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City Planning

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Planning the City Logistics Terminal Location by Applying the Green p-Median Model and Type-2 Neurofuzzy Network.

Computational intelligence and neuroscience
The paper herein presents green p-median problem (GMP) which uses the adaptive type-2 neural network for the processing of environmental and sociological parameters including costs of logistics operators and demonstrates the influence of these parame...

Land surface temperature estimating in urbanized landscapes using artificial neural networks.

Environmental monitoring and assessment
Scenario-based land surface temperature (LST) modeling is a powerful tool for adopting proper urban land use planning policies. In this study, using greater Isfahan as a case study, the artificial neural network (ANN) algorithm was utilized to explor...

Do street-level scene perceptions affect housing prices in Chinese megacities? An analysis using open access datasets and deep learning.

PloS one
Many studies have explored the relationship between housing prices and environmental characteristics using the hedonic price model (HPM). However, few studies have deeply examined the impact of scene perception near residential units on housing price...

Resilience actions to counteract the effects of climate change and health emergencies in cities: the role of artificial neural networks.

Annali dell'Istituto superiore di sanita
Both the World Health Organization (WHO) with its 2015 "Climate and Health Country Profile Project" and the Istituto Superiore di Sanità (ISS) with its 2018 "Health and Climate Change", agree on the emergency generated by the climate change and conce...

Spatiotemporal dynamics of urbanization and cropland in the Nile Delta of Egypt using machine learning and satellite big data: implications for sustainable development.

Environmental monitoring and assessment
The Nile Delta of Egypt is increasingly facing sustainability threats, due to a combination of nature- and human-induced changes in land cover and land use. In this paper, an analysis of big time series data from remotely sensed satellite images and ...

Learning from urban form to predict building heights.

PloS one
Understanding cities as complex systems, sustainable urban planning depends on reliable high-resolution data, for example of the building stock to upscale region-wide retrofit policies. For some cities and regions, these data exist in detailed 3D mod...

Inferring transportation mode from smartphone sensors: Evaluating the potential of Wi-Fi and Bluetooth.

PloS one
Understanding which transportation modes people use is critical for smart cities and planners to better serve their citizens. We show that using information from pervasive Wi-Fi access points and Bluetooth devices can enhance GPS and geographic infor...

Assessing alternative methods for unsupervised segmentation of urban vegetation in very high-resolution multispectral aerial imagery.

PloS one
To analyze types and patterns of greening trends across a city, this study seeks to identify a method of creating very high-resolution urban vegetation maps that scales over space and time. Vegetation poses unique challenges for image segmentation be...

Applying machine learning and geolocation techniques to social media data (Twitter) to develop a resource for urban planning.

PloS one
With all the recent attention focused on big data, it is easy to overlook that basic vital statistics remain difficult to obtain in most of the world. What makes this frustrating is that private companies hold potentially useful data, but it is not a...

Investigating the safety and operational benefits of mixed traffic environments with different automated vehicle market penetration rates in the proximity of a driveway on an urban arterial.

Accident; analysis and prevention
Traffic congestion is monotonically increasing, especially in large cities, due to rapid urbanization. Traffic congestion not only deteriorates traffic operation and degrades traffic safety, but also imposes costs to the road users. The concerns asso...