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|Other Titles: ||Operations assessment of the Taipei bike sharing system|
|Authors: ||黃晏珊;Huang, Yen-Shan|
|Keywords: ||公共自行車;自行車共享;大數據分析;時空模型;營運特性;Public Bicycle System;Bike Sharing;Big Data Analysis;Temporal-Spatial Model;Operational Assessment|
|Issue Date: ||2016-01-22 14:56:42 (UTC+8)|
|Abstract: ||因應能源短缺、全球暖化，各城市將自行車公共化而成為自行車共享(Bike Sharing)系統，臺灣各地也陸續推動，其中臺北YouBike以每輛周轉率12次/日居全球之冠。隨著系統服務範圍擴大及使用量增加，甲租乙還的時段性不平衡導致無車可租與無位可還，成為公共自行車營運與調度的核心要素，然而以往較缺乏以大規模缺車與缺位資訊為基礎的探討，故本研究以臺北市YouBike為對象，利用連續六個月、每隔五分鐘擷取的自行車租賃站即時可借車輛與可停車位數據(8,080,128筆)，以統計分析方法進行時間與空間特性的系統分析，除了嘗試建立多元營運評估指標以彌補現有不足外，同時依據租賃站規模與地域特性，檢視現行租賃站設置原則，提供未來設站及既有站點規模調整之參考，並提出相關營運策略建議。|
In response to energy shortage and global warming, public bikes have recently been gaining global popularity, including Taiwan. Among the cities with public bike systems, Taipei’s YouBike tops the world in terms of an average daily turnover rate of 12 times per bike. Meanwhile, as more and more users and stations involved in the system, YouBike stations confront such issues as few bikes or few parking slots available due to imbalanced rental demand - a headache of public bike operations and dispatch. Distinct from previous studies, this research collected big data on YouBike with a time span of 6 months for every 5 minutes, or a total data size of 8,080,128. We statistically modeled the spatial and temporal characteristics with respect to various performance indicators. In addition, we examined the exiting disposition criteria for new YouBike stations, and proposed supplementary criteria on the aspects of station scales and locations.
The findings of this study include:
1.For the 7 proposed indicators, the Taipei YouBike system has the following performances: (1) risk of insufficient bikes as 26%; (2) risk of insufficient parking spaces as 1%; (3) reliability as 73%; (4) average waiting time for bikes as 57 minutes and that for parking spaces as 21 minutes; (5) fluctuation rate as 28%; (6) resiliency as 14%; and (7) utility rate as 58%.
2.The current station disposition criteria exclude building new stations within 600 meters of any existing rental station. However, this study shows that some stations suffered from greater risks of insufficient bikes and spaces; in such a case, new stations could (and should) be considered as an exemption from the 600-meter criterion.
3.The temporal characteristics of YouBike include: (1) the number of users increased from 7:00 on weekdays, of which the peak occurred during the commuting hours; for weekends, the number of users increased from 9:00 and reached its top in the afternoon. (2) There were more bike users on weekends than on weekdays. (3) The risk of insufficient bikes or parking spaces was lower in the morning than that in the evening. (4) The fluctuation was small in the morning, but became large in the late evening.
4.The spatial characteristics of YouBike include: (1) The stations near transit facilities, schools, and commercial and recreational areas had greater risk of insufficient bikes; (2) The stations near schools, and commercial and recreational areas had greater risk of insufficient parking spaces; (3) Regardless of the station size, those around downtown and transit facilities tended to have insufficient bikes and parking spaces, resulting in greater fluctuation rates.
5.It is suggested that: (1) The bike operator should adopt different dispatch strategies regarding the peak and off peak periods, and weekdays and weekends. (2) More attention should be paid on the peak periods and downtown stations with greater fluctuation and waiting time. (3) The operator should provide incentives and develop an autonomy dispatch mechanism for stations with greater risk of insufficient bikes and/or parking spaces. (4) “differential pricing” should be considered as the weekday utility rates were different from the weekends.
|Appears in Collections:||[運輸管理學系暨研究所] 學位論文|
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