Analysis of Public Transportation in Montevideo, Uruguay During the COVID-19 Pandemic

This article presents the analysis of the demand and the characterization of mobility using public transportation in Montevideo, Uruguay, during the COVID-19 pandemic. A urban data-analysis approach is applied to extract useful insights from open data from different sources, including mobility of citizens, the public transportation system, and COVID cases. The proposed approach allowed computing significant results to determine the reduction of trips caused by each wave of the pandemic, the correlation between the number of trips and COVID cases, and the recovery of the use of the public transportation system. Overall, results provide useful insights to quantify and understand the behavior of citizens in Montevideo, regarding public transportation during the COVID-19 pandemic. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

The Impact of COVID-19 on Human Mobility: A Case Study on New York

COVID-19 has dramatically changed people's mobility patterns. This report aims to analyze the impact of COVID-19 on people's mobility through statistics and comparing the visits of POIs (Point-Of-Interests) in New York State in 2019 and 2020. The report uses data from SafeGraph, which is a data company. The raw data contains POI visits across the United States in 2019 and 2020. Considering the analysis size and difficulty of the data, POI visits from New York State are extracted for analysis, and POI locations are classified according to the tags provided by the source data. The scale of analysis is from macro to micro, and they are the total POI visits data of New York State based on different ways in 2019 and 2020, the POI visits of CBG (Census Block Group) division in New York City, and three representative POI samples to do individual analysis. The analysis methods are: (1) use line plot and bar plot statistics to compare the trends of POI visits data from 2019 to 2020, and (2) make the spatial visualization comparison, which includes grid map, scatter map, heatmap, and OD map, between the first peak of epidemic impact in the first full week of April 2019 and April 2020, and the scope is narrowed to New York City. Wherein the OD maps are drawn based on the CBG division. Compared to related work, the analysis object includes CBG, categories, and individual POI. In addition, the analysis method combines statistical graphs and spatial visualizations and explores the policy impact of the New York City government. This report adopts more multidimensional analysis methods and objects to improve the comprehensiveness and reliability of the analysis content. © 2022 IEEE.

VISUAL ANALYTICS OF MOBILITY NETWORK CHANGES OBSERVED USING MOBILE PHONE DATA DURING COVID-19 PANDEMIC

The limited exchange between human communities is a key factor in preventing the spread of COVID-19. This paper introduces a digital framework that combines an integration of real mobility data at the country scale with a series of modeling techniques and visual capabilities that highlight mobility patterns before and during the pandemic. The findings not only significantly exhibit mobility trends and different degrees of similarities at regional and local levels but also provide potential insight into the emergence of a pandemic on human behavior patterns and their likely socio-economic impacts. © 2022 IEEE.

Objective and perceived acoustic environments during the COVID-19 lockdown: An overview of evidence

The COVID-19 pandemic took a heavy toll on population health directly, but also triggered profound changes to social life, daily mobility patterns, and activity spaces. At the beginning, public health measures for limiting the spread of the virus mandated home confinement and limited outdoor activities, which in turn reshaped typical acoustic environments for many people. This overview provides a synopsis of the evidence of changes to residential noise exposure and perceived soundscape quality and components across different contexts. Most studies reported sound level reductions in the range of 4-10 dB. Reductions were larger on weekends compared with weekdays, and in previously socially active areas compared with traffic-dominated locations. People had a clear preference for the new lockdown soundscapes. Traffic noise levels reportedly declined across various settings, allowing for natural sounds, hitherto masked, to become more prominent. © 2022 Internoise 2022 - 51st International Congress and Exposition on Noise Control Engineering. All rights reserved.

Charging after Lockdown: The Aftermath of COVID-19 Policies on Electric Vehicle Charging Behaviour in The Netherlands

During the COVID-19—related lockdowns (2020–2022), mobility patterns and charging needs were substantially affected. Policies such as work from home, lockdowns, and curfews reduced traffic and commuting significantly. This global pandemic may have also substantially changed mobility patterns on the long term and therefore the need for electric vehicle charging infrastructure. This paper analyzes changes in electric charging in the Netherlands for different user groups during different phases of the COVID-19 lockdown to assess the effects on EV charging needs. Charging needs dropped significantly during this period, which also changed the distribution of the load on the electricity grid throughout the day. Curfews affected the start times of charging sessions during peak hours of grid consumption. Infrastructure dedicated to commuters was used less intensively, and the charging needs of professional taxi drivers were drastically reduced during lockdown periods. These trends were partially observed during a post–lockdown measuring period of roughly 8 months, indicating a longer shift in mobility and charging patterns. © 2023 by the authors.

