Understanding the interplay between traffic optimization parameters and their alignment with sensors, control algorithms, and Sustainable Development Goals (SDGs) is essential for improving urban traffic management. The appropriate selection of parameters in urban traffic management is crucial to optimize vehicular flow and meet the Sustainable Development Goals (SDGs). To find out which parameters are most commonly used and appropriate, a comprehensive study was conducted, the results of which are presented in this article. This study uses a three-phase approach: qualitative exploration, systematic literature review, and multiple-dimensional analysis. This study’s contributions include a practical five-level framework for traffic optimization addressing congestion problems, the identification of 19 commonly used traffic control parameters, the analysis of their implementations in recent intelligent traffic control systems, and a proposal of trends to orient these parameters towards efficiency and compliance with the SDGs. The results lay the groundwork for creating new parameters or modifying existing parameters so that the parameters are oriented not only towards efficiency in control algorithms or user experience but also towards meeting the SDGs.

Detecting and characterising vehicles is one of the purposes of embedded systems used in intelligent environments. An analysis of a vehicle’s characteristics can reveal inappropriate or dangerous behaviour. This detection makes it possible to sanction or notify emergency services to take early and practical actions. Vehicle detection and characterisation systems employ complex sensors such as video cameras, especially in urban environments. These sensors provide high precision and performance, although the price and computational requirements are proportional to their accuracy. These sensors offer high accuracy, but the price and computational requirements are directly proportional to their performance. This article introduces a system based on modular devices that is economical and has a low computational cost. These devices use ultrasonic sensors to detect the speed and length of vehicles. The measurement accuracy is improved through the collaboration of the device modules. The experiments were performed using multiple modules oriented to different angles. This module is coupled with another specifically designed to detect distance using previous modules’ speed and length data. The collaboration between different modules reduces the speed relative error ranges from 1 to 5%, depending on the angle configuration used in the modules.

In the current context of the Internet of Things, embedded devices can have some intelligence and distribute both data and processed information. This article presents the paradigm shift from a hierarchical pyramid to an inverted pyramid that is the basis for edge, fog, and cloud-based architectures. To support the new paradigm, the article presents a distributed modular architecture. The devices are made up of essential elements, called control nodes, which can communicate to enhance their functionality without sending raw data to the cloud. To validate the architecture, identical control nodes equipped with a distance sensor have been implemented. Each module can read the distance to each vehicle and process these data to provide the vehicle’s speed and length. In addition, the article describes how connecting two or more CNs, forming an intelligent device, can increase the accuracy of the parameters measured. Results show that it is possible to reduce the processing load up to 22% in the case of sharing processed information instead of raw data. In addition, when the control nodes collaborate at the edge level, the relative error obtained when measuring the speed and length of a vehicle is reduced by one percentage point.

La gestión de la movilidad de personas y vehículos es un aspecto de continuo estudio debido a la relevancia que tiene en la contribución a la polución. El control de los semáforos determina las colas que en los cruces se pueden formar. Habitualmente este control no está adaptado al tráfico existente en un momento concreto, dado que la adaptación implica conocer los peatones y vehículos que se encuentran circulando en cada momento. Para resolver este problema, en el artículo se propone el uso de unos dispositivos inteligentes modulares que permiten detectar los vehículos y cambiar los tiempos de acceso al cruce dependiendo de las circunstancias. Para validar el sistema se ha realizado una simulación generando cargas en MatLab y simulando el control con Simulink. Se ha simulado un ciclo de semáforo con tiempos fijos y se ha comparado con ciclos de tiempos variables en función de la carga de peatones y de vehículos. En el artículo se proponen los indicadores Op y Sat como método de medición de la optimización del algoritmo de control sobre el estado del cruce. Por medio de dichos indicadores se ha comprobado que en el mejor de los casos es posible optimizar en un 50 % el tiempo de espera de forma casi independiente de la carga de tráfico.