Smart Mobility provides clean, safe and efficient mobility; with a wide range of transport modes such as bicycles, buses, light rail, subways, trams, taxis, autonomous vehicles, with most options for movement within the ecosystem of smart mobility. A Smart Mobility solution consists of smart parking lots in buildings that contain control and management systems monitored remotely to obtain available spaces. This document briefly details the concept and a comparison of results of Smart Mobility solutions for smart parking.

In this work, a bibliographic review on neurocomputing and the role it plays in pattern recognition has been carried out, the algorithms proposed by different authors have been studied and a report has been made of the most relevant works that apply the technique of pattern recognition. This study has been carried out due to the importance that the use of neurocomputing currently has for information processing in some areas such as sensor processing, data analysis and analysis of control aspects and in general where there is no algorithm that provides a solution. The methodology used allowed identifying, evaluating and analyzing various related studies to later apply a systematic categorization model and obtain the characteristics with their respective descriptions. In this way many algorithms that seek to solve the pattern recognition problem are based in computing models that imitate the way the human brain work focused on high-level cognitive functions such as neural networks characterized by their ability to generalize the information that implies learning processes or architectures under deep learning, however the trend that advances significantly involves the extraction of characteristics in the recognition of emotions.