Understanding the Impacts of the COVID-19 Pandemic on Public Transportation Travel Patterns in the City of Lisbon
The ongoing COVID-19 pandemic is creating disruptive changes in urban mobility that may compromise the sustainability of the public transportation system. As a result, worldwide cities face the need to integrate data from different transportation modes to dynamically respond to changing conditions....
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doaj-f43b77bf78d94d1893080254159a78452021-08-06T15:32:40ZengMDPI AGSustainability2071-10502021-07-01138342834210.3390/su13158342Understanding the Impacts of the COVID-19 Pandemic on Public Transportation Travel Patterns in the City of LisbonJoao T. Aparicio0Elisabete Arsenio1Rui Henriques2INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, PortugalLNEC, Department of Transport, 1700-066 Lisbon, PortugalINESC-ID, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, PortugalThe ongoing COVID-19 pandemic is creating disruptive changes in urban mobility that may compromise the sustainability of the public transportation system. As a result, worldwide cities face the need to integrate data from different transportation modes to dynamically respond to changing conditions. This article combines statistical views with machine learning advances to comprehensively explore changing urban mobility dynamics within multimodal public transportation systems from user trip records. In particular, we retrieve discriminative traffic patterns with order-preserving coherence to model disruptions to demand expectations across geographies and show their utility to describe changing mobility dynamics with strict guarantees of statistical significance, interpretability and actionability. This methodology is applied to comprehensively trace the changes to the urban mobility patterns in the Lisbon city brought by the current COVID-19 pandemic. To this end, we consider passenger trip data gathered from the three major public transportation modes: subway, bus, and tramways. The gathered results comprehensively reveal novel travel patterns within the city, such as imbalanced demand distribution towards the city peripheries, going far beyond simplistic localized changes to the magnitude of traffic demand. This work offers a novel methodological contribution with a solid statistical ground for the spatiotemporal assessment of actionable mobility changes and provides essential insights for other cities and public transport operators facing mobility challenges alike.https://www.mdpi.com/2071-1050/13/15/8342public transportationmultimodalityCOVID-19order-preserving traffic dynamicsdiscriminative pattern miningsustainable mobility |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Joao T. Aparicio Elisabete Arsenio Rui Henriques |
spellingShingle |
Joao T. Aparicio Elisabete Arsenio Rui Henriques Understanding the Impacts of the COVID-19 Pandemic on Public Transportation Travel Patterns in the City of Lisbon Sustainability public transportation multimodality COVID-19 order-preserving traffic dynamics discriminative pattern mining sustainable mobility |
author_facet |
Joao T. Aparicio Elisabete Arsenio Rui Henriques |
author_sort |
Joao T. Aparicio |
title |
Understanding the Impacts of the COVID-19 Pandemic on Public Transportation Travel Patterns in the City of Lisbon |
title_short |
Understanding the Impacts of the COVID-19 Pandemic on Public Transportation Travel Patterns in the City of Lisbon |
title_full |
Understanding the Impacts of the COVID-19 Pandemic on Public Transportation Travel Patterns in the City of Lisbon |
title_fullStr |
Understanding the Impacts of the COVID-19 Pandemic on Public Transportation Travel Patterns in the City of Lisbon |
title_full_unstemmed |
Understanding the Impacts of the COVID-19 Pandemic on Public Transportation Travel Patterns in the City of Lisbon |
title_sort |
understanding the impacts of the covid-19 pandemic on public transportation travel patterns in the city of lisbon |
publisher |
MDPI AG |
series |
Sustainability |
issn |
2071-1050 |
publishDate |
2021-07-01 |
description |
The ongoing COVID-19 pandemic is creating disruptive changes in urban mobility that may compromise the sustainability of the public transportation system. As a result, worldwide cities face the need to integrate data from different transportation modes to dynamically respond to changing conditions. This article combines statistical views with machine learning advances to comprehensively explore changing urban mobility dynamics within multimodal public transportation systems from user trip records. In particular, we retrieve discriminative traffic patterns with order-preserving coherence to model disruptions to demand expectations across geographies and show their utility to describe changing mobility dynamics with strict guarantees of statistical significance, interpretability and actionability. This methodology is applied to comprehensively trace the changes to the urban mobility patterns in the Lisbon city brought by the current COVID-19 pandemic. To this end, we consider passenger trip data gathered from the three major public transportation modes: subway, bus, and tramways. The gathered results comprehensively reveal novel travel patterns within the city, such as imbalanced demand distribution towards the city peripheries, going far beyond simplistic localized changes to the magnitude of traffic demand. This work offers a novel methodological contribution with a solid statistical ground for the spatiotemporal assessment of actionable mobility changes and provides essential insights for other cities and public transport operators facing mobility challenges alike. |
topic |
public transportation multimodality COVID-19 order-preserving traffic dynamics discriminative pattern mining sustainable mobility |
url |
https://www.mdpi.com/2071-1050/13/15/8342 |
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