Mental representations of spatial knowledge are organized hierarchically. Among people familiar with an urban environment, common spatial knowledge from these spatial mental representations enables successful communication of place and route descriptions, consisting of hierarchically-ordered references to prominent spatial features, such as streets. The more prominent a street is, the more like...

1-      introduction the increasing growth of urbanization in recent decade and occurring most of the economic and social activities of human being in urban environments cause that the city is considered as a place a citizen spends much time and it is one of the effective and important places in which the majority of memories, experiences, emotions are formed. thus, cities play important role i...

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 ...

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...

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...

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...

Streets are large, diverse, and used for several (and possibly conflicting) transport modalities as well as social and cultural activities. Proper planning is essential and requires data. Manually fabricating data that represent streets (street reconstruction) is error-prone and time consuming. Automatising street reconstruction is a challenge because of the diversity, size, and scale of the de...