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The rapid urbanization trend in most developing countries including India iscreating a plethora of civic concerns such as loss of green space, degradationof environmental health, clean water availability, air pollution, trafficcongestion leading to delays in vehicular transportation, etc. Transportationand network modeling through transportation indices have been widely used tounderstand transportation problems in the recent past. This necessitatespredicting transportation indices to facilitate sustainable urban planning andtraffic management. Recent advancements in deep learning research, inparticular, Generative Adversarial Networks (GANs), and their modifications inspatial data analysis such as CityGAN, Conditional GAN, and MetroGAN haveenabled urban planners to simulate hyper-realistic urban patterns. Thesesynthetic urban universes mimic global urban patterns and evaluating theirlandscape structures through spatial pattern analysis can aid in comprehendinglandscape dynamics, thereby enhancing sustainable urban planning. This researchaddresses several challenges in predicting the urban transportation index forsmall and medium-sized Indian cities. A hybrid framework based on Kernel RidgeRegression (KRR) and CityGAN is introduced to predict transportation indexusing spatial indicators of human settlement patterns. This paper establishes arelationship between the transportation index and human settlement indicatorsand models it using KRR for the selected 503 Indian cities. The proposed hybridpipeline, we call it RidgeGAN model, can evaluate the sustainability of urbansprawl associated with infrastructure development and transportation systems insprawling cities. Experimental results show that the two-step pipeline approachoutperforms existing benchmarks based on spatial and statistical measures.

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