The Analysis of Mobility Patterns during the COVID-19 Pandemic in Thailand Using Time Series Clustering

The COVID-19 pandemic has affected the lives, health, economics, and travel of all nations, including Thailand. The purpose of this study is to investigate human mobility patterns during the pandemic. We opted to use the public transportation data from January 1st, 2020 until September 28th, 2022 collected from the Ministry of Transport, Thailand as a data source. We conducted a time series study on trend and seasonality patterns, as well as clustering analysis. It can be concluded that public buses and Bangkok electric trains, nationwide state trains and domestic air travel are the two pairs of public transportation with the most similar usage patterns. Moreover, the majority of personal car travel patterns are quite similar to public buses and Bangkok electric trains during some periods. © 2023 IEEE.

An Evidence Study of Long-term Impacts on Mobility Patterns Brought by COVID-19

The outbreak of COVID-19 at the end of 2019 has posed an enormous threat to people's physical and psychological health, especially those who are infected during the epidemic. Understanding how the infected people behaved during the pandemic and whether long-term effects are exerted even after they were cured is essential for guiding them to conduct a more comprehensive recovery. Large scale crowd-sourced data provides a chance to investigate their behavior patterns. In this paper, we explore the possible differences in mobility patterns between the infected and the uninfected, relying on a large volume of crowd -sourced location data contributed by smartphone users consisting of 11,414 infected cases and 12,793 uninfected people between Jun. 1, 2019 and Dec 31, 2020 in Wuhan, China. We characterize mobility distinctions of the two groups by introducing five mobility indicators that accurately capture spatio-temporal patterns of human mobility. We reveal that the infected kept higher mobility level during the pandemic. Moreover, the COVID-19 caused lower recovery efficiency on mobility of the infected, including later recovery time, lower speed and worse status. © 2022 IEEE.

Mobility patterns and COVID growth: Moderating role of country culture.

The COVID-19 pandemic has resulted in countries reacting differently to an ongoing crisis situation. Latent to this reaction mechanism is the inherent cultural characteristics of each society resulting in differential responses to epidemic spread. Epidemiological studies have confirmed the positive effect of population mobility on the growth of infection. However, the effect of culture on indigenous mobility patterns during pandemics needs further investigation. This study aims to bridge this gap by exploring the moderating role of country culture on the relationship between population mobility and growth of CoVID-19. Hofstede's cultural factors; power distance, individualism/collectivism, masculinity/femininity, uncertainty avoidance, long-term and short-term orientation are hypothesised to moderate the effect of mobility on the reproduction number (R) of COVID-19. Panel regression model, using mobility data and number of confirmed cases across 95 countries for a period of 170 days has been preferred to test the hypotheses. The results are further substantiated using slope analysis and Johnson-Neyman technique. The findings suggest that as power distance, individualism and long-term orientation scores increase, the impact of mobility on epidemic growth decreases. However, masculinity scores in a society have an opposite moderating impact on epidemic growth rate. These Hofstede factors act as quasi moderators affecting mobility and epidemic growth. Similar conclusions could be not be confirmed for uncertainty avoidance. Cross-cultural impact, as elucidated by this study, forms a crucial element in policy formulation on epidemic control by indigenous Governing bodies.

