Optimising Real-World Traffic Cycle Programs by Using Evolutionary Computation

Abstract

Traffic congestion, and the consequent loss of time, money, quality of life, and higher pollution, is currently one of the most important problems in cities, and several approaches have been proposed to reduce it. In this paper, we propose a novel formulation of the traffic light scheduling problem in order to alleviate it. This novel formulation of the problem allows more realistic scenarios to be modeled, and as a result, it becomes much harder to solve in comparison to previous formulations. The proposal of more advanced and efficient techniques than those applied in past research is thus required. We propose the application of diversity-based multi-objective optimizers, which have shown to provide promising results when addressing single-objective problems. The wide experimental evaluation performed over a set of real-world instances demonstrates the good performance of our proposed diversity-based multi-objective method to tackle traffic at a large scale, especially in comparison to the best-performing single-objective optimizer previously proposed in the literature. Consequently, in this paper, we provide new state-of-the-art algorithmic schemes to address the traffic light scheduling problem that can deal with a whole city, instead of just a few streets and junctions, with a higher level of detail than the one found in present studies due to our micro-analysis of streets.INDEX TERMS Traffic light scheduling problem, traffic management, diversity preservation, real-world application.

Intelligent IoT systems for traffic management: A practical application

“…Within the first group, it stands out the work carried out by Sánchez et al [24], where GA is used to encode a fuzzy logic controller in the chromosomes and find the optimal parameters according to the number of cars waiting. Also, similar works on optimising traffic light timings using GA are presented in [21], [25]. An approach linking GAs with device communication (D2D) can be found in [26].…”

Contribución al aumento de la capacidad cognitiva de los sistemas inteligentes de transporte mediante inteligencia artificial

“…After applying the optimization process, the obtained control policy allowed controlling vehicles in simple scenarios. Similar works can be found in [339], [341]. Also, in [342] we can find an approach that unifies the communication between track and GA devices.…”

“…In the scientific literature, we can find numerous works addressing intelligent traffic control at regulated intersections using artificial intelligence or relying on AI to optimize an advanced control policy. The most important of these are fuzzy logic [324]- [326], reservation and market-based system [327]- [329], neural networks [330]- [332], reinforcement learning [72], [333]- [336] and swarm intelligence and evolutionary computation [337]- [339] that try to solve the traffic management problem by proposing new approaches in traffic light control.…”

Real Time Optimisation of Traffic Signals to Prioritise Public Transport

“…Researchers mainly distinguish between macroscopic and microscopic models. Macroscopic simulators are more abstract, as not individual traffic, but rather traffic flows are simulated [24]. A macroscopic simulator aims to answer questions about the general traffic flow, rather than providing information about individual vehicle movements.…”