Traffic lights synchronization for Bus Rapid Transit using a parallel evolutionary algorithm

This article presents a parallel evolutionary algorithm for public transport optimization by synchronizing traffic lights in the context of Bus Rapid Transit systems. The related optimization problem is NP-hard, so exact computational methods are not useful to solve real-world instances. Our research introduces a parallel evolutionary algorithm to efficiently configure and synchronize traffic lights and improve the average speed of buses and other vehicles. The Bus Rapid Transit on Garzón Avenue (Montevideo, Uruguay) is used as a case study. This is an interesting complex urban scenario due to the number of crossings, streets, and traffic lights in the zone. The experimental analysis compares the numerical results computed by the parallel evolutionary algorithm with a scenario that models the current reality. The results show that the proposed evolutionary algorithm achieves better quality of service when compared with the current reality, improving up to 15.3% the average bus speed and 24.8% the average speed of other vehicles. A multiobjective optimization analysis also demonstrates that additional improvements can be achieved by assigning different priorities to buses and other vehicles. In addition, further improvements can be achieved on a modified scenario simply by deleting a few bus stops and changing some traffic lights rules. The benefits of using a parallel solver are also highlighted, as the parallel version is able to accelerate the execution times up to 26.9x when compared with the sequential version

Traffic lights synchronization for Bus Rapid Transit using a parallel evolutionary algorithm

This article presents a parallel evolutionary algorithm for public transport optimization by synchronizing traffic lights in the context of Bus Rapid Transit systems. The related optimization problem is NP-hard, so exact computational methods are not useful to solve real-world instances. Our research introduces a parallel evolutionary algorithm to efficiently configure and synchronize traffic lights and improve the average speed of buses and other vehicles. The Bus Rapid Transit on Garzón Avenue (Montevideo, Uruguay) is used as a case study. This is an interesting complex urban scenario due to the number of crossings, streets, and traffic lights in the zone. The experimental analysis compares the numerical results computed by the parallel evolutionary algorithm with a scenario that models the current reality. The results show that the proposed evolutionary algorithm achieves better quality of service when compared with the current reality, improving up to 15.3% the average bus speed and 24.8% the average speed of other vehicles. A multiobjective optimization analysis also demonstrates that additional improvements can be achieved by assigning different priorities to buses and other vehicles. In addition, further improvements can be achieved on a modified scenario simply by deleting a few bus stops and changing some traffic lights rules. The benefits of using a parallel solver are also highlighted, as the parallel version is able to accelerate the execution times up to 26.9× when compared with the sequential version. Keywords: Bus Rapid Transit, Traffic lights synchronization, Evolutionary algorithm Document type: Articl

Multiobjective electric vehicle charging station locations in a city scale area: Malaga study case.

This article presents a multiobjective variation of the problem of locating electric vehicle charging stations (EVCS) in a city known as the Multiobjective Electric Vehicle Charging Stations Locations (MO-EVCS-L) problem. MO-EVCS-L considers two conflicting objectives: maximizing the quality of service of the charging station network and minimizing the deployment cost when installing different types of charging stations. Two multiobjective metaheuristics are proposed to address MO-EVCS-L: the Non-dominated Sorting Genetic Algorithm, version II (NSGA-II) and the Strength Pareto Evolutionary Algorithm, version 2 (SPEA2). The experimental analysis is performed on a real-world case study defined in Malaga, Spain, and it compares the proposed approaches with a baseline algorithm. Results show that the SPEA2 computes the most competitive solutions, even though both metaheuristics found an accurate set of solutions that provide different trade-offs between the quality of service and the installation costs.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Hybridization of Evolutionary Operators with Elitist Iterated Racing for the Simulation Optimization of Traffic Lights Programs.

