Evaluation of transport events with the use of Big Data, artificial intelligence and augmented reality techniques

Abstract

The phenomenon of "smart cities" generalizes the use of Information and Communication Technologies. The generation and use of data to manage mobility is a challenge that many cities are betting on and investing in. Through the Internet of all things (IoT) and the use of sensors and mechanisms for capturing information, the number of data analysis tools such as Big Data, Artificial Intelligence (AI), and Augmented Reality (AR) has increased. The tools that are used to interpret events are applied to the analysis of video and photographic images and comprise of a set of programs, mathematical algorithms and protocols. The implementation of procedures could enable the automatic interpretation of image information, from the most basic such as changes in the presence of objects or people, to the identification of complex shapes and relevant event detection. With the constant use of the assisted process learning (Machine Learning), it’s possible to improve event interpretation through the customization of learning protocols. Repetitively trained software can identify relevant events and report changes in critical scenarios that can trigger a series of protocols. The use of artificial intelligence techniques makes it possible to automate monotonous processes and improve transport management. This article analyzes different technologies used to generate transport information and data validation. It is intended to experiment with the use of technologies in the detection of relevant facts, changes of state, and identification of events. It also measures the reliability level when detecting events, and studies the implementation of possible solutions into the transport management system, in order to assist in decision making processes.