This paper analyzes public transit mobility of different economic classes of Curitiba, Brazil, exploring an official smart card dataset provided by the city. With the population divided into subsets corresponding to economic strata, we characterized vital spatial-temporal transit usage patterns, such as departure times and destinations reached by different economic classes. We also constructed a network representing the common origin and destination of public transit users, enabling discovering distinct patterns. Among the results, we observe that with the increase in wealth, the morning activity is postponed (on average for 2 h), and the spatial distribution of the trips becomes more localized compared with lower classes. We also show that our model captures fairly well realistic mobility patterns exploring a cheaper and larger-scale data source by comparing our results with a household travel survey from Curitiba. Understand how people in different economic classes appropriate urban spaces help to provide subsidies for, e.g., more sustainable economic development propositions.

This study analyzes the urban mobility of Curitiba through the use of public transit by the population divided into socioeconomic strata. To study the established mobility graph and its spatio-temporal patterns, a multilayered approach called Multi-Aspect Graph (MAG) with four aspects is applied. The results detail patterns that cannot be observed in a traditional graph, such as the most frequent routes and the most central points of the city, taking into account all the modeled aspects. The model used allows a strategic understanding of the use of the beneficial public transit system for different applications, such as actions in emergencies and outbreaks of rapidly contagious diseases.

Em tempos de pandemia, o transporte público pode ser crucial para a disseminação de vírus, principalmente nas grandes cidades. As vacinas costumam fazer parte das estratégias para reduzir o contágio; no entanto, estas podem ser escassas em cenários pandêmicos. Utilizando dados do sistema de transporte público, este trabalho propõe o uso de redes multicamadas variantes no tempo para identificar os principais locais críticos a serem considerados prioritários em intervenções, como campanhas de vacinação, para ajudar a reduzir o contágio nesse meio de locomoção. Nossa abordagem considera os pontos de ônibus críticos como pontos prioritários de vacinação, indicando que a vacinação nesses locais reduz a propagação da infecção usando menos doses do que uma vacinação aleatória. A abordagem proposta neste estudo não se limita às estratégias de vacinação, sendo também aplicável a outros problemas que compartilham propriedades semelhantes, mesmo em contextos diferentes.

In times of a global pandemic, public transit can be crucial to spreading viruses, especially in big cities. Many works have shown that the human infection risk could be extremely high due to the length of exposure time, transmission routes, and structural characteristics during public transportation, and this can result in the rapid spread of the infection. Vaccines are often part of strategies to reduce contagion; however, they can be scarce in pandemic settings. Considering real-world and large-scale traffic data, this work proposes using time-varying multilayer networks to identify the main critical places to be prioritized in interventions, such as vaccination campaigns, to help reduce contagion on public transit. We exemplify our strategy in different vaccination scenarios. First, when considering only critical bus stops as priority vaccination points, determined by our approach, we indicate that focusing on vaccination in these locations reduces the spread of infection using fewer doses than a random vaccination. In another experiment, we demonstrate the flexibility of our approach in identifying other critical points of interest, healthcare units in this case. Vaccination in these vital health units could also be a viable strategy to curb contagion using a predetermined number of doses. The approach proposed in this study is not limited to vaccination strategies. It also applies to other problems that share similar properties, even in several different contexts, such as optimization in public transit or exploring different points of interest to gather insights from other issues of interest.