Changes in mobility patterns and the switching roles of second homes as a result of the first wave of COVID-19

The COVID-19 pandemic has already had significant impact on tourist flows worldwide. The requirements of safe models of tourism in the time of COVID-19, avoiding crowded localities and providing individual types of accommodation, can largely be met in second homes. This study aims to examine whether and how the COVID-19 pandemic and related restrictions impacted the usage patterns of second homes in terms of: (1) the number of visits and length of stay, (2) the purpose of the second-home utilisation. An integral part of the study was to recognise how these new and existing im/mobilities were determined by a range of personal, social, contextual, and relational factors. The data collected from direct interviews and online surveys was tested using sign and Wilcoxon tests, while the interactive classification tree (C&RT) model was used to explain the reasons for changing or maintaining an existing second-home usage pattern. The research results showed that for most second-home owners their home-usage pattern remained the same as in 2019. If it changed, it was more common to extend the stay by moving in, working at a distance, or commuting to work, rather than to shorten the stay at the second home. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

Mobility Pattern Analysis during 2020 Chinese National Day under the COVID-19 pandemic

With the fast development of new technologies, such as Internet of Things, big data and Internet plus, Intelligent Transportation Systems (ITS) have made remarkable achievements and the intelligence in ITS has also been continuously increased, which a new field, i.e., Social Transportation, is emerging. In social transportation systems, physical and cyber elements are tightly conjoined, coordinated, and integrated with human and social characteristics. In this paper, we collect and analyze traffic data from physical world and social media data from cyberspace to sense the human mobility patterns during holidays under the COVID-19 pandemic. © 2022 IEEE.

The role of neighbourhood design in cycling activity during COVID-19: An exploration of the Melbourne experience.

COVID-19 restrictions imposed significant changes on human mobility patterns, with some studies finding significant increases or decreases in cycling. However, to date there is little understanding on how the neighbourhood-level built environment influenced cycling behaviour during the COVID-19 restrictions. As different neighbourhood have different built environment characteristics, it is possible that cycling trends varied across different built environment settings. We aimed to answer this question by examining recreational cycling during different stages of lockdown in Melbourne, Australia. We compared self-reported recreational cycling frequency (weekly) data from 1344 respondents between pre-COVID and two different stages in lockdown. We tested whether the built environment of their residential neighbourhood and different sociodemographic characteristics influenced leisure cycling rates and whether the effect of these factors varied between different stages of COVID-19 restriction. We found that cycling declined significantly during the two stages of COVID-19 lockdown. Cycling infrastructure density and connectivity are two built environment factors that had a significant effect on limiting the decline in leisure cycling during the pandemic. Furthermore, men and younger people had higher cycling rates in comparison to other groups, suggesting that restrictions on indoor activities and travel limits were not enough to encourage women or older people to cycle more during the pandemic.

An Incremental Learning-based Framework for Non-stationary Traffic Representations Clustering and Forecasting

To curb the growth of COVID-19, many rules, including a work-from-home policy, were issued in 2020. While these limits successfully prevented the virus's transmission, they completely altered original mobility patterns, resulting in considerable reductions in travel time and vehicle miles traveled. Under this non-stationary data stream, the US Department of Transportation struggled to anticipate future traffic conditions. Obviously, two essential challenges need to be addressed immediately: 1) it is challenging for transportation agencies to learn representative traffic patterns from the continually changing traffic circumstances. And 2) when and how should the forecasting model be updated to learn new patterns without forgetting previous tasks? We proposed an incremental learning-based framework for non-stationary data clustering and forecasting in transportation scenarios to tackle the issues mentioned above. It is a dual-module architecture that includes a Temporal Neighborhood Clustering module and an Incremental Learning module. The objective of the first component is to dynamically detect the optimal boundary for clustering statistically similar neighbors by enlarging both the in-group similarity and between-group dissimilarity. The second module applies the online-EWC approach, which is commonly used in image classification tasks but rarely in regression models, to learn new tasks and avoid catastrophic forgetting, which is a typical occurrence while training neural networks with multiple tasks. Experiments on the Greater Seattle Area employed loop detector data collected in 2020 yielded reliable prediction performance in both robustness and accuracy. The dual-module framework can generate promising results from pre-COVID-19 to post-COVID-19 time frames. This framework would aid government agencies and the general public in developing long-term policies and strategies for post-pandemic intelligent transportation systems. © 2022 IEEE.

