José Luís Borges

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.

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

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).