Abstract
Traditional dock-based public bicycle systems continue to dominate cycling in most cities, even though bicycle-sharing services are an increasingly popular means of transportation in many of China’s large cities. A few studies investigated the traditional public bicycle systems in small and mid-sized cities in China. The time series clustering method’s advantages for analyzing sequential data used in many transportation-related studies are restricted to time series data, thereby limiting applications to transportation planning. This study explores the characteristics of a typical third-tier city’s public bicycle system (where there is no bicycle-sharing service) using station classification via the time series cluster algorithm and bicycle use data. A dynamic time warping distance-based k-medoids method classifies public bicycle stations by using one-month bicycle use data. The method is further extended to non-time series data after format conversion. The paper identified three clusters of stations and analyzed the relationships between clusters’ features and the stations’ urban environments. Based on points-of-interest data, the classification results were validated using the enrichment factor and the proportional factor. The method developed in this paper can apply to other transportation analysis and the results also yielded relevant strategies for transportation development and planning.
Highlights
As a type of sustainable urban transportation system [1,2,3,4], public bicycle systems worldwide totaled nearly 1200 in 2016 [5]
By analyzing POI data, we fully explored the effects of the stations’ surrounding environments on the classification results
The enrichment factor was normalized to eliminate the effects of the imthbeaelfafnectesdoPf tOheI simizbeaslancrcoesdsPcOatIesgizoersieasc.roInsspcaartteigcourliaers,.aInFpi,javrtaicluelaor,faoFni,ej vsaulugegeosftosntehsaut gthgesetsntrhicaht ment factotrhoefetnhreicPhmOeInint ftahcteojrtohfctahteePgOorIyinetqhueajltsh tchaetergeogryioenqaulaalsvtehreagreeg, iwonhaelraevaesraFgi,ej,>w1heorreaFsi,jF11moeraFni,js that the P
Summary
As a type of sustainable urban transportation system [1,2,3,4], public bicycle systems worldwide totaled nearly 1200 in 2016 [5]. Since 2008, China has implemented traditional dock-based public bicycle systems by implementing a pattern of government buying services in Hangzhou, Shanghai, Beijing, Guangzhou, and Shenzhen. Bicycle-sharing services have become an important part of urban transportation in China’s megacities, the traditional public bicycle system is the main service in most small and mid-sized cities [6]. Mid-sized, and large cities in China only have the government-provided public bicycle system; the popular dockless bicycle-sharing services such as Ofo and Mobike, are focused on megacity markets. In order to use the bicycle-sharing services and the public bicycle systems more efficiently and effectively, we must first mine data related to bicycles to identify where the various types of bicycles are parked in the cities [7]
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