面向智慧城市的共享单车出行时空间特征研究——以广州天河中心区为例

 信息通信技术的快速发展和广泛使用推动了智慧城市的发展,变革了市民的生产和生活方式。如何基于智慧技术,精准识别社会需求,实现供需的匹配,是智慧城市研究的新内容。近年来出现的共享单车作为一种便捷、高效、绿色的新型出行方式,普及度较高,但相关研究成果较少。以天河中央商务区（CBD）为研究范围,基于对研究范围内的共享单车在工作日和休息日的分布和使用情况的分析,重点探究天河北、天河路、珠江新城等商业金融区的单车流动强度和出行的时空间特征。研究发现,在时间分布方面,共享单车在工作日的出行具有早高峰特征;而在休息日,共享单车全天的出行较为平均,高峰使用时段集中在午间和下午,拥有更高的使用效率。在空间分布方面,工作日共享单车的出行集中在广园快速路沿线的交通枢纽和教育科研功能区;而在休息日,单车则散布在外围的地区,出行轨迹主要分布在中部的天河路商圈和南部的珠江新城商圈。同时,单车在商业金融区整体呈现低流动、低使用的特征,而共享单车出行密度较高的区域流动强度依然较低,建议进一步通过构建智能反馈系统对单车的分布情况、流动性和用户出行的情况进行实时监控,为科学、合理的单车调配提供参考,并为共享单车的调配布局提出优化策略。

Spatiotemporal characteristics of bike-sharing: An empirical study of Tianhe District,Guangzhou

With the prevalence of smart city, big data and sharing economy, bike-sharing as a mode of transport to lessen carbon dioxide, promote public health, and solve the problem of the first/last mile connection to other transition modes has been popular in China since 2008. This paper conducts a spatiotemporal analysis of bike-sharing in areas under different land uses, especially the commercial area, in Tianhe district, Guangzhou. The data used in this project consist of over 267 thousand sharing bikes recorded on two weekdays plus one weekend in October and November, 2017. Line tracking and density analysis tools are used in the geographic information system to simulate cycling tracks and carry out a spatial visualization analysis of different time and distance distributions of cycling flows during the day. Results show that, in the time aspect, travel of bike-sharing on weekdays happens mainly in the morning rush hours, while travel at the weekend could be more dispersed. In the space distribution aspect, sharing bikes on weekdays are centrally distributed along the Guangyuan expressway while at weekends, bicycles tend to spread out in the periphery, especially the Tianhe road and Zhujiang New Town trading area. The sharing-bikes in the commercial area are characterized by low turnover rate as well as low usage. The paper suggests that the future allocation of sharing bikes should meet the spatiotemporal characteristics of cycling. The present findings could be useful for bike-sharing enterprises and the authority who want to promote the use of sharing bikes, lower marginal operating costs and further encourage the practice of human-oriented planning that addresses citizens' everyday experience and quality of life.