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This thesis introduces, implements and evaluates an innovative concept for assessing driving behavior in public transportation through Mobile Crowd Sensing (MCS), under the field of Advanced Public Transportation System (APTS) - a sub-group of Intelligent Transportation Systems (ITS). Aggressive driving behavior is known to be a cause of avoidable accidents and to increase fuel consumption. In public transportations, it is also a case for costumers’ dissatisfaction. Monitoring the quality of driving behavior is a key element to overcome this issue and to improve road safety and customer satisfaction. In this research project, a software application (app) for mobile devices was developed as an experimental tool / proof-of-concept, to monitor aggressive driving behavior in bus drivers, collecting data coming from mobile device’s accelerometer and passengers’ qualitative evaluation. The experimental procedure took place in public transportation in Macau (bus only) and consisted of data collection of drivers’ aggressive driving behavior using the developed application. The analysis of collected data suggests that MCS is a viable way to assess drivers’ behavior in public transportation, thus contributing to the improvement of the service and increase of road safety. Although the methodology has been tailor-made for Macau public transportation, it is believed that the same concept can be applied to other cities, leading them towards the goal of becoming smarter cities. Keywords: driving behavior; mobile crowd sensing; crowdsourcing; smart city; advanced public transportation system; intelligent transportation system; road safety; mobile device accelerometer
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A study of Portuguese cultural vestiges assimilated into the local cultures of the island of Flores, in Eastern Indonesia, and maintained today. The vestiges include, principally, street theatre (the Bobo tradition) as well as ritual traditions inherited through the Confraternities established by Dominican missionaries in the 16th century.
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The knowledge of spatial distribution of grasshoppers can be very relevant for agricultural planning purposes. On the other hand, the comparison of spatial interpolators for efficiency and reliability reasons is also a key factor to understand interpolation maps outcomes (versus reality). At last, but not least, the use of open Web geographical tools to disseminate true spatial inferential methods to address spatial issues is still quite limited (if none) in high schools and universities, particularly in Geography subjects. If the latter can be addressed with myGeoffice©, the first issue will use the Utah, USA, dataset (58 samples) to layout the spatial distribution of grasshoppers and understand the counties that are more pro to this kind of agriculture infestation. Inverse Distance Weighted (IDW), Moving Average (MA), Multi-quadratic, Inverse Multi-quadratic and Nearest Neighbor (NN) will produce interpolated surfaces of grasshopper’s properties. Efficiency of spatial interpolators was assessed in this writing based on the prediction error’s statistics derived from the difference between the estimation and the real samples on a cross-validation procedure. Remarkably, results show that NN was the most accurate one when compared with the remaining deterministic approaches at sample’s locations.
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