Automatic identification of bottlenecks for ambulance passage on urban streets: A deep learning-based approach

Urban streets exhibit a diverse range of characteristics, with some presenting significant challenges to ambulance passage, directly impacting the safety of residents. Thus, ensuring unimpeded passage for ambulances on streets is a key focus of urban renewal and street governance initiatives. However, the identification of bottlenecks for emergency vehicle passage on urban streets currently relies on labor-intensive and inefficient on-site manual audits. This study proposes a deep learning-based approach to achieve automatic identification of ambulance passage on urban streets. The Vision Transformer network is utilized to construct the classification model of Impassable Narrow Roads, Passable Narrow Roads, and Wide Roads based on street view images. To train and test the constructed models, a specialized dataset is established, consisting of street view images labeled by experienced ambulance drivers. Comparative experiments are conducted to confirm the optimal structure of the model and the necessity of semantic segmentation preprocessing for street view images. To confirm the superiority of the proposed approach, four commonly used deep learning methods, MobileNet, ShuffleNet, SuperViT and DualViT serve as the baseline tests. Experimental results reveal that the model with four-head and one sequential encoder achieves the highest evaluation accuracy at 75.65% among the proposed models on the original dataset, significantly outperforming benchmark models. Meanwhile, the segmentation of street view images improves accuracy to 77.42%, but it reduces computational efficiency from 0.01 to 3 seconds per image. Finally, the optimal model is applied to the area within the Second Ring Road of Beijing as an example to discuss how the deep learning-based approach proposed in this paper supports urban planning practice and emergency medical response. The proposed approach facilitates the rapid and large-scale identification of bottlenecks in urban streets for ambulances with very limited costs, making a significant contribution to the accurate identification of key areas for urban renewal and street governance efforts. The proposed method can further assist emergency vehicle dispatchers and drivers in identifying accessible routes with greater precision during operations, thereby enabling more timely transportation of patients to medical facilities.