A study on predictive models for public bicycle usage in Daejeon†
Jeongyeon Park1 · Jihwan Park2 · Sangin Lee3
12Department of Statistics and Data Science, Chungnam National University,
3Department of Information and Statistics, Chungnam National University
3Department of Information and Statistics, Chungnam National University
Correspondence to: † This research was supported by the National Research Foundation (NRF) grant funded by the Korea government (NRF-2022M3J6A1084843).
1 Master student, Department of Statistics and Data Science, Chungnam National University, Daejeon 31434, Korea.
2 Master student, Department of Statistics and Data Science, Chungnam National University, Daejeon 31434, Korea.
3 Associate professor, Department of Information and Statistics, Chungnam National University, Daejeon 31434, Korea. E-mail:sanginlee44@gmail.com
1 Master student, Department of Statistics and Data Science, Chungnam National University, Daejeon 31434, Korea.
2 Master student, Department of Statistics and Data Science, Chungnam National University, Daejeon 31434, Korea.
3 Associate professor, Department of Information and Statistics, Chungnam National University, Daejeon 31434, Korea. E-mail:sanginlee44@gmail.com
Received August 1, 2024; Revised August 29, 2024; Accepted September 3, 2024.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
- Abstract
- This paper investigates the current usage patterns and characteristics of the public bicycle system ”Tashu” in Daejeon, South Korea, and proposes a model for predicting demand at individual rental stations. With the increasing popularity of competitive shared personal mobility services such as electric scooters and electric bicycles, there is a need to improve the operation of Daejeon’s bicycle-sharing system. Currently, all Tashu bicycles are maintained at the Tashu Center operated by the Daejeon Metropolitan Express Transit Corporation, leading to inconveniences and issues such as increased maintenance due to long distances between the center and rental stations. To address these challenges, this study analyzes the usage patterns of Tashu bicycles and identifies cluster-specific characteristics based on spatial and temporal patterns. Additionally, a model for predicting rental counts is developed using linear regression, negative binomial regression, and mixed-effects models, incorporating both station-specific and time-specific features. Through these analyses, the paper aims to explore ways to enhance the efficiency of Daejeon’s public bicycle service and provide better services to users.
- Keywords : Linear mixed model, linear regression model, negative binomial regression model, public bicycle
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