BACKGROUND The effects of daily abuse and hardship on the streets lead to poor mental health in children living on the streets, resulting in them choosing ineffective and self-destructive coping strategies that impact their physical health and overall sense of wellbeing. The facilitation of the mental health of children living on the streets who are subjected to daily threats to their survival ...

Deep learning has become very popular as a method to predict short-term traffic volumes on road networks, especially highway networks, based on real-time observation. Various studies have confirmed that the performance of deep learning in predicting traffic volumes is better than that of previous machine learning models and statistical models. Although it is natural to consider that the traffic...

We describe the algorithms we have developed to automatically generate street networks and building plots in the automatic procedural creation of a realistic city. Our system first sites the important regions of a city, such as the commercial center, historical center, and residential areas. Then, a network of freeway and main roads are built. Finally, a network of streets are built throughout ...

Centrality has revealed crucial for understanding the structural properties of complex relational networks. Centrality is also relevant for various spatial factors affecting human life and behaviors in cities. Here, we present a comprehensive study of centrality distributions over geographic networks of urban streets. Five different measures of centrality, namely degree, closeness, betweenness,...

A novel method for extracting linear streets from a street network is proposed where a linear street is defined as a sequence of connected street segments having a shape similar to a straight line segment. Specifically a given street network is modeled as a Conditional Random Field (CRF) where the task of extracting linear streets corresponds to performing learning and inference with respect to...

We propose a novel approach to selection of important streets from a network, based on the technique of a self-organizing map (SOM), an artificial neural network algorithm for data clustering and visualization. Using the SOM training process, the approach derives a set of neurons by considering multiple attributes including topological, geometric and semantic properties of streets. The set of n...

Abstract Disinformation continues to raise concerns due its increasing threat society. Nevertheless, a of disinformation-based attack on critical infrastructure is often overlooked. Here, we consider urban traffic networks and focus fake information that manipulates drivers’ decisions create congestion at city scale. Specifically, two complementary scenarios, one where drivers are persuaded mov...