ABSTRACT
With the frequent occurrence of COVID-19 virus, online learning is currently the response of most educational institutions. However, in online learning, teachers do not have enough grasp of students' learning status, so how to assess student concentration in online learning is of great significance. In order to reduce the interference of classroom teaching, this paper adopt a non-contact observation method to analyze and evaluate the students' facial features. Considering that student concentration is a fuzzy variable, a reasonable weight of each factor is constructed in combination with the analytic hierarchy method, and a framework for identifying the concentration of online learning that integrates multi-source data is proposed. Finally, the effect of concentration assessment was verified by experiments. © 2022 IEEE.
ABSTRACT
Conventional public transportation is an important part of public transportation, and it has always been the focus of urban transportation research to excavate the characteristics of public transportation and analyze residents' travel patterns. In 2020, the new crown epidemic broke out. The outbreak and continuation of the epidemic have caused shocks and challenges to conventional public transportation, and the characteristics of conventional public transportation have developed significantly. Taking Guangzhou as an example, this paper conducts bus IC card mining based on multi-source data fusion, and conducts research on the characteristics of changes in Guangzhou's regular bus travel rules under the influence of the new crown epidemic. Research shows that under the continuous influence of the epidemic, the scale of bus trips has dropped significantly, the attraction of conventional buses to commuter passengers has been weakened, special groups are important users of public transport, and the ride code has become the most important payment method. © 2022 SPIE.
ABSTRACT
COVID-19 has critically impacted many aspects of societies worldwide, particularly on mobility. This chapter summarizes impacts of the COVID-19 pandemic, reviews existing research, and identifies future research needs in the scope of traffic theory and modeling/optimization and traffic flow. We first review models on contagion spreading through transportation networks, including aggregated spatial metapopulation models and disaggregated individual-based models. Further research is needed to consider both intercity and intracity mobilities and leverage emerging multiple data resources for constructing individuals’ complete trip chains. Based on modeling contagion spreading, we further discuss transport operation needs in the aftermath of COVID-19. There remains a need for operating multimodal urban transport systems to satisfy basic travel demands while minimizing contagion risks. Relevant research needs are identified in optimizing transport operation via modern data acquisition technologies and advanced modeling methods. Practical intervention measures and policy implications are recommended for optimizing transport systems during the COVID-19 pandemic.
ABSTRACT
The outbreak of COVID-19 has been a critical social event in the past two years. The pandemic has seriously affected the world. Meanwhile, various forms of data about COVD-19 emerge on the Web endlessly, such as SNS discussions, Press releases, WHO statistics, etc. It is valuable work for government departments, news media, and health organizations to integrate and analyze these pandemics-related multi-source data on the web. In this work, we propose an interactive visual analytics system as CVAS that aims at mining and analyzing multi-source data concerned with COVD-19. Having been inspired by the Sankey diagram, we developed a view elaborately. Through appropriate interactions, massive patients' mobility data can be visualized, thus showing the spread features of the pandemic in time and space more specifically. In addition, we collected more than 10,000 trending topics and nearly 10 million related comments on the SNS as Sina Weibo. We performed NLP to analyze their sentiment, identifying key events since the outbreak and the impact of the pandemic on public sentiment. Part of our work was awarded at the China visualization and visual analysis conference (ChinaVis2020) and recognized by peers. © 2022 ACM.
ABSTRACT
Big Traffic data [1] is cross-border multi-source data for multiple industries, but traffic roads have brought significant economic and social benefits, the number of traffic accidents and casualties is on the rise. Among them, traffic accidents are related to many factors, such as weather and population density. The data set used in this article is open source in Barcelona. The Random Forest algorithm is used to screen essential risk factors, establish a traffic risk prediction model, and compare traffic risks before and after COVID-19. It is concluded that the outbreak of the new crown virus -19-19 has a great impact on people's travel and transportation. Finally, the R square of the model established by Random Forest is 0.9. The K-means clustering algorithm is used to determine the location of the accident handling centre. Moreover, the scope of each accident risk management centre can cover more than 85 percent of traffic accident sites from 2016 to 2020. © 2021 IEEE.
ABSTRACT
Coronaviruses have caused multiple global pandemics. As an emerging epidemic, the coronavirus disease relies on nonpharmacological interventions to control its spread. However, the specific effects of these interventions are unknown. To evaluate their effects, we extend the susceptible-latent-infectious-recovered model to include suspected cases, confirmed cases, and their contacts and to embed isolation, close contact tracing, and quarantine into transmission dynamics. The model simplifies the population into two parts: the undiscovered part (where the virus spreads freely-the extent of freedom is determined by the strength of social distancing policy) and the discovered part (where the cases are incompletely isolated or quarantined). Through the isolation of the index case (suspected or confirmed case) and the subsequent tracing and quarantine of its close contacts, the infections flow from the undiscovered part to the discovered part. In our case study, multisource data of the novel coronavirus SARS-CoV-2 (COVID-19) in Wuhan were collected to validate the model, the parameters were calibrated based on the prediction of the actual number of infections, and then the time-varying effective reproduction number was obtained to measure the transmissibility of COVID-19 in Wuhan, revealing the timeliness and lag effect of the nonpharmacological interventions adopted there. Finally, we simulated the situation in the absence of a strict social distancing policy. Results show that the current efforts of isolation, close contact tracing, and quarantine can take the epidemic curve to the turning point, but the epidemic could be far from over;there were still 4,035 infected people, and 1,584 latent people in the undiscovered part on March 11, 2020, when the epidemic was actually over with a strict social distancing policy.
ABSTRACT
OBJECTIVES: Since January 23, 2020, stringent measures for controlling the novel coronavirus epidemic have been gradually enforced and strengthened in mainland China. The detection and diagnosis have been improved, as well. However, the daily reported cases remaining at a high level make the epidemic trend prediction difficult. METHODS: Since the traditional SEIR model does not evaluate the effectiveness of control strategies, a novel model in line with the current epidemic's process and control measures was proposed, utilizing multisource datasets including the cumulative number of reported, deceased, quarantined and suspected cases. RESULTS: Results show that the trend of the epidemic mainly depends on quarantined and suspected cases. The predicted cumulative numbers of quarantined and suspected cases nearly reached static states, and their inflection points have already been achieved, with the epidemic's peak coming soon. The estimated effective reproduction numbers using model-free and model-based methods are decreasing, as well as new infections, while newly reported cases are increasing. Most infected cases have been quarantined or put in the suspected class, which has been ignored in existing models. CONCLUSIONS: The uncertainty analyses reveal that the epidemic is still uncertain, and it is important to continue enhancing the quarantine and isolation strategy and improving the detection rate in mainland China.