Usage and Temporal Patterns of Public Bicycle Systems: Comparison among Points of Interest

The public bicycle system is an important component of “mobility as a service” and has become increasingly popular in recent years. To provide a better understanding of the station activity and driving mechanisms of public bicycle systems, the study mainly compares the usage and temporal characteristics of public bicycles in the vicinity of the most common commuting-related points of interest and land use. It applies the peak hour factor, distribution fitting, and K-means clustering analysis on station-based data and performs the public bicycles usage and operation comparison among different points of interest and land use. The following results are acquired: (1) the demand type for universities and hospitals in peaks is return-oriented when that of middle schools is hire-oriented; (2) bike hire and return at metro stations and hospitals are frequent, while only the rental at malls is; (3) compared to middle schools and subway stations with the shortest bike usage duration, malls have the longest duration, valued at 18.08 min; and (4) medical and transportation land, with the most obvious morning return peak and the most concentrated usage in a whole day, respectively, both present a lag relation between bike rental and return. In rental-return similarity, the commercial and office lands present the highest level.

[1]  Ghim Ping Ong,et al.  Estimating Public Bicycle Trip Characteristics with Consideration of Built Environment Data , 2021, Sustainability.

[2]  Lingqian Hu,et al.  Urban Spatial Structure and Travel in China , 2020, Journal of Planning Literature.

[3]  Adrian E. Raftery,et al.  How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis , 1998, Comput. J..

[4]  Philip S. Yu,et al.  Top 10 algorithms in data mining , 2007, Knowledge and Information Systems.

[5]  Yishay Mansour,et al.  An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clustering , 1997, UAI.

[6]  Xingle Long,et al.  Determinants of intention and behavior of low carbon commuting through bicycle-sharing in China , 2019, Journal of Cleaner Production.

[7]  Michael Rabbat,et al.  How Does Land-Use and Urban Form Impact Bicycle Flows--Evidence from the Bicycle-Sharing System (BIXI) in Montreal , 2014 .

[8]  Iraj Mahdavi,et al.  Balancing public bicycle sharing system using inventory critical levels in queuing network , 2020, Comput. Ind. Eng..

[9]  Rick Dale,et al.  Good things peak in pairs: a note on the bimodality coefficient , 2013, Front. Psychol..

[10]  Jun Zhang,et al.  Sustainable bike-sharing systems: characteristics and commonalities across cases in urban China , 2015 .

[11]  Dimitrios V. Vougas,et al.  Lead-lag relationship between futures and spot markets in Greece: 1999 - 2001 , 2007 .

[12]  M. Kamargianni,et al.  Providing quantified evidence to policy makers for promoting bike-sharing in heavily air-polluted cities: A mode choice model and policy simulation for Taiyuan-China , 2018 .

[13]  Naveen Eluru,et al.  Incorporating the impact of spatio-temporal interactions on bicycle sharing system demand: A case study of New York CitiBike system , 2016 .

[14]  Jinn-Tsai Wong,et al.  Exploring Activity Patterns of The Taipei Public Bikesharing System , 2015 .

[15]  Licia Capra,et al.  Measuring the impact of opening the London shared bicycle scheme to casual users , 2012 .

[16]  Yufei Yuan,et al.  A comparison in travel patterns and determinants of user demand between docked and dockless bike-sharing systems using multi-sourced data , 2020 .

[17]  Elise Miller-Hooks,et al.  Large-Scale Vehicle Sharing Systems: Analysis of Vélib' , 2013 .

[18]  Rafael E. Banchs,et al.  Article in Press Pervasive and Mobile Computing ( ) – Pervasive and Mobile Computing Urban Cycles and Mobility Patterns: Exploring and Predicting Trends in a Bicycle-based Public Transport System , 2022 .

[19]  Javier Gutiérrez,et al.  Transit ridership forecasting at station level: an approach based on distance-decay weighted regression , 2011 .

[20]  Yufei Yuan,et al.  Comparison of usage regularity and its determinants between docked and dockless bike-sharing systems: A case study in Nanjing, China , 2020 .

[21]  Philip Chan,et al.  Learning States and Rules for Detecting Anomalies in Time Series , 2005, Applied Intelligence.

[22]  Ying Zhang,et al.  Exploring the impact of built environment factors on the use of public bikes at bike stations: Case study in Zhongshan, China , 2017 .

[23]  Elliot K. Fishman,et al.  Bikeshare: A Review of Recent Literature , 2016 .

[24]  Yongping Zhang,et al.  Environmental benefits of bike sharing: A big data-based analysis , 2018, Applied Energy.

[25]  Alan Bundy,et al.  Dynamic Time Warping , 1984 .

[26]  Wafic El-Assi,et al.  Effects of built environment and weather on bike sharing demand: a station level analysis of commercial bike sharing in Toronto , 2017 .