Big Data-Driven Approach to Analyzing Spatio-Temporal Mobility Pattern

It is imperative to understand human movement and behavior, from epidemic monitoring to complex communications. So far, most research and studies on investigating and interpreting human movements have traditionally depended on private and accumulated data such as mobile records. In this work, social network data is suggested as a proxy for human mobility, as it relies on a large amount of publicly accessible data. A mechanism for urban mobility mining and extraction scheme is proposed in this research to shed light on the importance and benefits of the publicly available social network data. Given the potential value of the Big Data obtained from social network platforms, we sought to demonstrate the process of analyzing and understanding human mobility patterns and activity behavior in urban areas through the social network data. Human mobility is far from spontaneous, follows well-defined statistical patterns. This research provides evidence of spatial and temporal regularity in human mobility patterns by examining daily individual trajectories of users covering an average time span of three years (2018 to 2020). Despite the diversity of individual movements history, we concluded that humans follow simple, reproducible patterns. Additionally, we studied and evaluated the effect of COVID-19 on human mobility and activity behavior in urban areas and established a strong association between human mobility and COVID-19 spread. Numerous years of mobility data analysis can reveal well-established trends, such as social or cultural activities, which serve as a baseline for detecting anomalies and changes in human mobility and activity behavior.

Multiwave COVID-19 Prediction from Social Awareness Using Web Search and Mobility Data

Recurring outbreaks of COVID-19 have posed enduring effects on global society, which calls for a predictor of pandemic waves using various data with early availability. Existing prediction models that forecast the first outbreak wave using mobility data may not be applicable to the multiwave prediction, because the evidence in the USA and Japan has shown that mobility patterns across different waves exhibit varying relationships with fluctuations in infection cases. Therefore, to predict the multiwave pandemic, we propose a Social Awareness-Based Graph Neural Network (SAB-GNN) that considers the decay of symptom-related web search frequency to capture the changes in public awareness across multiple waves. Our model combines GNN and LSTM to model the complex relationships among urban districts, inter-district mobility patterns, web search history, and future COVID-19 infections. We train our model to predict future pandemic outbreaks in the Tokyo area using its mobility and web search data from April 2020 to May 2021 across four pandemic waves collected by Yahoo Japan Corporation under strict privacy protection rules. Results demonstrate our model outperforms state-of-the-art baselines such as ST-GNN, MPNN, and GraphLSTM. Though our model is not computationally expensive (only 3 layers and 10 hidden neurons), the proposed model enables public agencies to anticipate and prepare for future pandemic outbreaks. © 2022 Owner/Author.

Simulating pandemic emergency scenarios considering mobility patterns through an agent-based model

One of the main policies to contain a pandemic spreading is to reduce people mobility. However, it is not easy to predict its actual impact, and this is a limitation for policy-makers who need to act effectively and timely to limit virus spreading. Data are fundamental for monitoring purposes;however, models are needed to predict the impact of different scenarios at a granular scale. Based on this premise, this paper presents the first results of an agent-based model (ABM) able to dynamically simulate a pandemic spreading under mobility restriction scenarios. The model is here used to reproduce the first wave of COVID-19 pandemic in Italy and considers factors that can be attributed to the diffusion and lethality of the virus and population mobility patterns. The model is calibrated with real data (considering the first wave), and it is based on a combination of static and dynamic parameters. First results show the ability of the model to reproduce the pandemic spreading considering the lockdown strategy adopted by the Italian Government and pave the way for scenario analysis of different mobility restrictions. This could be helpful to support policy-making by providing a strategic decision-tool to contrast pandemics. © 2021 Copyright for this paper by its authors.

Modelling the relationship between covid-19 restrictive measures and mobility patterns across Europe using time-series analysis

Since early 2020, strict restrictions on non-essential movements were imposed globally as countermeasures to the rapid spread of COVID-19. The various containment and closures strategies, taken by the majority of countries, have directly affected travel behavior. This paper aims to investigate and model the relationship between covid-19 restrictive measures and mobility patterns across Europe using time-series analysis. Driving and walking data, as well as confinement policies were collected from February 2020 to February 2021 for twenty-five European countries and were implemented into Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors (SARIMAX) time-series models. Results reveal a significant number of models in order to estimate mobility during pandemic almost in every country of the study. School closing was found to be the most important exogenous factor for describing driving or walking, while “Stay at home” orders had not a significant effect on the evolution of people movements. In addition, countries which suffered the most due to the pandemic indicated a strong correlation with the restrictive measures. No time-series models were found to describe the countries which implemented weak confinement policies. © 2022 Marianthi Kallidoni, Christos Katrakazas, George Yannis.

