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Publicações

Publicações por Joel Ribeiro

2020

Process discovery on geolocation data

Autores
Ribeiro, J; Fontes, T; Soares, C; Borges, JL;

Publicação
Transportation Research Procedia

Abstract

2020

A Deep Learning Approach for Predicting Bus Passenger Demand Based on Weather Conditions

Autores
Fontes, T; Correia, R; Ribeiro, J; Borges, JL;

Publicação
Transport and Telecommunication Journal

Abstract
AbstractThis work apply a deep learning artificial neural network model – the Multilayer Perceptron – as a regression model to estimate the demand of bus passengers. Transit bus ridership and weather conditions were collected over a year from a medium-size European metropolitan area and linked under the assumption: individuals choose the travel mode based on the weather conditions that are observed during (a) the departure hour, (b) the hour before or (c) two hours prior to the travel start. The transit ridership data were also labelled according to the hour of the day, day of the week, month, and whether there was a strike and/or holiday or not. The results show that the prediction error of the model decrease by ~9% when the weather conditions observed two hours before travel start is taken into account. The model sensitivity analyses reveals that the worst performance is obtained for a strike day of a weekday in spring (typically Wednesdays or Thursdays).

2020

Accessibility as an indicator to estimate social exclusion in public transport

Autores
Ribeiro, J; Fontes, T; Soares, C; Borges, JL;

Publicação
Transportation Research Procedia

Abstract