Seguridad espacial de los peatones en las zonas escolares de Riad: un enfoque basado en datos
Evaluación de la seguridad espacial de los peatones en las zonas escolares de Riad mediante regresión lineal múltiple y aprendizaje automático: un enfoque basado en datos
DOI:
https://doi.org/10.62161/sauc.v11.5977Palabras clave:
Zona escolar, Seguridad vial, Peatones, Espacial, Regresión, Aprendizaje automático, Inteligencia ArtificialResumen
Este estudio analiza de forma exhaustiva la densidad de atropellos de peatones (el número de atropellos por kilómetro cuadrado de superficie del distrito) en las zonas escolares de Riad, empleando técnicas avanzadas de inteligencia artificial (IA), como la regresión lineal múltiple (MLR) y el aprendizaje automático (ML), con el fin de mejorar la eficiencia urbana, la calidad de vida y la resiliencia. Se recopilaron datos de 884 zonas escolares distribuidas por Riad, que abarcaban diversos contextos infraestructurales, socioeconómicos y demográficos. El modelo MLR optimizado identificó predictores significativos, como la longitud de las carreteras del distrito, la gravedad media de los accidentes (EPDO), la renta media, la densidad de población y la disponibilidad de paradas de transporte público, que en conjunto explicaban aproximadamente el 65 % de la variabilidad de la densidad de accidentes. El modelo ML Bosque aleatorio mejoró aún más la precisión predictiva (R² ≈ 0,88), revelando interacciones complejas y no lineales entre variables clave, como el volumen de tráfico, los límites de velocidad, el número de carriles, la disponibilidad de pasos de peatones y la población estudiantil.
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