The purpose of this research is to investigate the relationships between the movement time, travel speed and occupant density during trial evacuations, particularly for theatre-type rooms. The study mainly focuses on crowd movement behaviour within a restricted space and covers aspects of human behaviour and issues needed to be considered in terms of the characteristics of lecture theatres. A set of experiments were carried out in three building blocks at the University of Canterbury in order to obtain the actual data for analysis. The number of students evacuating from each exit and the evacuation time were recorded, and their movement behaviour was monitored by video camera. Based on the experimental data, a numerical analysis was undertaken to formulate an equation for the prediction of evacuation time applying to lecture theatres. The developed equation was compared with other available relationships from the literature. An evacuation model under development, named EvacuatioNZ, was applied to simulate the experiments and the results were compared with the experimental data. The comparison showed that the developed equation showed a better performance in predicting evacuation time of lecture theatres than other available methods however, had some limitations. The EvauctioNZ model was able to be improved by using an alternative geometry input but was still not as accurate as the developed method. A recommended modification of the model was presented for improvement.

Friction Stir Welding (FSW) was used to perform a Design of Experiment (DOE) to determine the welding parameters effects on yielding consistent mechanical properties across the length of the weld. The travel speed was varied across set forge force and RPM conditions, to find a dataset that will yield consistent mechanical properties independent of the travel speed. Six different welds were completed on two different aluminum panels, the advancing side being Aluminum alloy 2195-T8 at a thickness of .350”, with the retreating side being Aluminum alloy 2219-T851 with a gauge thickness of .360”. A Left-hand Right-hand self-reacting pin tool was used for each weld. The mechanical properties of interest are the Ultimate Tensile Strength, Yield Strength, Elasticity and Hardness. The strengths were evaluated by tensile testing, with the Elasticity being measure post break. Specimens were then polished where macrograph and micrograph analysis was completed. Micro-hardness testing was then completed on the weld nuggets.

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / The Highway Capacity Manual (HCM-2010) indicates in Volume III (Chapter 17) how the characteristics of the environment can represent obstacles that influence the determination of the average pedestrian space. However, the HCM does not specifically analyse in detail how these obstacles affect specifically the path, speed and average walking time of pedestrian. This article performs a comparative analysis between two microsimulations models where it is evidence how by not considering the characteristics of the fixed and mobile environment, the transhipment time and the speed of people are modified. As a case study, the obstacles present in the environment during the transhipment carried out by users in an integrated multimodal transport system in the city of Lima are identified. The comparative analysis between both cases shows the influence of the characteristics of the environment of the transhipment and variation in the average speed of the pedestrians. The proposed study methodology is calibrated and validated by microsimulations in Vissim Software. The comparative analysis reflects an increase in the transhipment time of 19.4% and a decrease in the average speed of the pedestrians by 14.8 %, reflecting in the microsimulation model values near to the real behaviour of pedestrians.

The previous research available to predict travel speed is wide and has been extensively studied. What currently is missing from the previous work is to estimate the travel speed when different non-recurrent events occur, such as car accidents and road maintenance work. This research implements a machine learning model to predict the average speed on a road segment with and without road accidents. The model would assist in (1) planning the most efficient route which could reduce CO2 emissions and travel time (2) the drivers in traffic could get an estimate of when the traffic will open up again (3) the authorities could take safety measures if drivers are expected to be stuck for too long. In our work, we conducted a review to determine some of the optimal machine learning models to predict on time series data. What we found by comparing GRU (Gated Recurrent Unit) and LSTM (Long Short Term Memory) on travel speed data over a road in Sweden provided by the Swedish Transport Administration, is that there is no major difference in performance between the LSTM and GRU algorithms to predict the average travel speed. We also study the impact of using weather, date and accident related parameters on the model’s predictions. What we found is that we obtained much better results when including the weather data. Furthermore, the inclusion of road events vaguely hints that it could improve performance, but can not be verified due to the low number of road accidents in our dataset.