Analysis of Determining Factors of Urban Vitality with Mobile Phone Location-Based Origin-Destination Bigdata by Travel Purpose : Using the PageRank Algorithm and SHAP Machine Learning

Park, Junsang; Kim, Sunjae; Lee, Sugie

doi:10.17208/jkpa.2022.10.57.5.72

Cited by 7 publications

(1 citation statement)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To extract the significance of air passenger demand using SHAP analysis [23,24], air passengers were designated as input variables to the LightGBM ensemble model [25]. In addition, a research model for measuring performance was configured with the stock price, exchange rate, and socioeconomic variables set as learning data.…”

Section: Methodsmentioning

confidence: 99%

Determining Factors Influencing Short-Term International Aviation Traffic Demand Using SHAP Analysis: Before COVID-19 and Now

Song,

Choi,

Elkosantini

et al. 2023

Sustainability

View full text Add to dashboard Cite

Due to the COVID-19 outbreak, international aviation travel has declined globally to the level it was 30 years ago. Influencing factors are explored to understand the difference in short-term international aviation travel demand before and after the COVID-19 pandemic. SHapley Additive exPlanations (SHAP), an exploratory data analysis methodology, is applied to identify the factors affecting aviation demand. Daily international aviation passenger volume data (1462 in total) between 2018 and 2021 are analyzed with 10 socioeconomic variables and the number of daily confirmed COVID-19 cases in Korea. It was found that the number of confirmed cases did not have the greatest direct influence on the short-term demand for international demand, but it has a strong correlation with socioeconomic factors. This study’s findings on the factors influencing short-term international air passenger demand from a macro perspective will contribute to demand forecasting after COVID-19. It is expected that this research can be applied to other countries or other pandemic data to investigate the post-pandemic demand changes.

show abstract

Section: Methodsmentioning

confidence: 99%