<b>​A paper "</b><b><i>Probing Urban Congestion Risks Through the Physical Environment: An Explainable Framework Based on Street View Imagery</i></b><b>"</b>

This project includes the code for the paper "Probing Urban Congestion Risks Through the Physical Environment: An Explainable Framework Based on Street View Imagery." It consists of 5 Python projects and 2 additional files:1. <b><i>SRF_embedding.py</i></b> - Constructs a spatial network of features formed through panoramic segmentation and edge expansion of images, utilizing GCN to extract road spatial feature (SRF) of the urban physical environment from street view images.2. <b><i>BEF_embedding.py</i></b> - Employs Grad-CAM to extract landscape features related to traffic tasks from street view images, which are then organized into a graph structure. Likewise, GCN is used to derive built environment feature (BEF) from street view imagery.3. <b><i>CAM_resnet.py</i></b> - An extended ResNet model enhanced with CAM that is capable of extracting hotspot areas in street view images based on the requirements of downstream tasks.4. <b><i>Congestion_risk_task_Concatenate.py</i></b> - Designed for predicting congestion risks by integrating RSF and BEF based on a feature concatenation strategy.5. <b><i>Congestion_risk_task_Fusion.py</i></b> - Also used for predicting congestion risks, this script integrates RSF and BEF based on a feature fusion strategy.6. <b><i>requirements.txt</i></b> - Lists all the necessary environments for the code to function properly.7. <b><i>read.txt</i></b> - Provides an overview of all the files included in this project.