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a2026-06-25
en All wildboar-vehicle collisions respond to the same variables? Looking for patterns using self-organizing maps. -
Modificado el valor del campo
modified
del recurso Acceso al recurso a2026-06-25
(anteriormente2026-06-23
) en All wildboar-vehicle collisions respond to the same variables? Looking for patterns using self-organizing maps.
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| 80 | "notes": "Selecting the most efficient mitigation measures to reduce | 94 | "notes": "Selecting the most efficient mitigation measures to reduce | ||
| 81 | animal-vehicle collisions is difficult without knowledge of the | 95 | animal-vehicle collisions is difficult without knowledge of the | ||
| 82 | circumstances on each stretch of road. The identification of patterns, | 96 | circumstances on each stretch of road. The identification of patterns, | ||
| 83 | based on the variables that explain the spatial distribution of | 97 | based on the variables that explain the spatial distribution of | ||
| 84 | road-kills would be useful to improve decision-making. We used data | 98 | road-kills would be useful to improve decision-making. We used data | ||
| 85 | mining techniques to identify patterns within a dataset of wild | 99 | mining techniques to identify patterns within a dataset of wild | ||
| 86 | boar-vehicle collisions in Castilla y Le\u00f3n, Spain. Collisions | 100 | boar-vehicle collisions in Castilla y Le\u00f3n, Spain. Collisions | ||
| 87 | were grouped using a Kohonen\u00b4s selforganizing map which is a | 101 | were grouped using a Kohonen\u00b4s selforganizing map which is a | ||
| 88 | neural network of competitive learning. A 4x4 matrix was derived to | 102 | neural network of competitive learning. A 4x4 matrix was derived to | ||
| 89 | obtain 16 groups of collisions with similar properties in relation to | 103 | obtain 16 groups of collisions with similar properties in relation to | ||
| 90 | the traffic, the road and the surrounding environment. This number | 104 | the traffic, the road and the surrounding environment. This number | ||
| 91 | could vary to improve the adjustment between patterns and mitigation | 105 | could vary to improve the adjustment between patterns and mitigation | ||
| 92 | measures. We used logistic regressions to model each pattern. Knowing | 106 | measures. We used logistic regressions to model each pattern. Knowing | ||
| 93 | what the more important variables in each collision are, we could | 107 | what the more important variables in each collision are, we could | ||
| 94 | choose the best type of mitigation measures in accordance with the | 108 | choose the best type of mitigation measures in accordance with the | ||
| 95 | road segment properties. Expensive wildlife passes and fences should | 109 | road segment properties. Expensive wildlife passes and fences should | ||
| 96 | be restricted to segments of highways with high traffic volumes and | 110 | be restricted to segments of highways with high traffic volumes and | ||
| 97 | hotspots, where the probability of successful crossing is very low. | 111 | hotspots, where the probability of successful crossing is very low. | ||
| 98 | For medium and low volumes of traffic the best solutions vary in | 112 | For medium and low volumes of traffic the best solutions vary in | ||
| 99 | relation to the characteristics of the surrounding landscape.", | 113 | relation to the characteristics of the surrounding landscape.", | ||
| 100 | "notes_translated": { | 114 | "notes_translated": { | ||
| 101 | "en": "Selecting the most efficient mitigation measures to reduce | 115 | "en": "Selecting the most efficient mitigation measures to reduce | ||
| 102 | animal-vehicle collisions is difficult without knowledge of the | 116 | animal-vehicle collisions is difficult without knowledge of the | ||
| 103 | circumstances on each stretch of road. The identification of patterns, | 117 | circumstances on each stretch of road. The identification of patterns, | ||
| 104 | based on the variables that explain the spatial distribution of | 118 | based on the variables that explain the spatial distribution of | ||
| 105 | road-kills would be useful to improve decision-making. We used data | 119 | road-kills would be useful to improve decision-making. We used data | ||
| 106 | mining techniques to identify patterns within a dataset of wild | 120 | mining techniques to identify patterns within a dataset of wild | ||
| 107 | boar-vehicle collisions in Castilla y Le\u00f3n, Spain. Collisions | 121 | boar-vehicle collisions in Castilla y Le\u00f3n, Spain. Collisions | ||
| 108 | were grouped using a Kohonen\u00b4s selforganizing map which is a | 122 | were grouped using a Kohonen\u00b4s selforganizing map which is a | ||
