Understanding the Operational Efficiency of Bicycle-Sharing Based on the Influencing Factor Analyses: A Case Study in Nanjing, China
With the expansion of urban scale and the growth of urban population, the bicycle-sharing system has been greatly helping grease the wheels of convenience and diversity of citizens' travel. Nevertheless, there are a set of additional problems, including imbalance of supply and demand at rental...
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doaj-6835773ddda24ec78b276cd13f29af3f2021-02-15T12:52:41ZengHindawi-WileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/88185488818548Understanding the Operational Efficiency of Bicycle-Sharing Based on the Influencing Factor Analyses: A Case Study in Nanjing, ChinaBeibei Hu0Yunfeng Gao1Jiechen Yan2Yue Sun3Yang Ding4Ji Bian5Xianlei Dong6Huijun Sun7School of Business, Shandong Normal University, Jinan, ChinaSchool of Business, Shandong Normal University, Jinan, ChinaSchool of Business, Shandong Normal University, Jinan, ChinaSchool of Business, Shandong Normal University, Jinan, ChinaSchool of Business, Shandong Normal University, Jinan, ChinaSchool of Business, Shandong Normal University, Jinan, ChinaSchool of Business, Shandong Normal University, Jinan, ChinaKey Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong University, Beijing, ChinaWith the expansion of urban scale and the growth of urban population, the bicycle-sharing system has been greatly helping grease the wheels of convenience and diversity of citizens' travel. Nevertheless, there are a set of additional problems, including imbalance of supply and demand at rental stations and low utilization of system operation, which have disrupted the travel experience of consumers, the profitability of businesses, and the coordination of government. In this study, we take Nanjing as an example to measure the operating efficiency of bicycle-sharing by calculating the capacity utilization rate (CUR). Afterwards, based on the IC card data of bicycle-sharing users, we statistically analyzed the traffic inflow and outflow at rental stations. Besides, this paper discusses the factors influencing the use of bicycle-sharing, by introducing the method of sampling stepwise regression into the study of rental situation and geographical environment. The results are as follows: (1) demand for bicycle-sharing is higher on weekdays than on weekends, especially during the morning and evening rush hours. (2) The daily average capacity utilization rate of bicycle-sharing is less than 0.08, indicating that the system is not efficient enough. During morning and evening rush hours, only less than 10% of rental stations have high inflow and outflow, and there is an imbalance of inflow and outflow for the same rental station at different times of the day. (3) The stepwise regression results show that the inflow and outflow of bicycle-sharing rental stations are mainly affected by the distribution of traffic, education, entertainment, medical, and other functional zones near the stations. These findings could provide relevant government departments and enterprises with strategies and suggestions for the efficient and healthy operation of the urban bicycle-sharing system.http://dx.doi.org/10.1155/2021/8818548 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Beibei Hu Yunfeng Gao Jiechen Yan Yue Sun Yang Ding Ji Bian Xianlei Dong Huijun Sun |
spellingShingle |
Beibei Hu Yunfeng Gao Jiechen Yan Yue Sun Yang Ding Ji Bian Xianlei Dong Huijun Sun Understanding the Operational Efficiency of Bicycle-Sharing Based on the Influencing Factor Analyses: A Case Study in Nanjing, China Journal of Advanced Transportation |
author_facet |
Beibei Hu Yunfeng Gao Jiechen Yan Yue Sun Yang Ding Ji Bian Xianlei Dong Huijun Sun |
author_sort |
Beibei Hu |
title |
Understanding the Operational Efficiency of Bicycle-Sharing Based on the Influencing Factor Analyses: A Case Study in Nanjing, China |
title_short |
Understanding the Operational Efficiency of Bicycle-Sharing Based on the Influencing Factor Analyses: A Case Study in Nanjing, China |
title_full |
Understanding the Operational Efficiency of Bicycle-Sharing Based on the Influencing Factor Analyses: A Case Study in Nanjing, China |
title_fullStr |
Understanding the Operational Efficiency of Bicycle-Sharing Based on the Influencing Factor Analyses: A Case Study in Nanjing, China |
title_full_unstemmed |
Understanding the Operational Efficiency of Bicycle-Sharing Based on the Influencing Factor Analyses: A Case Study in Nanjing, China |
title_sort |
understanding the operational efficiency of bicycle-sharing based on the influencing factor analyses: a case study in nanjing, china |
publisher |
Hindawi-Wiley |
series |
Journal of Advanced Transportation |
issn |
0197-6729 2042-3195 |
publishDate |
2021-01-01 |
description |
With the expansion of urban scale and the growth of urban population, the bicycle-sharing system has been greatly helping grease the wheels of convenience and diversity of citizens' travel. Nevertheless, there are a set of additional problems, including imbalance of supply and demand at rental stations and low utilization of system operation, which have disrupted the travel experience of consumers, the profitability of businesses, and the coordination of government. In this study, we take Nanjing as an example to measure the operating efficiency of bicycle-sharing by calculating the capacity utilization rate (CUR). Afterwards, based on the IC card data of bicycle-sharing users, we statistically analyzed the traffic inflow and outflow at rental stations. Besides, this paper discusses the factors influencing the use of bicycle-sharing, by introducing the method of sampling stepwise regression into the study of rental situation and geographical environment. The results are as follows: (1) demand for bicycle-sharing is higher on weekdays than on weekends, especially during the morning and evening rush hours. (2) The daily average capacity utilization rate of bicycle-sharing is less than 0.08, indicating that the system is not efficient enough. During morning and evening rush hours, only less than 10% of rental stations have high inflow and outflow, and there is an imbalance of inflow and outflow for the same rental station at different times of the day. (3) The stepwise regression results show that the inflow and outflow of bicycle-sharing rental stations are mainly affected by the distribution of traffic, education, entertainment, medical, and other functional zones near the stations. These findings could provide relevant government departments and enterprises with strategies and suggestions for the efficient and healthy operation of the urban bicycle-sharing system. |
url |
http://dx.doi.org/10.1155/2021/8818548 |
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