A network-based analysis to assess Covid-19 disruptions in the Bogotá BRT system
Bibliographic Data
| ID | 21247508 |
|---|---|
| Authors | Juan D Garcia-Arteaga (0000-0002-4534-6393, Universidad Nacional de Colombia, corresponding author), Laura Lotero (0000-0002-6537-3276, Universidad Pontificia Bolivariana) |
| Year | 2023 |
| Volume | 50 |
| Issue | 4 |
| Pages | 983-999 |
| Publication date | 2023-05-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Environment and Planning B Urban Analytics and City Science (JOURNAL) |
| Journal identifiers | ISSN: 2399-8083 • E-ISSN: 2399-8091 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/23998083221150646 |
| PMID | 38603410 |
| OpenAlex | W4315645058 |
| Language | EN |
| Citations received | 1 |
| References cited | 46 |
The global COVID-19 crisis has severely affected mass transit in the cities of the global south. Fear of widespread propagation in public spaces and the dramatic decrease in human mobility due to lockdowns have resulted in a significant reduction of public transport options. We analyze the case of TransMilenio in Bogotá, a massive Bus Rapid Transit system that is the main mode of transport for an urban area of roughly 10 million inhabitants. Concerns over social distancing and new health regulations reduced the number of trips to under 20% of its historical values during extended periods of time during the lockdowns. This has sparked a renewed interest in developing innovative data-driven responses to COVID-19 resulting in large corpora of TransMilenio data being made available to the public. In this paper we use a database updated daily with individual passenger card swipe validation microdata including entry time, entry station, and a hash of the card’s ID. The opportunity of having daily detailed minute-to-minute ridership information and the challenge of extracting useful insights from the massive amount of raw data (∼1,000,000 daily records) require the development of tailored data analysis approaches. Our objective is to use the natural representation of urban mobility offered by networks to make pairwise quantitative similarity measurements between daily commuting patterns and then use clustering techniques to reveal behavioral disruptions as well as the most affected geographical areas due to the different pandemic stages. This method proved to be efficient for the analysis of large amount of data and may be used in the future to make temporal analysis of similarly large datasets in urban contexts
Business · Computer security · Environmental health · Geography · Pandemic · Public transport · Raw data · Smart card · SwIPe · Transport engineering · Travel behavior · Travel survey · TRIPS architecture · Computer Science · COVID-19 epidemiological studies · Engineering · Human Mobility and Location-Based Analysis · Medicine · Urban Transport and Accessibility
Social Network Analysis
The promises of big data and small data for travel behavior (aka human mobility) analysis
A tutorial on spectral clustering
The effect of human mobility and control measures on the Covid-19 epidemic in China
Multiscale dynamic human mobility flow dataset in the U.S. during the Covid-19 epidemic
Exploring the impacts of Covid-19 on travel behavior and mode preferences
The worldwide air transportation network
Changes in mobility and socioeconomic conditions during the Covid-19 outbreak
The Coronavirus crisis
Presence and mobility of the population during the first wave of Covid-19 outbreak and lockdown in Italy
Human mobility behavior in Covid-19
Democracy on the move? Bogotá’s urban transport strategies and the access to the city
Centrality in social networks conceptual clarification
Exploring the network structure and nodal centrality of China’s air transport network
| Citations per year | 0.33 |
|---|---|
| Citation span | 2023 - 2023 (1) |
| Highly cited | No |