininig validation evidence files.</p> |
| title | Synthetic DAS signal Generation from PTV VISSIM transport models. [10.5281/zenodo.15144354] |
| topic | Fiber Optic Technology Traffic monitoring Traffic infrastructure Road traffic Transport layer Computer Simulation Data processing |
| url | https://doi.org/10.5281/zenodo.15144354 |