adding intelligence to a smartphone application prototype for exchaniging public transport information among travellers

• Autor(a) principal: Luís Carlos Moreira Dias
• Data de Publicação: 2014
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: https://hdl.handle.net/10216/72301
• topic: Engenharia electrotécnica, electrónica e informática; Electrical engineering, Electronic engineering, Information engineering

The increasing number of private owned cars in urban areas is contributing to the increase of urban mobility problems. Thus, the need for expanding of the public transport utilization is imminent, and one of the main decisions to be made centres in improving the traveller's experience. At the same time, the massification of the social networks' use and the increase on the number of smartphones available is changing the way we share information and everyday the mobility of the user is less of a problem. The increase of users in public transports connected to social networks in their journey allows them to share in real time information regarding their travel. This information can be useful to other users, giving relevant knowledge that can influence behaviour, and to the transport company managers, providing reliable data to be used on decision making. With a better understanding of the travellers' travel patterns and the similarities with other travellers' routines, the relevance and efficiency of the information shared in a public transport can be largely improved. At the same time, the behaviour of the user can be understood with this information, providing the identification of an unique user profile. In the context of this problem, this dissertation aims to propose the implementation of algorithms that identifies travel patterns of public transport users, which allows the sharing of information with improved efficiency and relevance. These goals are achieved by inferring travellers' destinations and predicting future travels, modelling travel patterns and creating networks of users who share similar travel routines. The results of this work will improve an existing prototype of a smartphone application for the exchange of public transport related information between travellers that was developed in the context of the same project. It will allow users to automatically connect with travellers with similar routines to get and share relevant information to their current or predicted future travel.