| 109 | neural network of competitive learning. A 4x4 matrix was derived to | 123 | neural network of competitive learning. A 4x4 matrix was derived to | ||
| 110 | obtain 16 groups of collisions with similar properties in relation to | 124 | obtain 16 groups of collisions with similar properties in relation to | ||
| 111 | the traffic, the road and the surrounding environment. This number | 125 | the traffic, the road and the surrounding environment. This number | ||
| 112 | could vary to improve the adjustment between patterns and mitigation | 126 | could vary to improve the adjustment between patterns and mitigation | ||
| 113 | measures. We used logistic regressions to model each pattern. Knowing | 127 | measures. We used logistic regressions to model each pattern. Knowing | ||
| 114 | what the more important variables in each collision are, we could | 128 | what the more important variables in each collision are, we could | ||
| 115 | choose the best type of mitigation measures in accordance with the | 129 | choose the best type of mitigation measures in accordance with the | ||
| 116 | road segment properties. Expensive wildlife passes and fences should | 130 | road segment properties. Expensive wildlife passes and fences should | ||
| 117 | be restricted to segments of highways with high traffic volumes and | 131 | be restricted to segments of highways with high traffic volumes and | ||
| 118 | hotspots, where the probability of successful crossing is very low. | 132 | hotspots, where the probability of successful crossing is very low. | ||
| 119 | For medium and low volumes of traffic the best solutions vary in | 133 | For medium and low volumes of traffic the best solutions vary in | ||
| 120 | relation to the characteristics of the surrounding landscape.", | 134 | relation to the characteristics of the surrounding landscape.", | ||
| 121 | "es": "Selecting the most efficient mitigation measures to reduce | 135 | "es": "Selecting the most efficient mitigation measures to reduce | ||
| 122 | animal-vehicle collisions is difficult without knowledge of the | 136 | animal-vehicle collisions is difficult without knowledge of the | ||
| 123 | circumstances on each stretch of road. The identification of patterns, | 137 | circumstances on each stretch of road. The identification of patterns, | ||
| 124 | based on the variables that explain the spatial distribution of | 138 | based on the variables that explain the spatial distribution of | ||
| 125 | road-kills would be useful to improve decision-making. We used data | 139 | road-kills would be useful to improve decision-making. We used data | ||
| 126 | mining techniques to identify patterns within a dataset of wild | 140 | mining techniques to identify patterns within a dataset of wild | ||
| 127 | boar-vehicle collisions in Castilla y Le\u00f3n, Spain. Collisions | 141 | boar-vehicle collisions in Castilla y Le\u00f3n, Spain. Collisions | ||
| 128 | were grouped using a Kohonen\u00b4s selforganizing map which is a | 142 | were grouped using a Kohonen\u00b4s selforganizing map which is a | ||
| 129 | neural network of competitive learning. A 4x4 matrix was derived to | 143 | neural network of competitive learning. A 4x4 matrix was derived to | ||
| 130 | obtain 16 groups of collisions with similar properties in relation to | 144 | obtain 16 groups of collisions with similar properties in relation to | ||
| 131 | the traffic, the road and the surrounding environment. This number | 145 | the traffic, the road and the surrounding environment. This number | ||
| 132 | could vary to improve the adjustment between patterns and mitigation | 146 | could vary to improve the adjustment between patterns and mitigation | ||
| 133 | measures. We used logistic regressions to model each pattern. Knowing | 147 | measures. We used logistic regressions to model each pattern. Knowing | ||
| 134 | what the more important variables in each collision are, we could | 148 | what the more important variables in each collision are, we could | ||
| 135 | choose the best type of mitigation measures in accordance with the | 149 | choose the best type of mitigation measures in accordance with the | ||
| 136 | road segment properties. Expensive wildlife passes and fences should | 150 | road segment properties. Expensive wildlife passes and fences should | ||
| 137 | be restricted to segments of highways with high traffic volumes and | 151 | be restricted to segments of highways with high traffic volumes and | ||
| 138 | hotspots, where the probability of successful crossing is very low. | 152 | hotspots, where the probability of successful crossing is very low. | ||
| 139 | For medium and low volumes of traffic the best solutions vary in | 153 | For medium and low volumes of traffic the best solutions vary in | ||
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