Multilayer distributed model predictive control of urban traffic

Abstract

Urban traffic is a relevant topic for the population of cities that suffer from daily traffic jams. People not only spend time on their journeys but also lose energy. With the purpose of improving the traffic flow, it is possible to control the temporization of traffic lights. However, because of the intrinsic characteristics of the system-multivariable, stochastic and dynamic-controlling the traffic of a city through the temporization of its actuators is insufficient to achieve an optimal level. This way, the objective of this work is to elaborate a multilayer distributed model predictive control (ML-DMPC) of the traffic. The proposed model is composed of two layers of control: local and global. Each agent of control is responsible for the local control logic of a set of interdependent semaphores-in other words, semaphores that belong to the same intersection between streets. In order to achieve the local control objective, each control agent is modelled as a neural network and the following components are weighted: the waiting time of the vehicles; the waiting time of the pedestrians; and synchronization of the semaphores. The input variables of the system-flow of vehicles and pedestrians in all directions-are treated by fuzzy logic for the purpose of improving the quality of the information. The principles of consensus between control agents and behavioural coordination are used to conciliate local and global control objectives. In the global control layer, reinforcement learning is applied to improve the modelling process of complex dynamic systems, as the city traffic, as well as to provide adaptability to the model through learning. By means of the proposed model, it is possible to achieve the dynamic control of the traffic lights system of a city.

Sistema de control cooperativo aplicado a una red de tráfico urbano

“…Los algoritmos de consenso han sido ampliamente aplicados a los sistemas cooperativos distribuidos, tales como grupos de vehículos [5,16], enjambres de robots [9], sistemas de control de tráfico [10,18,20], o redes de sensores inalámbricos [8], optimización de tráfico y control en entornos urbanos [4,19], entre otros. En estos casos, el control por consenso permite a cada sistema o agente individual conocer la meta global y, a continuación, en función del estado de los otros componentes del sistema, decidir la mejor acción de control local en la búsqueda de ese objetivo.…”

“…En estos casos, el control por consenso permite a cada sistema o agente individual conocer la meta global y, a continuación, en función del estado de los otros componentes del sistema, decidir la mejor acción de control local en la búsqueda de ese objetivo. El problema de optimización del tráfico en entornos urbanos, basada en información de la contaminación, se puede abordar estableciendo un consenso entre los diferentes agentes de control involucrados [4]. En un problema similar (gestión y control del tráfico urbano), Castro et al propuso un modelo distribuido de múltiples capas para el control predictivo del tráfico urbano.…”

Consensus-Based Cooperative Control Based on Pollution Sensing and Traffic Information for Urban Traffic Networks

“…The problem of traffic optimization in urban environments based on city-pollution information can be addressed by a consensus-based approach between different control agents [ 18 ]. Focused on a similar problem (urban-traffic management and control), Castro et al proposed a multilayer distributed model for predictive control of urban traffic.…”

Consensus-Based Cooperative Control Approach Applied to Urban Traffic Networks

“…Consensus algorithms have been widely applied to distributed cooperative systems such as swarm robots [3], urban traffic control systems [4] and wireless sensor networks, among others. Furthermore, the problem of traffic optimization in urban environments based on city pollution information can be engineered as a consensus between control agents [5].…”