Insight into mobility patterns under COVID-19: A time-sequence-based approach

Various measures have been taken to prevent the spread of COVID-19. Even with widespread vaccination, the control of the epidemic is still difficult due to the mutation of the virus. When an epidemic breaks out, the simplest and most efficient method of control is still social isolation, which greatly affects daily lives and mobility patterns. To study mobility patterns, we leveraged mobile base station data in Shulan, China, during the epidemic. Our main discoveries are as follows: (1) With the development of COVID-19, travel volumes and the scopes of trips were gradually reduced. (2) In addition to the government's prevention policy, media coverage of COVID-19 had a huge impact on mobility patterns. (3) Previous studies focused on morning and evening rush hours. However, our results show that humans tend to intensively travel at noon. (4) The travel network was significantly more active in the early stages of the COVID-19 outbreak;hence, the possibility of disease transmission was greater. (5) With the development of the epidemic, travel intervals became increasingly longer, and the number of contacts between base stations decreased. (6) By analyzing the temporal path length, we found that some nodes were still active during the epidemic. © 2021 ACM.

Assessing the Economic Feasibility of PV-BESS Systems in Connection with Pandemic-induced Loads

COVID-19 has impacted on energy (uses, load profiles) and mobility (patterns, means of transport). The change can create a fertile environment for local renewable generation, energy storage, and electrical mobility. In this context, the scope of this work is to assess the economic feasibility of a residential system of PV generation, electrochemical storage, and charging point, following pandemic-induced loads. First, the prospected impact is estimated via a simplified model. Then, a dynamic model is used to simulate the system operation subjected to a rule-based control. Different scenarios are considered, to assess the impact of the pandemic. The results show that the pandemic loads increase the savings from the PV-BESS system, +36% in the presence of a home-charged EV and +40% in its absence. Further savings can be theoretically achieved by tailoring the system design to the specific load demand. © 2021 IEEE

Telecommunication activity-derived public mobility indices as drivers of PM2.5 concentration in northwestern Croatia at the time of Covid-19 outbreak in the early 2020

The Covid-19 pandemic has emerged as a challenge for numerous socio-economic processes established in times prior the outbreak. Here we contribute to the subject with an analysis of the mobility pattern changes, derived from telecommunications location services data, and their effects on the air pollution with the PM2.5 particles during the imposed lockdown in Krapinsko-zagorska county (Županija krapinsko-zagorska) in the north-western Croatia in the first quarter of 2020. The study is conducted using the publicly available data. Statistical analysis reveals the complex cause-effect relationship between the PM2.5 concentration, as the measure of air quality, and the general public mobility. We continue our research to contribute to understanding of the Covid-19 epidemiological development, and the socio-economic impact on the climate change and environmental conditions © 2021 IEEE.

Exploring Post COVID-19 Outbreak Intradaily Mobility Pattern Change in College Students: A GPS-Focused Smartphone Sensing Study.

With the outbreak of the COVID-19 pandemic in 2020, most colleges and universities move to restrict campus activities, reduce indoor gatherings and move instruction online. These changes required that students adapt and alter their daily routines accordingly. To investigate patterns associated with these behavioral changes, we collected smartphone sensing data using the Beiwe platform from two groups of undergraduate students at a major North American university, one from January to March of 2020 (74 participants), the other from May to August (52 participants), to observe the differences in students' daily life patterns before and after the start of the pandemic. In this paper, we focus on the mobility patterns evidenced by GPS signal tracking from the students' smartphones and report findings using several analytical methods including principal component analysis, circadian rhythm analysis, and predictive modeling of perceived sadness levels using mobility-based digital metrics. Our findings suggest that compared to the pre-COVID group, students in the mid-COVID group generally 1) registered a greater amount of midday movement than movement in the morning (8-10 a.m.) and in the evening (7-9 p.m.), as opposed to the other way around; 2) exhibited significantly less intradaily variability in their daily movement; 3) visited less places and stayed at home more everyday, and; 4) had a significant lower correlation between their mobility patterns and negative mood.