In the traffic light scheduling problem, the evaluation of candidate solutions requires the simulation of a process under various (traffic) scenarios. Thus, good solutions should not only achieve good objective function values, but they must be robust (low variance) across all different scenarios. Previous work has shown that combining IRACE with evolutionary operators is effective for this task due to the power of evolutionary operators in numerical optimization. In this article, we further explore the hybridization of evolutionary operators and the elitist iterated racing of IRACE for the simulation–optimization of traffic light programs. We review previous works from the literature to find the evolutionary operators performing the best when facing this problem to propose new hybrid algorithms. We evaluate our approach over a realistic case study derived from the traffic network of Málaga (Spain) with 275 traffic lights that should be scheduled optimally. The experimental analysis reveals that the hybrid algorithm comprising IRACE plus differential evolution offers statistically better results than the other algorithms when the budget of simulations is low. In contrast, IRACE performs better than the hybrids for a high simulations budget, although the optimization time is much longer.This research was partially funded by the University of Malaga, Andaluc ´ ´ıa Tech and the project TAILOR Grant #952215, H2020-ICT-2019-3. C. Cintrano is supported by a FPI grant (BES-2015-074805) from Spanish MINECO. M. Lopez-Ib ´ a´nez is a ˜ “Beatriz Galindo” Senior Distinguished Researcher (BEAGAL 18/00053) funded by the Ministry of Science and Innovation of the Spanish Government. J. Ferrer is supported by a postdoc grant (DOC/00488) funded by the Andalusian Ministry of Economic Transformation, Industry, Knowledge and Universities

Otimização do transporte público visando a integração entre zonas central e sul de Porto Alegre utilizando o software de simulação SUMO

A sustentabilidade da mobilidade urbana é um dos aspectos de maior relevância na luta contra as alterações climáticas e da qualidade de vida em grandes metrópoles. O Brasil é uma economia emergente e possui algumas cidades com grandes populações urbanas. Uma vez que a maioria dessas cidades cresceu de forma descontrolada, o transporte é extremamente complexo e privilegia meios individuais em detrimento dos meios coletivos, o que se opõe aos objetivos do desenvolvimento sustentável. No planejamento do transporte público, a questão da otimização de rotas é muito importante para a sua eficiência e é crucial para a sua atratividade. Neste trabalho, o objetivo é de definir o horário de partida e a quantidade de veículos de uma linha BRT de forma a minimizar as emissões de CO2, respeitando a condição imposta pelo número de passageiros a serem transportados. Para realizar a simulação, foi utilizada a ferramenta SUMO (Simulation of Urban Mobility), integrada com o Java OpenStreetMap Editor para a geração dos mapas da malha rodoviária, a linguagem de programação Python para o desenvolvimento do cenário de otimização e a biblioteca TraCI (Traffic Control Interface) para a integração entre Python e SUMO. A partir de Python implementou-se o algoritmo de otimização Nelder-Mead. A metodologia desenvolvida foi coerente, porém a redução de emissões de CO2 obtida foi de apenas 7%. O que pode significar que esse tipo de abordagem não gera tanto impacto, e que reduções mais significativas seriam obtidas em caso de mudança de tecnologia de motorização dos veículos e/ou na migração de usuários de veículos individuais para o transporte público, com a melhoria da atratividade do modal.The sustainability of urban mobility is one of the most relevant aspects in the fight against climate change and the quality of life in large metropolises. Brazil is an emerging economy and has some cities with large urban populations. Since most of these cities have grown in an uncontrolled way, transportation is extremely complex and privileges individual means of transport to the detriment of collective means, which is opposed to the objectives of sustainable development. In public transport planning, the issue of route optimization is very important for its efficiency and is crucial for its attractiveness. In this work, the goal is to define the departure time and the number of vehicles of a BRT line to minimize CO2 emissions, respecting the condition imposed by the number of passengers to be transported. To perform the simulation, the simulator SUMO (Simulation of Urban Mobility) was used, integrated with the Java OpenStreetMap Editor for the generation of the road network maps, the Python programming language for the development of the optimization scenario and the TraCI (Traffic Control Interface) library for the integration between Python and SUMO. From Python the Nelder-Mead optimization algorithm was implemented. The methodology developed was consistent, but the CO2 emission reduction obtained was only 7%. This may mean that this type of approach does not generate as much impact, and that more significant reductions would be obtained in the case of a change in vehicle motorization technology and/or the migration of users from individual vehicles to public transportation, improving the attractiveness of the modal

Advances on Smart Cities and Smart Buildings

Modern cities are facing the challenge of combining competitiveness at the global city scale and sustainable urban development to become smart cities. A smart city is a high-tech, intensive and advanced city that connects people, information, and city elements using new technologies in order to create a sustainable, greener city; competitive and innovative commerce; and an increased quality of life. This Special Issue collects the recent advancements in smart cities and covers different topics and aspects