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

• URL: http://arxiv.org/abs/2306.05951v1
• Date: Fri, 9 Jun 2023 15:05:40 GMT
• Title: Prediction of Transportation Index for Urban Patterns in Small and Medium-sized Indian Cities using Hybrid RidgeGAN Model
• Authors: Rahisha Thottolil, Uttam Kumar, Tanujit Chakraborty
• Abstract summary: 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. The proposed hybrid pipeline, we call it RidgeGAN model, can evaluate the sustainability of urban sprawl.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Unified Data Management and Comprehensive Performance Evaluation for Urban Spatial-Temporal Prediction [Experiment, Analysis & Benchmark] [78.05103666987655]
  This work addresses challenges in accessing and utilizing diverse urban spatial-temporal datasets. We introduceatomic files, a unified storage format designed for urban spatial-temporal big data, and validate its effectiveness on 40 diverse datasets. We conduct extensive experiments using diverse models and datasets, establishing a performance leaderboard and identifying promising research directions.
  arXiv  Detail & Related papers  (2023-08-24T16:20:00Z)
• A Contextual Master-Slave Framework on Urban Region Graph for Urban Village Detection [68.84486900183853]
  We build an urban region graph (URG) to model the urban area in a hierarchically structured way. Then, we design a novel contextual master-slave framework to effectively detect the urban village from the URG. The proposed framework can learn to balance the generality and specificity for UV detection in an urban area.
  arXiv  Detail & Related papers  (2022-11-26T18:17:39Z)
• MetroGAN: Simulating Urban Morphology with Generative Adversarial Network [10.504296192020497]
  We propose a GAN framework with geographical knowledge, namely Metropolitan GAN (MetroGAN) for urban morphology simulation. Results show that MetroGAN outperforms the state-of-the-art urban simulation methods by over 20% in all metrics.
  arXiv  Detail & Related papers  (2022-07-06T11:02:24Z)
• Predicting Future Occupancy Grids in Dynamic Environment with Spatio-Temporal Learning [63.25627328308978]
  We propose a-temporal prediction network pipeline to generate future occupancy predictions. Compared to current SOTA, our approach predicts occupancy for a longer horizon of 3 seconds. We publicly release our grid occupancy dataset based on nulis to support further research.
  arXiv  Detail & Related papers  (2022-05-06T13:45:32Z)
• Pedestrian Wind Factor Estimation in Complex Urban Environments [0.0]
  Urban planners and policy makers face the challenge of creating livable and enjoyable cities for larger populations in much denser urban conditions. While the urban microclimate holds a key role in defining the quality of urban spaces today and in the future, the integration of wind microclimate assessment in early urban design and planning processes remains a challenge. This work develops a data-driven workflow for real-time pedestrian wind comfort estimation in complex urban environments.
  arXiv  Detail & Related papers  (2021-10-06T01:09:30Z)
• Methodological Foundation of a Numerical Taxonomy of Urban Form [62.997667081978825]
  We present a method for numerical taxonomy of urban form derived from biological systematics. We derive homogeneous urban tissue types and, by determining overall morphological similarity between them, generate a hierarchical classification of urban form. After framing and presenting the method, we test it on two cities - Prague and Amsterdam.
  arXiv  Detail & Related papers  (2021-04-30T12:47:52Z)
• Empowering Urban Governance through Urban Science: Multi-scale Dynamics of Urban Systems Worldwide [0.0]
  The current science of cities can provide a useful foundation for future urban policies. International comparisons of the evolution of cities often produce uncertain results because national territorial frameworks are not always in strict correspondence with the dynamics of urban systems. We propose to provide various compositions of systems of cities to better take into account the dynamic networking of cities that go beyond regional and national territorial boundaries.
  arXiv  Detail & Related papers  (2020-05-20T12:47:40Z)
• Smart Urban Mobility: When Mobility Systems Meet Smart Data [55.456196356335745]
  Cities around the world are expanding dramatically, with urban population growth reaching nearly 2.5 billion people in urban areas and road traffic growth exceeding 1.2 billion cars by 2050. The economic contribution of the transport sector represents 5% of the GDP in Europe and costs an average of US $482.05 billion in the U.S.
  arXiv  Detail & Related papers  (2020-05-09T13:53:01Z)
• City limits in the age of smartphones and urban scaling [0.0]
  Urban planning still lacks appropriate standards to define city boundaries across urban systems. ICT provide the potential to portray more accurate descriptions of the urban systems. We apply computational techniques over a large volume of mobile phone records to define urban boundaries.
  arXiv  Detail & Related papers  (2020-05-06T17:31:21Z)
• Traffic Modelling and Prediction via Symbolic Regression on Road Sensor Data [0.8602553195689513]
  We propose a novel and accurate traffic flow prediction method based on symbolic regression enhanced with a lag operator. Our approach produces robust models suitable for the intricacies of urban roads, much more difficult to predict than highways.
  arXiv  Detail & Related papers  (2020-02-14T16:03:04Z)
• Physical-Virtual Collaboration Modeling for Intra-and Inter-Station Metro Ridership Prediction [116.66657468425645]
  We propose a unified Physical-Virtual Collaboration Graph Network (PVCGN), which can effectively learn the complex ridership patterns from the tailor-designed graphs. Specifically, a physical graph is directly built based on the realistic topology of the studied metro system. A similarity graph and a correlation graph are built with virtual topologies under the guidance of the inter-station passenger flow similarity and correlation.
  arXiv  Detail & Related papers  (2020-01-14T16:47:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.