156 | TRAFFIC MANAGEMENT
Improving traffic manager vision With more vehicles on the roads, managers of road infrastructure need a technological helping hand to ensure that traffic flows as it should and that problems are minimized Words | Roman Guerra Kroenberg and Roberto Ruiz Fernández, Lector Vision, Spain
The global increase of motorized transport, especially private vehicles, is putting extra pressure on the physical transportation infrastructures that accommodate them. The problem is exaggerated by the additional factors of population growth, high density of population and industries in big cities, and the progressive industrialization of emerging countries.
Solutions that meet many needs Road designers and infrastructure managers have to provide solutions to issues that relate to a range of topics. Concerning sustainable mobility, they must tackle the issue of increased pollution and traffic congestion, while for road safety, reducing traffic accidents and numbers of victims affected is important. In terms of the safety of people, they must consider terrorism threats, thefts and vehicles without insurance. All of these issues also have, of course, a financial impact that must be considered. To answer the mobility management challenge, the first step is to understand the problem in depth, as it is a complex issue that requires gleaning all the necessary information about the situation on our roads and in our cities before analyzing and diagnosing the problem. Artificial vision systems applied to ITS allow for the collection of data in real time, for its immediate examination or for later analysis. Two main advantages of these systems are their capacity to detect uncooperative traffic and the possibility of generating graphic evidence of a situation when is needed, for example, as proof of an infringement. Years ago, these systems had limited productivity, but with the generalization of deep learning algorithms, the increase
Intertraffic World | Annual Showcase 2018
of processing units capacity and their application In artificial vision systems (above all as non-linear classifiers) have experienced an extraordinary growth in their capacities and possibilities. The Traffic Eye and Traffic Guard developed by Lector Vision are perfect examples of how artificial vision system capabilities and capacities for ITS data processing has developed. The two products can provide the necessary tools for accurately identifying a vehicle – including its license plate number – and the counting and classifying of categories, tracking of its trajectory, detecting strange driving behavior (vehicles or people circulating on restricted areas or directions), and potentially dangerous situations such as jams, accidents, or objects on the road.
In the future, systems like these could assume many of the tasks previously performed by humans, but with better results than people ever could achieve.
Defining big data The term ‘big data’ is one that is worth mentioning at this point. A simple way to describe it is as a collection of data records, the size, complexity and velocity of which make their collecting, management and analysis very difficult using only traditional analysis techniques. The use of different techniques such as associative learning, regression analysis, genetic algorithms or machine learning have allowed for the processing of information that, before, was impossible to obtain, facilitating the job of ITS designers and managers as a result.
