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.

翻译：快速城市化趋势在包括印度在内的大多数发展中国家引发了诸多市政问题，例如绿地减少、环境健康恶化、清洁水资源短缺、空气污染、交通拥堵导致车辆运输延误等。近年来，通过交通指数进行交通与网络建模已被广泛用于理解交通问题。因此，预测交通指数对于促进可持续城市规划和交通管理至关重要。深度学习研究的最新进展，特别是生成对抗网络（GANs）及其在空间数据分析中的变体（如CityGAN、条件GAN和MetroGAN），使城市规划者能够模拟超逼真的城市格局。这些合成城市宇宙模仿全球城市格局，通过空间格局分析评估其景观结构有助于理解景观动态，从而增强可持续城市规划。本研究解决了预测中小型印度城市交通指数的若干挑战。提出了一种基于核岭回归（KRR）和CityGAN的混合框架，利用人类居住格局的空间指标预测交通指数。本文建立了交通指数与人类居住指标之间的关系，并针对选定的503个印度城市使用KRR进行建模。我们称所提出的混合流水线为RidgeGAN模型，该模型能够评估与基础设施发展和交通系统相关的城市扩张可持续性。实验结果表明，该两阶段流水线方法在空间和统计指标上均优于现有基准方法。