Impacts of COVID-19 Pandemic Crisis in the Transportation Sector: A Classification Analysis in Regard with Preferred Modes of Transportation Using Random Forest Algorithm
2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2021
; 2021.
Article
in English
| Scopus | ID: covidwho-1788678
ABSTRACT
The study observes the Pandemic Crisis (Covid 19) that resulted in impacts on the Transportation category in the area National Capital Region. Public transportation is an important aspect of human's ability to travel to different places whether its personal or business purpose, it's a part of life that people take for granted and can't be taken away easily. But due to the pandemic era, people have been careful in their choices, which resulted in the change standard when it comes to public transportation choices. With that said, to understand and observe these impacts, a scenario must be made such as before and after the pandemic designed as an environment for the study to take root. The study has used machine learning called Random Forest Algorithm with the used several parameters to create a prediction model. As for the method in gathering data, a survey of Google Form is utilized to gather 200 participants of the National Capital Region with varying parameters for their choice of public transportation. The machine algorithm has shown satisfactory accuracy of 89.88% and 88.88%. As an important note, it is observed that travel expense has more impact on public transportation choices than other parameters. The Random Forest Algorithm has been utilized in creating classification types of models and can help future researchers improve the machine learning approach. © 2021 IEEE.
Confusion Matrix; Mode of Transportation; Prediction Model; Random Forest; Spyder; Learning algorithms; Machine learning; Surveys; Classification analysis; National capital regions; Prediction modelling; Public transportation; Random forest algorithm; Random forests; Transportation sector; Decision trees
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Experimental Studies
/
Randomized controlled trials
Language:
English
Journal:
2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2021
Year:
2021
Document Type:
Article
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