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Publications

Publications by Vera Lúcia Costa

2017

Understanding commercial synergies between public transport and services located around public transport stations

Authors
Ferreira, MC; Costa, V; Dias, TG; Falcão E Cunha, J;

Publication
Transportation Research Procedia

Abstract

2015

How to predict journey destination for supporting contextual intelligent information services?

Authors
Costa, V; Fontes, T; Costa, PM; Dias, TG;

Publication
2015 IEEE 18TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS

Abstract
The adoption of smart cards in urban public transport has fundamentally changed how transport providers manage and plan their networks. Traveller information services, in particular, have leveraged this contextual data for targeting passengers and providing relevant information. Thus, it becomes increasingly relevant for the next generation of services to obtain on-time contextual passenger information, to support the development of intelligent information services. In this paper an adaptation of the Top-K algorithm is proposed for predicting journey destination, applied to different scenarios in public transport. The performance and efficiency are analysed and compared to a decision tree classifier. Finally, the feasibility and potential of applying the proposed methods to large-scale systems in a real-world environment is discussed.

2019

Prediction of Journey Destination for Travelers of Urban Public Transport: A Comparison Model Study

Authors
Costa, V; Fontes, T; Borges, JL; Dias, TG;

Publication
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering - Intelligent Transport Systems, From Research and Development to the Market Uptake

Abstract

2020

Average Speed of Public Transport Vehicles Based on Smartcard Data

Authors
Costa, V; Borges, JL; Dias, TG;

Publication
Smart Systems Design, Applications, and Challenges - Advances in Computational Intelligence and Robotics

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.

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