Semantic Scholar Open Access 2023 3 sitasi

Predicting Operating Train Delays into New York City using Random Forest Regression and XGBoost Regres-sion Models

T. Wiese

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Abstrak

The Long Island Railroad operates one of the largest commuter rail networks in the U.S.[1]. This study uses data which includes the location and arrival time of trains based on onboard GPS position and other internal sources. This paper analyzes the GPS position of the train to gain insight into potential gaps in on time performance and train operations. This was done by developing a Random Forest Re-gression model [2] and an XGBoost regression model [3[. Both models prove to be useful to make such predictions and should be used to help railroads to prepare and adjust their operations.

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T. Wiese

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Wiese, T. (2023). Predicting Operating Train Delays into New York City using Random Forest Regression and XGBoost Regres-sion Models. https://doi.org/10.22161/ijebm.7.1.5

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Lihat di Sumber doi.org/10.22161/ijebm.7.1.5

Informasi Jurnal

Tahun Terbit: 2023
Bahasa: en
Total Sitasi: 3×
Sumber Database: Semantic Scholar
DOI: 10.22161/ijebm.7.1.5
Akses: Open Access ✓