Ana Carolina Olivera

Optimal Cycle Program of Traffic Lights With Particle Swarm Optimization

Optimal staging of traffic lights, and in particular optimal light cycle programs, is a crucial task in present day cities with potential benefits in terms of energy consumption, traffic flow management, pedestrian safety, and environmental issues. Nevertheless, very few publications in the current literature tackle this problem by means of automatic intelligent systems, and, when they do, they focus on limited areas with elementary traffic light schedules. In this paper, we propose an optimization approach in which a particle swarm optimizer (PSO) is able to find successful traffic light cycle programs. The solutions obtained are simulated with simulator of urban mobility, a well-known microscopic traffic simulator. For this study, we have tested two large and heterogeneous metropolitan areas with hundreds of traffic lights located in the cities of Bahía Blanca in Argentina (American style) and Málaga in Spain (European style). Our algorithm is shown to obtain efficient traffic light cycle programs for both kinds of cities. In comparison with expertly predefined cycle programs (close to real ones), our PSO achieved quantitative improvements for the two main objectives: 1) the number of vehicles that reach their destination and 2) the overall journey time.

A memetic algorithm based on a NSGAII scheme for the flexible job-shop scheduling problem

The Flexible Job-Shop Scheduling Problem is concerned with the determination of a sequence of jobs, consisting of many operations, on different machines, satisfying several parallel goals. We introduce a Memetic Algorithm, based on the NSGAII (Non-Dominated Sorting Genetic Algorithm II) acting on two chromosomes, to solve this problem. The algorithm adds, to the genetic stage, a local search procedure (Simulated Annealing). We have assessed its efficiency by running the algorithm on multiple objective instances of the problem. We draw statistics from those runs, which indicate that this Memetic Algorithm yields good and low-cost solutions.

Reducing vehicle emissions and fuel consumption in the city by using particle swarm optimization

Nowadays, the increasing levels of polluting emissions and fuel consumption of the road traffic in modern cities directly affect air quality, the city economy, and especially the health of citizens. Therefore, improving the efficiency of the traffic flow is a mandatory task in order to mitigate such critical problems. In this article, a Swarm Intelligence approach is proposed for the optimal scheduling of traffic lights timing programs in metropolitan areas. By doing so, the traffic flow of vehicles can be improved with the final goal global target of reducing their fuel consumption and gas emissions (CO and NOx). In this work we optimize the traffic lights timing programs and analyze their effect in pollution by following the standard HBEFA as the traffic emission model. Specifically, we focus on two large and heterogeneous urban scenarios located in the cities of Malaga and Seville (in Spain). When compared to the traffic lights timing programs designed by experts close to real ones, the proposed strategy obtains significant reductions in terms of the emission rates (23.3 % CO and 29.3 % NOx) and the total fuel consumption.

Enhancing the urban road traffic with Swarm Intelligence: A case study of Córdoba city downtown

In current modern cities, the increasing number of traffic lights that control the vehicular traffic flow requires a highly complex scheduling. Thousands of red lights, that have to be optimally programmed, are nowadays operating in congested urban areas. Therefore, automatic intelligent systems are indispensable tools for optimally tackling this task. In this work, we propose a Swarm Intelligence approach that, coupled with the SUMO traffic simulator, is able to find successful cycle programs of traffic lights for large urban areas. In concrete, we have focused on a metropolitan area of the city downtown of Córdoba (in Spain). The experiments and comparisons with other techniques reveal that our proposed approach obtains significant profits in terms of traffic flow and global trip time.

Bus Network Optimization Through Time-Dependent Hybrid Algorithm

This paper focuses on a new hybrid technique that combines a genetic algorithm with simulation to solve the bus-network scheduling problem (BNSP). The BNSP has several factors that complicate both the problem formulation and the selection of efficient algorithms for its resolution. This problem is challenging because not only the BNSP is NP-complete, but also the existing methods fail to contemplate environment dependent dynamic variables. The hybrid algorithm proposed in this article comprises two stages: a modified GRASP (greedy randomized adaptive search procedures) as an initialization method, and the genetic algorithm with simulation to find the values of the environment- dependent dynamic variables. The final goal consisted in designing a meta-heuristic technique that yields an adequate scheduling to solve this general problem. The BNSP, chosen as case study, satisfies both the demand and the offer of transport. The method was applied to a solution of experimental examples with good results.

An Improved Hybrid Algorithm for Stochastic Bus-Network Design

The purpose of this work is to present the elastic hybrid algorithm, a method that deals in a realistic manner with the bus-network design problem. The novel technique integrates a Floyd–Warshall initialization method, a multiobjective evolutionary algorithm based on the strength Pareto evolutionary algorithm 2, and a simulation procedure. The Floyd–Warshall procedure initializes the distances and routes between each pair of bus stops. The evolutionary stage obtains several quasi-optimal bus networks, with the help of a simulation procedure that calculates the values of the environmentally dependent dynamic variables associated with the user. The method was successfully tested with a real-life case study, and its relevance was assessed after it was compared with other authors’ works. As a conclusion subsequent to several experimental stages, it can be confirmed that the elastic hybrid algorithm achieves highly competitive results compared to those from the literature, while obtaining solutions that exhibit a strong similarity to various real features of the problem under study.

Evolutionary Techniques in Multi-Objective Optimization Problems in Non-Standardized Production Processes

To schedule production in a Job-Shop environment means to allocate adequately the available resources. It requires to rely on efficient optimization procedures. In fact, the JobShop Scheduling Problem (JSSP) is a NP-Hard problem (Ullman, 1975), so ad-hoc algorithms have to be applied to its solution (Frutos et al., 2010). This is similar to other combinatorial programming problems (Olivera et al., 2006), (Cortes et al., 2004). Most instances of the Job-Shop Scheduling Problem involve the simultaneous optimization of two usually conflicting goals. This one, like most multi-objective problems, tends to have many solutions. The Pareto frontier reached by an optimization procedure has to contain a uniformly distributed number of solutions close to the ones in the true Pareto frontier. This feature facilitates the task of the expert who interprets the solutions (Kacem et al., 2002). In this paper we present a Genetic Algorithm linked to a Simulated Annealing procedure able to schedule the production in a Job-Shop manufacturing system (Cortes et al., 2004), (Tsai & Lin, 2003), (Wu et al., 2004), (Chao-Hsien & Han-Chiang, 2009).