SIALAC Benchmark: On the design of adaptive algorithms for traffic lights problems

International audienceOptimizing traffic lights in road intersections is a mandatory step to achieve sustainable mobility and efficient public transportation in modern cities. Several mono or multi-objective optimization methods exist to find the best traffic signals settings, such as evolutionary algorithms, fuzzy logic algorithms, or even particle swarm optimizations. However, they are generally dedicated to very specific traffic configurations. In this paper, we introduce the SIALAC benchmark bringing together about 24 real-world based study cases, and investigate fitness landscapes structure of these problem instances

The Influence of Jogjakarta Outer Ring Road Development Plan on the National Roads in the Special Region of Yogyakarta

The Influence of Jogjakarta Outer Ring Road Development Plan on the National Roads in the Special Region of Yogyakart

High Performance Computing for City-Scale Modelling and Simulations

The 21st Century is witnessing a rapid rise of urbanization both in the developed and the developing world. Cities increasingly need to be able to do more with less in order to provide for the well-being of their citizens in a sustainable way. The promise of Smart City is an emerging ability to understand, to respond to, and to shape human activity at urban population and geographic scales so that a more agile, adaptive, and sustainable urban environment can be created (see Batty et al., 2012; Caragliu et al., 2011; Chourabi et al., 2012; Su et al., 2011 for early adoption of Smart City). To be effective, this requires the predictive power of data-driven modelling and city-scale computational simulations. Recently city-scale simulations are becoming possible thanks to a surge of development in the high-performance computing (HPC) domain including advanced hardware, computational and algorithmic techniques such as domain decomposition across multi-GPUs and multigrid techniques. Advanced high performance computing systems (a billion billion calculations per second) are now becoming available to performance city-scale simulations with micro-scale models of an individual objective (structure, people, vehicle, etc.) (e.g. Sánchez-Medina et al., 2010; Hori, 2011; Zia et al., 2012; Wijerathne et al., 2013; Pijanowski et al., 2014; Yoshimura et al., 2016; Johansen et al., 2017; Lu and Guan., 2017

The Calibration of Traffic Simulation Models : Report on the assessment of different Goodness of Fit measures and Optimization Algorithms MULTITUDE Project – COST Action TU0903

In the last decades, simulation optimization has received considerable attention from both researchers and practitioners. Simulation optimization is the process of finding the best values of some decision variables for a system whose performance is evaluated using the output of a simulation model. A possible example of simulation optimization is the model calibration. In traffic modelling this topic is particularly relevant since the solutions to the methodological issues arising when setting up a calibration study cannot be posed independently. This calls for methodologies able to check the robustness of a calibration framework as well as further investigations of the issue, in order to identify possible “classes” of problems to be treated in a similar way. Therefore in the present work, first a general method for verifying a traffic micro-simulation calibration procedure (suitable in general for simulation optimization) is described, based on a test with synthetic data. Then it is applied, my means of two different case studies, to draw inferences on the effect that different combinations of parameters to calibrate, optimization algorithm, measures of Goodness of Fit and noise in the data may have on the optimization problem. Results showed the importance of verifying the calibration procedure with synthetic data. In addition they ascertained the need for global optimization solutions, giving new insights into the topic. Research contained within this paper benefited from the participation in EU COST Action TU0903 MULTITUDEJRC.H.8-Sustainability Assessmen

Ant Colony Optimization

Ant Colony Optimization (ACO) is the best example of how studies aimed at understanding and modeling the behavior of ants and other social insects can provide inspiration for the development of computational algorithms for the solution of difficult mathematical problems. Introduced by Marco Dorigo in his PhD thesis (1992) and initially applied to the travelling salesman problem, the ACO field has experienced a tremendous growth, standing today as an important nature-inspired stochastic metaheuristic for hard optimization problems. This book presents state-of-the-art ACO methods and is divided into two parts: (I) Techniques, which includes parallel implementations, and (II) Applications, where recent contributions of ACO to diverse fields, such as traffic congestion and control, structural optimization, manufacturing, and genomics are presented