Volume 2 Issue 4
Authors: Stephen Swift; Fadratul Hafinaz Hassan; Allan Tucker
Abstract: A spatial layout design must consider not only ease of movement, but also safety in a panic situation, such as an emergency evacuation in a theatre, stadium or hospital. Using pedestrian simulation statistics, the activity of crowds can be used to study the consequences of different spatial layouts. A cellular automaton has been proposed to model pedestrian simulation so that pedestrian flows can be explored at a microscopic level. We examined four-way pedestrian flow statistics generated from feasible seating layout solutions using heuristic search techniques (hill climbing, simulated annealing and genetic algorithm style operators). The technique has shown promising results in identifying useful characteristics of spatial layout and sources of the pedestrian clogging phenomenon.
Keywords: Cellular Automata; Simulated Annealing; Hill Climbing; Genetic Algorithm; Pedestrian Simulation Statistics; Spatial Layout
Authors: Jun Tian; Xiaodong Wang; Daxin Zhu
Abstract: In some researches, time is a target variable. Factors that may influence the occurring time of an outcome need to be analysed. The effect of a factor on an outcome is often modified by another factor because there is an interaction between them. The analysis of the interaction between the factors is very important for us to better understand the mechanism of the effect that factors exert on an outcome. This paper proposes the method to evaluate interactions of the factors and their 95% confidence intervals in survival analysis. These factors are influencing the survival time of patients with cancer, and their interactions are successfully analysed by the method.
Keywords: Interaction; Survival Time; Confidence Intervals; Survival Analysis
Authors: Baker Kh. Abdalhaq; Maher I. Abu Baker
Abstract: Simulation today is one of the most used tools in science and engineering. Traffic engineering is no exception. Simulators to be usable passes through processes of verification, validation and calibration. All simulators are based on assumptions and parameters that need to be calibrated so as to be practical in real world applications. Some parameters change from site to site. Therefore, the calibration process is often needed. Calibration can be seen as an optimization process that seeks to minimize the difference between observed and simulated measures. The question of which optimization technique suits more for this particular problem remains open. In this paper the convergence velocity of main heuristic optimization techniques, namely Genetic Algorithm (GA), Tabu Search (TS), Particle Swarm Optimization (PS) and Simultaneous Perturbation for Stochastic Approximation algorithm (SPSA) were used to calibrate a traffic simulation model called SUMO. The results of the calibration of the mentioned optimization techniques were compared. Classical optimization techniques, namely Neldear-Mead and COBYLA were used as a baseline comparison. Each technique has its own parameters that affect convergence velocity. Therefore, optimization techniques themselves need to be calibrated. However, TS and PS are not widely used to calibrate traffic simulators. They perform well in this particular problem. PS is highly parallel compared to the TS and SPSA. The paper shows that classical optimization techniques are not suitable for this particular problem, PS and TS appear to be better than GA and SPSA. PS seems to be a promising optimization technique.
Keywords: Traffic Simulation Calibration; Genetic Algorithm; Participle Swarm Optimization; Tabu Search