{"help": "https://pro.iepnb.gob.es/catalogo/api/3/action/help_show?name=package_show", "success": true, "result": {"access_rights": "http://inspire.ec.europa.eu/metadata-codelist/LimitationsOnPublicAccess/noLimitations", "alternate_identifier": "ISBN: 978-91-637-7327-3", "author": "Colino-Rabanal, V.J.", "author_name": "Colino-Rabanal, V.J.", "classification_variables": {"es": ""}, "conforms_to": ["https://www.boe.es/eli/es/res/2013/02/19/(4)"], "contact_email": "organismo@example.org", "contact_name": "Organismo publicador del Cat\u00e1logo", "contact_role": "http://inspire.ec.europa.eu/metadata-codelist/ResponsiblePartyRole/pointOfContact", "contact_uri": "http://datos.gob.es/recurso/sector-publico/org/Organismo/EA0000000", "contact_url": "https://organismo.example.org/", "created": "2025-05-23", "creator_user_id": "d24a314a-79ce-48c0-abd9-54cb42706da4", "dataset_scope": "non_spatial_dataset", "dcat_type": "http://purl.org/dc/dcmitype/Text", "encoding": "UTF-8", "featured": false, "graphic_overview": "https://i1.rgstatic.net/publication/270957822_Identifying_Roadless_Areas_in_Europe/links/54df95710cf29666378b912d/largepreview.png", "hvd": "non_hvd", "id": "c2e5ffcf-4005-5839-af34-864fd1dd77d4", "identifier": "c2e5ffcf-4005-5839-af34-864fd1dd77d4", "inspire_id": "", "isopen": true, "language": "http://publications.europa.eu/resource/authority/language/SPA", "license_id": "cc-by", "license_title": "Creative Commons Attribution", "license_url": "http://www.opendefinition.org/licenses/cc-by", "lineage_process_steps": [], "lineage_source": ["2014 IENE International Conference. Programme and abstracts", "pag. 42"], "maintainer": "", "maintainer_name": "", "metadata_created": "2026-06-23T14:49:56.328992", "metadata_modified": "2026-06-25T12:29:43.487603", "metadata_profile": ["https://www.w3.org/TR/vocab-dcat-3/"], "miteco_data_population": {"es": ""}, "miteco_data_territory": {"es": ""}, "miteco_dataset_type": "http://publications.europa.eu/resource/authority/dataset-type/STATISTICAL", "miteco_geo_level": "1", "modified": "2026-06-25", "name": "c2e5ffcf-4005-5839-af34-864fd1dd77d4", "notes": "A wide range of mitigation measures have been proposed and tested to reduce the number of animal-vehicle collision (AVC). These mitigation measures work at different spatial scales, from regional planning to transversal structures and other mechanisms that work at road segment scale. They have different objectives, aimed to modify driver\u00b4s behavior, modify animal behavior, or both. Moreover, they also show different costs and effectiveness. The mitigation measures focused on the driver as the main responsible for avoiding AVC are commonly installed throughout the world. For example, traffic warning signs are the most widely applied measure. The warning expects to get driver\u00b4s attention and to manage a reduction of the vehicle speed, considering that speed is one of the most remarkable explanatory variables involved in AVC occurrence and in the seriousness of them. Nevertheless, road signs have shown limited long-term effectiveness because, after a certain period of time, the driver\u00b4s response to the warning decreases. This fact can be explained partly because of the absence of temporal correspondence between the warning and the real risk. Effectiveness may increase focusing the warning only in those moments and locations of high risk of AVC. Thus, one of the main characteristics that we can exploit to deal with the AVC problematic is that they are not randomly distributed either the space or time. There are important temporal changes across the seasons, within the day or within other facts as moon phases. Moreover, AVC commonly concentrate in certain landscapes and habitats. Hence, it is possible to model the spatio-temporal probability of AVC occurrence with a road network. We have obtained the model for AVC in Castile and Leon region (NW Spain). The resultant spatio-temporal model can be implemented in an app for smartphones or incorporated easily to a GPS vehicle navigation system, coupling it with the positioning system, the calendar and the clock of the device. The result is an application that shows at real-time the probability of AVC considering the vehicle positioning. Al alarm alerts the driver only when the risk of AVC is high, this is, when the driver passes through a road segment with high probability of AVC occurrence just in a period of time (month, hour, moon phase, etc.) given to AVC.", "notes_translated": {"en": "A wide range of mitigation measures have been proposed and tested to reduce the number of animal-vehicle collision (AVC). These mitigation measures work at different spatial scales, from regional planning to transversal structures and other mechanisms that work at road segment scale. They have different objectives, aimed to modify driver\u00b4s behavior, modify animal behavior, or both. Moreover, they also show different costs