## 3 Data and Methodology
### Explanatory Variables
#### 3.2.3 Streetscape Feature Variables  
Street-level variables are derived from semantic segmentation of street-view images to capture features such as roads, buildings, and greenery. These features effectively reflect street design. Semantic segmentation partitions an image into non-overlapping meaningful subregions where pixels within the same region share certain similarities. This study employs the DeepLabV3+ neural network model [93], an improved form of a deep convolutional neural network (DCNN). With its encoder-decoder structure, DeepLabV3+ gradually captures semantic information and high-level features. In the encoder, the model integrates a large number of dilated convolutions to expand the receptive field without losing critical information, while the decoder fuses both low-level and high-level features to improve boundary accuracy [94; 95].  
We adopt ResNet-101 as the backbone network for DeepLabV3+ and train the model on the public Cityscapes dataset, which is a large-scale open dataset for urban spatial scene understanding. Model parameters are updated through backpropagation in each iteration, defined as processing one batch of data. After each epoch, model performance is evaluated on the validation set. If the evaluation metric improves, the current model parameters are saved to replace the previously stored version. In our experiment, the learning rate is set to 0.1, the maximum number of epochs is 30,000, and the batch size is 8. Under the optimal configuration, DeepLabV3+ achieves strong segmentation performance on the validation set, with an overall accuracy of 95.96%, mean accuracy of 84.73%, and mean intersection over union (MIoU) of 77.36%. Once trained, the model segments each street-view image into 19 semantic categories, including road, sidewalk, building, wall, fence, pole, traffic light, traffic sign, vegetation, terrain, sky, person, rider, car, truck, bus, train, motorcycle,and bicycle. The model training and testing are conducted on a server equipped with one NVIDIA GeForce RTX 4090 GPU (NVIDIA Corporation, Santa Clara, CA, USA) and anIntel Core i9-13900K CPU (Intel Corporation, Santa Clara, CA, USA).  
The application of DeepLabV3+ in this context assumes that the target street-view images are semantically and visually similar to the urban street scenes in the Cityscapes dataset used for training. It also presumes that objects in street scenes exhibit spatial coherence and that the image quality provides sufficient visual cues for accurate pixelwise classification. To simulate how residents perceive the urban streetscape, street-view images were acquired via the Baidu Street View API. In each grid cell, four sampling points were placed at 125-meter intervals and shifted to the nearest roads, ensuring the images captured relevant street sections. Moreover, because a single image cannot provide a complete panoramic view of each sampling point, images were taken from four angles (0\({}^{\circ}\), 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\)), each with a 90\({}^{\circ}\) field of view. Due to data gaps in certain angles, a total of 5915 images were finally obtained. These images were segmented using the trained DeepLabV3+ model, generating 19 streetscape variables. This study focuses on six categories that are most closely related to ride-hailing demand: buildings, greenery, sky, roads, traffic control facilities (traffic lights and traffic signs), and sidewalks. The average proportion of each category within each grid cell is calculated to represent how street-level environmental features may influence travel demand.