  The rapid urbanization trend in most developing countries including India is
creating a plethora of civic concerns such as loss of green space, degradation
of environmental health, clean water availability, air pollution, traffic
congestion leading to delays in vehicular transportation, etc. Transportation
and network modeling through transportation indices have been widely used to
understand transportation problems in the recent past. This necessitates
predicting transportation indices to facilitate sustainable urban planning and
traffic management. Recent advancements in deep learning research, in
particular, Generative Adversarial Networks (GANs), and their modifications in
spatial data analysis such as CityGAN, Conditional GAN, and MetroGAN have
enabled urban planners to simulate hyper-realistic urban patterns. These
synthetic urban universes mimic global urban patterns and evaluating their
landscape structures through spatial pattern analysis can aid in comprehending
landscape dynamics, thereby enhancing sustainable urban planning. This research
addresses several challenges in predicting the urban transportation index for
small and medium-sized Indian cities. A hybrid framework based on Kernel Ridge
Regression (KRR) and CityGAN is introduced to predict transportation index
using spatial indicators of human settlement patterns. This paper establishes a
relationship between the transportation index and human settlement indicators
and models it using KRR for the selected 503 Indian cities. The proposed hybrid
pipeline, we call it RidgeGAN model, can evaluate the sustainability of urban
sprawl associated with infrastructure development and transportation systems in
sprawling cities. Experimental results show that the two-step pipeline approach
outperforms existing benchmarks based on spatial and statistical measures.