and effectiveness. The mitigation measures focused on the driver as the main responsible for avoiding AVC are commonly installed throughout the world. For example, traffic warning signs are the most widely applied measure. The warning expects to get driver\u00b4s attention and to manage a reduction of the vehicle speed, considering that speed is one of the most remarkable explanatory variables involved in AVC occurrence and in the seriousness of them. Nevertheless, road signs have shown limited long-term effectiveness because, after a certain period of time, the driver\u00b4s response to the warning decreases. This fact can be explained partly because of the absence of temporal correspondence between the warning and the real risk. Effectiveness may increase focusing the warning only in those moments and locations of high risk of AVC. Thus, one of the main characteristics that we can exploit to deal with the AVC problematic is that they are not randomly distributed either the space or time. There are important temporal changes across the seasons, within the day or within other facts as moon phases. Moreover, AVC commonly concentrate in certain landscapes and habitats. Hence, it is possible to model the spatio-temporal probability of AVC occurrence with a road network. We have obtained the model for AVC in Castile and Leon region (NW Spain). The resultant spatio-temporal model can be implemented in an app for  smartphones or incorporated easily to a GPS vehicle navigation system, coupling it with the positioning system, the calendar and the clock of the device. The result is an application that shows at real-time the probability of AVC considering the vehicle positioning. Al alarm alerts the driver only when the risk of AVC is high, this is, when the driver passes through a road segment with high probability of AVC occurrence just in a period of time (month, hour, moon phase, etc.) given to AVC.", "es": "A wide range of mitigation measures have been proposed and tested to reduce the number of animal-vehicle collision (AVC). These mitigation measures work at different spatial scales, from regional planning to transversal structures and other mechanisms that work at road segment scale. They have different objectives, aimed to modify driver\u00b4s behavior, modify animal behavior, or both. Moreover, they also show different costs and effectiveness. The mitigation measures focused on the driver as the main responsible for avoiding AVC are commonly installed throughout the world. For example, traffic warning signs are the most widely applied measure. The warning expects to get driver\u00b4s attention and to manage a reduction of the vehicle speed, considering that speed is one of the most remarkable explanatory variables involved in AVC occurrence and in the seriousness of them. Nevertheless, road signs have shown limited long-term effectiveness because, after a certain period of time, the driver\u00b4s response to the warning decreases. This fact can be explained partly because of the absence of temporal correspondence between the warning and the real risk. Effectiveness may increase focusing the warning only in those moments and locations of high risk of AVC. Thus, one of the main characteristics that we can exploit to deal with the AVC problematic is that they are not randomly distributed either the space or time. There are important temporal changes across the seasons, within the day or within other facts as moon phases. Moreover, AVC commonly concentrate in certain landscapes and habitats. Hence, it is possible to model the spatio-temporal probability of AVC occurrence with a road network. We have obtained the model for AVC in Castile and Leon region (NW Spain). The resultant spatio-temporal model can be implemented in an app for smartphones or incorporated easily to a GPS vehicle navigation system, coupling it with the positioning system, the calendar and the clock of the device. The result is an application that shows at real-time the probability of AVC considering the vehicle positioning. Al alarm alerts the driver only when the risk of AVC is high, this is, when the driver passes through a road segment with high probability of AVC occurrence just in a period of time (month, hour, moon phase, etc.) given to AVC."}, "num_resources": 1, "num_tags": 2, "organization": {"id": "bca483f7-26e2-4ed8-99e8-65f5b3c7a26e", "name": "iepnb", "title": "", "type": "organization", "description": "", "image_url": "", "created": "2026-06-23T14:36:08.942021", "is_organization": true, "approval_status": "approved", "state": "active"}, "owner_org": "bca483f7-26e2-4ed8-99e8-65f5b3c7a26e", "private": false, "provenance": {"en": "", "es": ""}, "publisher_email": "buzon-bdatos@miteco.es", "publisher_name": "\u00c1rea de Banco de Datos de la Naturaleza. Direcci\u00f3n General Biodiversidad, Bosques y Desertificaci\u00f3n. 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