An examination of the intersection environment associated with perceived crash risk among school-aged children: using street-level imagery and computer vision.

While computer vision techniques and big data of street-level imagery are getting increasing attention, a "black-box" model of deep learning hinders the active application of these techniques to the field of traffic safety research. To address this issue, we presented a semantic scene labeling approach that leverages wide-coverage street-level imagery for the purpose of exploring the association between built environment characteristics and perceived crash risk at 533 intersections. The environmental attributes were measured at eye-level using scene segmentation and object detection algorithms, and they were classified as one of four intersection typologies using the k-means clustering method. Data on perceived crash risk were collected from a questionnaire conducted on 799 children 10 to 12 years old. Our results showed that environmental features derived from deep learning algorithms were significantly associated with perceived crash risk among school-aged children. The results have revealed that some of the intersection characteristics including the proportional area of sky and roadway were significantly associated with the perceived crash risk among school-aged children. In particular, road width had dominant influence on risk perception. The findings While computer vision techniques and big data of street-level imagery are getting increasing attention, a "black-box" model of deep learning hinders the active application of these techniques to the field of traffic safety research. To address this issue, we presented a semantic scene labeling approach that leverages wide-coverage street-level imagery for the purpose of exploring the association between built environment characteristics and perceived crash risk at 533 intersections. The environmental attributes were measured at eye-level using scene segmentation and object detection algorithms, and they were classified as one of four intersection typologies using the k-means clustering method. Data on perceived crash risk were collected from a questionnaire conducted on 799 children 10 to 12 years old. Our results showed that environmental features derived from deep learning algorithms were significantly associated with perceived crash risk among school-aged children. The results have revealed that some of the intersection characteristics including the proportional area of sky and roadway were significantly associated with the perceived crash risk among school-aged children. In particular, road width had dominant influence on risk perception. The findings While computer vision techniques and big data of street-level imagery are getting increasing attention, a "black-box" model of deep learning hinders the active application of these techniques to the field of traffic safety research. To address this issue, we presented a semantic scene labeling approach that leverages wide-coverage street-level imagery for the purpose of exploring the association between built environment characteristics and perceived crash risk at 533 intersections. The environmental attributes were measured at eye-level using scene segmentation and object detection algorithms, and they were classified as one of four intersection typologies using the k-means clustering method. Data on perceived crash risk were collected from a questionnaire conducted on 799 children 10 to 12 years old. Our results showed that environmental features derived from deep learning algorithms were significantly associated with perceived crash risk among school-aged children. The results have revealed that some of the intersection characteristics including the proportional area of sky and roadway were significantly associated with the perceived crash risk among school-aged children. In particular, road width had dominant influence on risk perception. The findings While computer vision techniques and big data of street-level imagery are getting increasing attention, a "black-box" model of deep learning hinders the active application of these techniques to the field of traffic safety research. To address this issue, we presented a semantic scene labeling approach that leverages wide-coverage street-level imagery for the purpose of exploring the association between built environment characteristics and perceived crash risk at 533 intersections. The environmental attributes were measured at eye-level using scene segmentation and object detection algorithms, and they were classified as one of four intersection typologies using the k-means clustering method. Data on perceived crash risk were collected from a questionnaire conducted on 799 children 10 to 12 years old. Our results showed that environmental features derived from deep learning algorithms were significantly associated with perceived crash risk among school-aged children. The results have revealed that some of the intersection characteristics including the proportional area of sky and roadway were significantly associated with the perceived crash risk among school-aged children. In particular, road width had dominant influence on risk perception. The findings provide information useful to providing appropriate and proactive interventions that may reduce the risk of crashes at intersections.