Airport Security: Passenger's Perception and Design Issues

The main goal of this thesis is to report patterns of perceived safety in the context of airport infrastructure, taking the airport of Bologna as reference. Many personal and environmental attributes are investigated to paint the profile of the sensitive passenger and to understand why precise factors of the transit environment are so impactful on the individual. The main analyses are based on a 2014-2015 passengers’ survey, involving almost six thousand of incoming and outgoing passengers. Other reports are used to implement and support the resource. The analysis is carried out by using a combination of Chi-square tests and binary logistic regressions. Findings shows that passengers result to be particularly affected by the perception of airport’s environment (e.g., state and maintenance of facilities, clarity and efficacy of information system, functionality of elevators and escalators), but also by the way how the passenger reaches the airport and the quality of security checks. In relation to such results, several suggestions are provided for the improvement of passenger satisfaction with safety. The attention is then focused on security checkpoints and related operations, described on a theoretical and technical ground. We present an example of how to realize a proper model of the security checks area of Bologna’s airport, with the aim to assess present performances of the system and consequences of potential variations. After a brief introduction to Arena, a widespread simulation software, the existing model is described, pointing out flaws and limitations. Such model is finally updated and changed in order to make it more reliable and more representative of the reality. Different scenarios are tested and results are compared using graphs and tables.

Copy-paste data augmentation for domain transfer on traffic signs

City streets carry a lot of information that can be exploited to improve the quality of the services the citizens receive. For example, autonomous vehicles need to act accordingly to all the element that are nearby the vehicle itself, like pedestrians, traffic signs and other vehicles. It is also possible to use such information for smart city applications, for example to predict and analyze the traffic or pedestrian flows. Among all the objects that it is possible to find in a street, traffic signs are very important because of the information they carry. This information can in fact be exploited both for autonomous driving and for smart city applications. Deep learning and, more generally, machine learning models however need huge quantities to learn. Even though modern models are very good at gener- alizing, the more samples the model has, the better it can generalize between different samples. Creating these datasets organically, namely with real pictures, is a very tedious task because of the wide variety of signs available in the whole world and especially because of all the possible light, orientation conditions and con- ditions in general in which they can appear. In addition to that, it may not be easy to collect enough samples for all the possible traffic signs available, cause some of them may be very rare to find. Instead of collecting pictures manually, it is possible to exploit data aug- mentation techniques to create synthetic datasets containing the signs that are needed. Creating this data synthetically allows to control the distribution and the conditions of the signs in the datasets, improving the quality and quantity of training data that is going to be used. This thesis work is about using copy-paste data augmentation to create synthetic data for the traffic sign recognition task.