Teresa Galvão

Abstract

Abstract
This paper proposes an ontology-based framework to support integration and visualization of data from Intelligent Transportation Systems. These activities may be technically demanding for transportation stakeholders, due to technical and human factors, and may hinder the use of visualization tools in practice. The existing ontologies do not provide the necessary semantics for integration of spatio-temporal data from such systems. Moreover, a formal representation of the components of visualization techniques and expert knowledge can leverage the development of visualization tools that facilitate data analysis. The proposed Visualization-oriented Urban Mobility Ontology (VUMO) provides a semantic foundation to knowledge-assisted visualization tools (KVTs). VUMO contains three facets that interrelate the characteristics of spatio-temporal mobility data, visualization techniques and expert knowledge. A built-in rule set leverages semantic technologies standards to infer which visualization techniques are compatible with analytical tasks, and to discover implicit relationships within integrated data. The annotation of expert knowledge encodes qualitative and quantitative feedback from domain experts that can be exploited by recommendation methods to automate part of the visualization workflow. Data from the city of Porto, Portugal were used to demonstrate practical applications of the ontology for each facet. As a foundational domain ontology, VUMO can be extended to meet the distinctiveness of a KVT. © 2020

Abstract
With the continuous growth and complexity of public transport systems, it is essential that the users have access to transport maps that help them easily understand the underlying network, thus facilitating the user experience and public transports ridership. Spider Maps combine elements from geographical and schematic maps, to allow answering questions like “From where I am, where can I go?”. Although these maps could be very useful for travellers, they still are mostly manually generated and not widely used. Moreover, these maps have several design constraints, which turns the automation of the generation process into a complex problem. Although optimisation techniques can be applied to support the generation process, current solutions are time expensive and require heavy computational power. This paper presents a solution to automatically generate spider maps. It proposes an algorithm that adapts current methods and generates viable spider map solutions in a short execution time. Results show successful spider maps solutions for areas in Porto city. © 2020, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Abstract
Millions of people use public transport on a daily basis. Although most public transport systems use traditional ticketing approaches, based on tickets and smartcards, there are already ticketing alternatives based on smartphones. Most of the mobile ticketing solutions developed and available in the market use technologies such as Near Field Communication (NFC) or Quick Response Codes (QR Codes), and there are practically no studies on the use of Bluetooth Low Energy (BLE) for this purpose. This paper focuses on assessing the feasibility of using BLE beacons for mobile ticketing in urban passenger transport. The study was conducted during the development of a mobile ticketing solution for the Metropolitan Area of Porto (AMP) that takes advantage of the Bluetooth technology present on the passengers' smartphones. It uses BLE beacons to track the passengers' trips from the start to the end, as part of an implementation of a check-in/be-out system. This solution was implemented as a prototype to be tested in the AMP and all the tests performed were made during the course of a pilot test of this prototype. The study consisted of a set of technical tests related with beacons signal monitoring and the gathering and analysis of passengers' feedback who participated in a four months pilot test. The results obtained suggest that the BLE technology is feasible for mobile ticketing in urban passenger transport. The paper also presents the various available deployment alternatives, identifies the main problems found and proposes solutions to solve them, filling an important research gap in the literature. © 2020 The Authors

Abstract
In public transport, traveler dissatisfaction is widespread, due to long waits and travel time, or the low frequency of the service provided. Public transport providers are increasingly concerned about improving the service provided. To improve public transport, detailed knowledge of the network and its weaknesses is necessary. An easy and cheap way to achieve this information is to extract knowledge from the data daily collected in a public transport network. Thus, this chapter focuses on data analysis resulting from the smartcard-based ticketing system. The main objective is to detect patterns of average speed for all days of the week and times of the day, along with pairs of consecutive stops. To perform the analyses, the average speed was deduced from ticketing data, and clustering methods were applied. The results show that it is possible to find segments with similar patterns and identify days and times with similar patterns.