Whether the Weather Will Help Us Weather the COVID-19 Pandemic: Using Machine Learning to Measure Twitter Users' Perceptions

Objective: The potential ability for weather to affect SARS-CoV-2 transmission has been an area of controversial discussion during the COVID-19 pandemic. Individuals' perceptions of the impact of weather can inform their adherence to public health guidelines; however, there is no measure of their perceptions. We quantified Twitter users' perceptions of the effect of weather and analyzed how they evolved with respect to real-world events and time. Materials and Methods: We collected 166,005 tweets posted between January 23 and June 22, 2020 and employed machine learning/natural language processing techniques to filter for relevant tweets, classify them by the type of effect they claimed, and identify topics of discussion. Results: We identified 28,555 relevant tweets and estimate that 40.4% indicate uncertainty about weather's impact, 33.5% indicate no effect, and 26.1% indicate some effect. We tracked changes in these proportions over time. Topic modeling revealed major latent areas of discussion. Discussion: There is no consensus among the public for weather's potential impact. Earlier months were characterized by tweets that were uncertain of weather's effect or claimed no effect; later, the portion of tweets claiming some effect of weather increased. Tweets claiming no effect of weather comprised the largest class by June. Major topics of discussion included comparisons to influenza's seasonality, President Trump's comments on weather's effect, and social distancing. Conclusion: There is a major gap between scientific evidence and public opinion of weather's impacts on COVID-19. We provide evidence of public's misconceptions and topics of discussion, which can inform public health communications.

Meteorological Impacts on Commercial Aviation Delays and Cancellations in the Continental United States

Weather creates numerous operational and safety hazards within the National Airspace System (NAS). In 2014, extreme weather events attributed 4.3% to the total number of delay minutes recorded by the Bureau of Transportation Statistics. When factoring weather’s impact on the NAS delays and aircraft arriving late delays, weather was responsible for 32.6% of the total number of delay minutes recorded. Hourly surface meteorological aviation routine weather reports (METARs) at major airports can be used to provide valuable insight into the likely causes of weather delays at individual airports. When combined with the Federal Aviation Administration’s (FAA’s) Operations Network (OPSNET) delay data, METARs can be used to identify the major causes of delays and to create delay climatologies for a specific airport. Also, patterns for delays and cancellations for the study period of 2003–15 can be identified for the individual airports included in this study. These patterns can be useful for operators and airport planners to optimize performance in the future.

Fare Design in Urban Transit Network considering Elastic Demand and Adverse Weather’s Impact

Due to the disturbance of unexpected effects and adverse weather conditions, transit supply and demand manifests many uncertainties. In this paper, we take account of these uncertainties and propose a transit fare structure design model including both ground and underground public transportation. Such transit fare design problem is described through bilevel programming, in which the upper level is the transportation authority’s transit fare structure decision aiming to minimize the transit network’s total travel and operation cost, while the lower level is a transit network assignment model considering supply and demand uncertainties that influence passengers’ travel choice decisions. A heuristic algorithm is developed to solve the problem, and a numerical example is presented to illustrate the application. We get some important results: (1) a diversified fare structure considering uncertain weather’s impact is quite necessary; (2) when the value of time is at a high level, metro fare should be higher than bus fare; (3) the optimal metro and bus fare should be close under an extremely adverse weather condition; (4) fare structure could be quite different with varied value of time.