Prediction of Transportation Index for Urban Patterns in Small and Medium-sized Indian Cities using Hybrid RidgeGAN Model

In this study, we propose a hybrid model: RidgeGAN, to predict the transportation index (we refer as "road network density") for urban patterns in small and medium-sized Indian cities. Our study considers publicly available real-world cities database; human settlement datasets namely - World Urban Footprint (WUF,2019) and road Maps-Open Street Maps (OSM-2019) for the selected Indian cities. We establish a relationship between the transportation index and human settlement indicators and model it using Kernel Ridge Regression KRR.

Here, we demonstrate the components of the model used in the hybrid framework: First, we apply CityGAN, an unsupervised learning model to generate small and medium-sized Indian cities using the available urban morphological features.

Landscape structures of real and generated cities are measured in terms of Human Settlement Indices (HSI) using spatial landscape metrics.

We assess the relations between two important features of urban forms (human settlement and transportation system) and build a KRR model to predict the transportation index, namely network density and finally, the proposed hybrid model framework can predict the road network density on a given urban pattern for the urban universes generated in the first step.