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| Title: | 臺北市公共自行車系統升級後之使用特性與站點網絡效應分析
Analysis of Usage Characteristics and Station Network Effects after the Upgrade of Public Bike System in Taipei City |
| Authors: | 邱華奕
Chiu, Hua-Yi |
| Contributors: | 白仁德
甯方璽
Pai, Jen-Te
Ning, Fang-Shii
邱華奕
Chiu, Hua-Yi |
| Keywords: | 公共自行車使用特性
第四代公共自行車系統
站點網絡效應
多尺度地理加權迴歸
Public Bike System Usage Characteristics
4th Generation Public Bike System
Station Network Effects
Multiscale Geographically Weighted Regression |
| Date: | 2024 |
| Issue Date: | 2024-08-05 14:14:57 (UTC+8) |
| Abstract: | 由於氣候變遷、能源供應不穩、環境品質下降等情形日趨嚴峻，永續交通理念漸受重視，包含公共自行車等主動運輸方式有助減少汙染、碳排放及能源消耗，並滿足第一哩路與最後一哩路需求，因此成為受廣泛應用之運輸方式。公共自行車系統已經歷四世代發展，臺灣公共自行車系統亦已由第三代升級至第四代，增加站點密度、服務範圍、便利性隨技術提升而成為可能，同時使用者偏好、使用特性皆可能隨之改變。站點設置密度為系統升級前後最主要之差異，可能造成站點網絡效應改變，並影響公共自行車使用特性。然而現有研究甚少關注第四代公共自行車系統之站點網絡效應，及臺灣第四代公共自行車系統之使用特性，故本研究以臺北市YouBike 2.0系統為例，對公共自行車使用特性及站點網絡效應之時空分布進行探討。
本研究使用臺北市YouBike 2.0系統2022年10月至2023年9月租借紀錄與站點資料，進行敘述性統計、探索式空間資料分析，並依序以普通最小平方法、多尺度地理加權迴歸方式建立公共自行車使用特性模型，呈現並分析YouBike 2.0系統日均騎乘量時空分布，以及站點網絡效應與其他變數對騎乘量之影響。部分項目納入升級前YouBike 1.0系統2019年資料進行對比，以突顯升級後系統特性。
統計及空間分析結果顯示，YouBike 2.0系統平日騎乘比例較升級前系統高、單次租借時間較短，騎乘量則隨站點數量增加、設置密度提升而上升，同時騎乘量、熱門站點及旅次之空間分布雖仍集中，但較升級前系統分散。普通最小平方法模型結果顯示，站點網絡效應對單一站點騎乘量之影響在小範圍內為負面，大範圍內為正面；大眾運輸站點、特定土地使用、自行車道及站點容量變數普遍對騎乘量產生正面影響。多尺度地理加權迴歸模型結果則顯示，各項變數對騎乘量之影響於納入空間因素後更貼近實際情形，兩模型中多數變數對騎乘量影響方向相近；大眾運輸站點、站點容量、小範圍內站點網絡效應變數，對騎乘量之影響具空間異質性；土地使用、大範圍內站點網絡效應變數空間同質性較高。
Due to the increasing severity of issues such as climate change, unstable energy supply, and declining environmental quality, the concept of sustainable transportation is gaining more attention. Active mobility, such as public bike, help reduce pollution, carbon emissions, and energy consumption while meeting the needs of the "first mile" and "last mile" of travel. Consequently, these methods have become widely used modes of transportation. The public bike system has evolved through 4 generations, and Taiwan's public bike system has upgraded from the 3rd generation to the 4th. This upgrade has increased station density, service range, and convenience with technological advancements, potentially changing user preferences and usage characteristics. The primary difference between pre- and post-upgrade systems is the station density, which may alter the Station network effects and impact the usage characteristics for public bike. However, existing studies rarely focus on the Station network effects and usage characteristics of the 4th generation public bike system in Taiwan. Therefore, this study uses YouBike 2.0 system in Taipei as a case to explore the spatial and temporal distribution of public bike usage characteristics and Station network effects.
This study utilizes the rental records and station data of YouBike 2.0 system in Taipei from October 2022 to September 2023, performing descriptive statistics, exploratory spatial data analysis, and sequentially establishing a public bike usage characteristics model using Ordinary Least Squares (OLS) and Multiscale Geographically Weighted Regression (MGWR). The study presents and analyzes the spatial and temporal distribution of YouBike 2.0 ridership, as well as the impact of Station network effects and other variables on demand. Some items incorporate data from YouBike 1.0 system in 2019 for comparison to highlight the characteristics of the upgraded system.
The statistical and spatial analysis results indicate that YouBike 2.0 system has a higher proportion of weekday rides, shorter single rental durations, and increased ridership with more stations and higher density compared to the previous system. Although the ridership, popular stations, and popular trip distributions are still concentrated, they are more dispersed than in the previous system. The OLS model results show that the impact of Station network effects on single station ridership is negative in the small-scale but positive in the huge-scale. Public transport stations, specific land uses, bike lanes, and station capacity variables generally positively impact ridership. The MGWR model results indicate that the influence of various variables on ridership aligns more closely with the actual situation when spatial factors are included. Most variables in both models have similar impacts on ridership. Public transport stations, station capacity, and small-scale Station network effects exhibit spatial heterogeneity, while land use and huge-scale Station network effects have high spatial homogeneity. |
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| Description: | 碩士
國立政治大學
地政學系
111257003 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0111257003 |
| Data Type: | thesis |
| Appears in Collections: | [地政學系] 學位論文
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| 700301.pdf | | 11600Kb | Adobe PDF | 1 | View/Open |
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