@article{ART003234190},
author={Min Kyu Jeong and Ju Yeon Park and Young-Tae Kwak},
title={Development of City Bus Passenger Prediction System Using Function Optimization},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2025},
volume={30},
number={8},
pages={65-73}
TY - JOUR
AU - Min Kyu Jeong
AU - Ju Yeon Park
AU - Young-Tae Kwak
TI - Development of City Bus Passenger Prediction System Using Function Optimization
JO - Journal of The Korea Society of Computer and Information
PY - 2025
VL - 30
IS - 8
PB - The Korean Society Of Computer And Information
SP - 65
EP - 73
SN - 1598-849X
AB - This study proposes a bus passenger prediction system that utilizes transportation card big data and reboarding passenger counts to overcome the limitations of missing alighting data in local cities.
Focusing on three major routes in Jeonju (101, 165, and 970), the study applies and compares three machine learning algorithms (Random Forest, XGBoost, and LightGBM) while analyzing model performance by day of the week and time of day. April 2025 data was used for training and May 2025 for testing. Derived variables such as weekday/weekend indicators were created, and categorical features were label-encoded to fit the models. Performance was evaluated using RMSE, and LightGBM consistently showed the most stable and accurate results. The analysis revealed that prediction accuracy was higher on weekdays (Mon-Fri), whereas weekends (Sat-Sun) showed increased errors across all routes. By time of day, predictions were most accurate during morning commuting hours (06:00-08:00) and showed the largest errors during evening rush hours (17:00-19:00). The study demonstrates the feasibility of predicting demand even without alighting data and highlights the practicality and efficiency of the proposed system. The results can support transportation policy applications such as dynamic scheduling, route optimization, and efficient public resource allocation.
KW - City Bus;Prediction of Boarding Passengers;Transportation Card Big Data;Random Forest;XGBoost;LightGBM
DO -
UR -
ER -
Min Kyu Jeong, Ju Yeon Park and Young-Tae Kwak. (2025). Development of City Bus Passenger Prediction System Using Function Optimization. Journal of The Korea Society of Computer and Information, 30(8), 65-73.
Min Kyu Jeong, Ju Yeon Park and Young-Tae Kwak. 2025, "Development of City Bus Passenger Prediction System Using Function Optimization", Journal of The Korea Society of Computer and Information, vol.30, no.8 pp.65-73.
Min Kyu Jeong, Ju Yeon Park, Young-Tae Kwak "Development of City Bus Passenger Prediction System Using Function Optimization" Journal of The Korea Society of Computer and Information 30.8 pp.65-73 (2025) : 65.
Min Kyu Jeong, Ju Yeon Park, Young-Tae Kwak. Development of City Bus Passenger Prediction System Using Function Optimization. 2025; 30(8), 65-73.
Min Kyu Jeong, Ju Yeon Park and Young-Tae Kwak. "Development of City Bus Passenger Prediction System Using Function Optimization" Journal of The Korea Society of Computer and Information 30, no.8 (2025) : 65-73.
Min Kyu Jeong; Ju Yeon Park; Young-Tae Kwak. Development of City Bus Passenger Prediction System Using Function Optimization. Journal of The Korea Society of Computer and Information, 30(8), 65-73.
Min Kyu Jeong; Ju Yeon Park; Young-Tae Kwak. Development of City Bus Passenger Prediction System Using Function Optimization. Journal of The Korea Society of Computer and Information. 2025; 30(8) 65-73.
Min Kyu Jeong, Ju Yeon Park, Young-Tae Kwak. Development of City Bus Passenger Prediction System Using Function Optimization. 2025; 30(8), 65-73.
Min Kyu Jeong, Ju Yeon Park and Young-Tae Kwak. "Development of City Bus Passenger Prediction System Using Function Optimization" Journal of The Korea Society of Computer and Information 30, no.8 (2025) : 65-73.
