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About

Tânia Fontes is an FCT researcher at INESC TEC. Her area of ??expertise is urban mobility, focusing in particular on the assessment of the environmental impacts of mobility. Her research interests include the areas of last mile logistics, transport policy assessment, data science and decision support systems design. Tânia has led several research projects in the area of ??passenger and cargo mobility, particularly in urban spaces (opti-MOVES and e-LOG). Besides these, she has actively collaborated on other research projects (eg Seamless Mobility, SmartDecision, CIVITAS-ELAN), consultancy projects (eg VoxPop), and Cost actions (eg ARTS, TEA, TRANSITS). In 2016, she spent 6 months in Beijing to study the impacts of transport policies on the city's air quality. She regularly publishes in journals in the field of transport and environment. Tânia holds a PhD in Sciences Applied to the Environment from the University of Aveiro (2010). She also has a degree in Computer Engineering (ISEP, 2007) and Environmental Engineering (UFP, 2001).

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Details

Details

002
Publications

2022

Real-Time Detection of Vehicle-Based Logistics Operations

Authors
Ribeiro, J; Tavares, J; Fontes, T;

Publication
INTELLIGENT TRANSPORT SYSTEMS (INTSYS 2021)

Abstract

2022

Detection of vehicle-based operations from geolocation data

Authors
Tavares, J; Ribeiro, J; Fontes, T;

Publication
Transportation Research Procedia

Abstract

2021

Forecasting of Urban Public Transport Demand Based on Weather Conditions

Authors
Correia, R; Fontes, T; Borges, JL;

Publication
Advances in Intelligent Systems and Computing

Abstract
Weather conditions have a major impact on citizens’ daily mobility. Depending on weather conditions trips may be delayed, demand may be changed as well as the modal shift. These variations have a major impact on the use and operation of public transport, particularly in transport systems that operate close to capacity. However, the influence of weather conditions on transport demand is difficult to predict and quantify. For this purpose, an artificial neural network model – the Multilayer Perceptron – is used as a regression model to estimate the demand of urban public transport buses based on weather conditions. Transit bus ridership and weather conditions were collected along a year from a medium-size European metropolitan area (Oporto, Portugal) and linked under the assumption that individuals choose the travel mode based on the weather conditions that are observed during the departure hour, the hour before and two hours before. The transit ridership data were also labelled according to the hour, day of the week, month, and whether there was a strike and/or holiday or not. The results demonstrate that it is possible to predict the demand of public transport buses using the weather conditions observed two hours before with low error for the entire network (MAE = 143 and RMSE = 322). The use of weather conditions allow to decreases the error of the prediction by ~8% for the entire network. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

2021

Are BERT embeddings able to infer travel patterns from Twitter efficiently using a unigram approach?

Authors
Murços, F; Fontes, T; Rossetti, RJF;

Publication
IEEE International Smart Cities Conference, ISC2 2021, Manchester, United Kingdom, September 7-10, 2021

Abstract

2020

Design of a Route-Planner for Urban Public Transport, Promoting Social Inclusion

Authors
Dias, R; Fontes, T; Galvao, T;

Publication
INTELLIGENT TRANSPORT SYSTEMS

Abstract
People that do not have access to the transport system and therefore, a facilitated access to goods and services essential to daily life, can be regarded as transport-related social excluded. This is a big issue, namely for groups of people that have physical, sensorial and/or cognitive limitations. This paper provides guidelines to design route planners for socially excluded groups, by promoting social inclusion in public transportation. For this purpose, a set of mock-up user-interfaces of an inclusive inter-modal route planning application were developed. These interfaces will deliver ready availability of information about infrastructures and other journey related data. © 2020, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Supervised
thesis

2021

Discovery of Transport Operations from Geolocation Data

Author
Jorge Alberto da Mota Vieira Tavares

Institution
UP-FEUP

2020

Especificação de requisitos para o desenvolvimento de um sistema de apoio à decisão para gestão de transportes públicos intermodais

Author
João Nuno Lemos de Sousa

Institution
UP-FEUP

2020

Automatic identification of anomalies in the operation of urban public transport networks due to meteorological events

Author
Ricardo Fernandes Correia

Institution
UP-FEUP

2019

Design of a route-planner for urban public transport, promoting social inclusion

Author
Rafael Marques Dias

Institution
UP-FEUP

2016

MobileVJ: A mobile app for a novel wearable human sensing system

Author
Pavel Alexeenko

Institution
UP-FEUP