Prediction of transportation index for urban patterns in small and medium-sized Indian cities using hybrid RidgeGAN model

被引:1
作者
Thottolil, Rahisha [1 ]
Kumar, Uttam [1 ]
Chakraborty, Tanujit [2 ,3 ,4 ]
机构
[1] Int Inst Informat Technol, Ctr Data Sci, Spatial Comp Lab, Bangalore 560100, India
[2] Sorbonne Univ Abu Dhabi, Dept Sci & Engn, Abu Dhabi, U Arab Emirates
[3] Woxsen Univ, Sch Business, Hyderabad, Telangana, India
[4] Sorbonne Univ, Sorbonne Ctr Artificial Intelligence, Paris, France
关键词
LANDSCAPE; REGRESSION; GROWTH;
D O I
10.1038/s41598-023-49343-3
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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, scarcity of clean water, rise in air pollution, and exacerbated traffic congestion resulting in significant delays in vehicular transportation. To address the intricate nature of transportation issues, many researchers and planners have analyzed the complexities of urban and regional road systems using transportation models by employing transportation indices such as road length, network density, accessibility, and connectivity metrics. This study addresses the complexities of predicting road network density for small and medium-sized Indian cities that come under the Integrated Development of Small and Medium Towns (IDSMT) project at a national level. A hybrid framework based on Kernel Ridge Regression (KRR) and the CityGAN model is introduced to predict network density using spatial indicators of human settlements. The major goal of this study is to generate hyper-realistic urban patterns of small and medium-sized Indian cities using an unsupervised CityGAN model and to study the causal relationship between human settlement indices (HSIs) and transportation index (network density) using supervised KRR for the real cities. The synthetic urban universes mimic Indian urban patterns and evaluating their landscape structures through the settlement indices can aid in comprehending urban landscape, thereby enhancing sustainable urban planning. We analyzed 503 real cities to find the actual relationship between the urban settlements and their road density. The nonlinear KRR model may help urban planners in deriving the network density for GAN-generated futuristic urban patterns through the settlement indicators. The proposed hybrid process, termed as RidgeGAN model, can gauge the sustainability of urban sprawl tied to infrastructure and transportation systems in sprawling cities. Analysis results clearly demonstrate the utility of RidgeGAN in predicting network density for different kinds of human settlements, particularly for small and medium Indian cities. By predicting future urban patterns, this study can help in the creation of more livable and sustainable areas, particularly by improving transportation infrastructure in developing cities.
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页数:18
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