A graded cluster system to mine virtual stations in free-floating bike-sharing system on multi-scale geographic view

Abstract Bike sharing is one of the means of green travel. On the one hand, sharing bicycles facilitates people’s travel and enriches the way of travel. On the other hand, sharing bicycles improves people’s awareness and sense of responsibility for energy saving and emission reduction, which in turn reflects the meaning of green travel. In order to support people using shared bicycles for green travel, it is very important to optimize the configuration of bike-sharing system. The increasing amounts of free-floating bicycles give a serious challenge in parking planning. Dispatching random parking bicycles will increase vehicles workload and offset emission reduction effect of the bicycles. At this paper we discuss about the virtual stations in a free-floating bike-sharing system. This method is the focus of efficient and environmentally friendly dispatching, which enables the bike-sharing company to achieve cleaner production. We introduce the modifiable areal unit problem and propose a new method called the graded cluster system to mine virtual stations on a multiscale geographic view. The new method can generate more virtual stations more evenly. This not only makes it more convenient for people to use shared bicycles for green travel, but also dense virtual stations can make shared bicycles more visible in people’s sight, strengthening people’s awareness of green travel.

[1]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[2]  Eren Özceylan,et al.  A GIS-based MCDM approach for the evaluation of bike-share stations , 2018, Journal of Cleaner Production.

[3]  W. Y. Szeto,et al.  A hybrid large neighborhood search for the static multi-vehicle bike-repositioning problem , 2017 .

[4]  B. Giles-Corti,et al.  The relationship between destination proximity, destination mix and physical activity behaviors. , 2008, Preventive medicine.

[5]  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 .

[6]  Gilbert Laporte,et al.  The static bicycle relocation problem with demand intervals , 2014, Eur. J. Oper. Res..

[7]  Kyoungok Kim,et al.  Investigation on the effects of weather and calendar events on bike-sharing according to the trip patterns of bike rentals of stations , 2018 .

[8]  Tien Dung Tran,et al.  Modeling Bike Sharing System using Built Environment Factors , 2015 .

[9]  K. Krizek Operationalizing Neighborhood Accessibility for Land Use-Travel Behavior Research and Regional Modeling , 2003 .

[10]  Alan T. Murray,et al.  Excess Commuting and the Modifiable Areal Unit Problem , 2002 .

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

[12]  Federico Chiariotti,et al.  A Dynamic Approach to Rebalancing Bike-Sharing Systems , 2018, Sensors.

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

[14]  Darren M. Scott,et al.  Understanding bike share cyclist route choice using GPS data: Comparing dominant routes and shortest paths , 2018, Journal of Transport Geography.

[15]  W. Y. Szeto,et al.  Chemical reaction optimization for solving a static bike repositioning problem , 2016 .

[16]  Liang Zhao,et al.  Data clustering using controlled consensus in complex networks , 2013, Neurocomputing.

[17]  Satish V. Ukkusuri,et al.  An Algorithm for the One Commodity Pickup and Delivery Traveling Salesman Problem with Restricted Depot , 2016 .

[18]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[19]  W. Y. Szeto,et al.  The rebalancing of bike-sharing system under flow-type task window , 2020 .

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

[21]  Vipin Kumar,et al.  Chameleon: Hierarchical Clustering Using Dynamic Modeling , 1999, Computer.

[22]  W. Y. Szeto,et al.  A modeling framework for the dynamic management of free-floating bike-sharing systems , 2018 .

[23]  T. C. Chou,et al.  A Geo-Aware and VRP-Based Public Bicycle Redistribution System , 2012 .

[24]  Alexis J. Comber,et al.  A spatiotemporal and graph-based analysis of dockless bike sharing patterns to understand urban flows over the last mile , 2019, Comput. Environ. Urban Syst..

[25]  Guangdong Wu,et al.  Critical Factors to Achieve Dockless Bike-Sharing Sustainability in China: A Stakeholder-Oriented Network Perspective , 2018, Sustainability.

[26]  David Manley,et al.  Scales, levels and processes: Studying spatial patterns of British census variables , 2006, Comput. Environ. Urban Syst..

[27]  Brian W. Baetz,et al.  Using GIS for Evaluation of Neighborhood Pedestrian Accessibility , 1997 .

[28]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[29]  Ming Zhang,et al.  Metrics of Urban Form and the Modifiable Areal Unit Problem , 2005 .

[30]  Wei Chen,et al.  A deep learning approach on short-term spatiotemporal distribution forecasting of dockless bike-sharing system , 2018, Neural Computing and Applications.

[31]  Robert C. Hampshire,et al.  Inventory rebalancing and vehicle routing in bike sharing systems , 2017, Eur. J. Oper. Res..

[32]  W. Y. Szeto,et al.  A multiple type bike repositioning problem , 2016 .

[33]  Farhad Atash,et al.  Redesigning Suburbia for Walking and Transit: Emerging Concepts , 1994 .

[34]  Karim Labadi,et al.  A branch-and-bound algorithm for solving the static rebalancing problem in bicycle-sharing systems , 2016, Comput. Ind. Eng..

[35]  Iris A. Forma,et al.  A 3-step math heuristic for the static repositioning problem in bike-sharing systems , 2015 .

[36]  Gulsah Akar,et al.  Bike sharing differences among Millennials, Gen Xers, and Baby Boomers: Lessons learnt from New York City’s bike share , 2018, Transportation Research Part A: Policy and Practice.

[37]  J. Jiao,et al.  Promoting public bike-sharing: A lesson from the unsuccessful Pronto system. , 2018, Transportation research. Part D, Transport and environment.

[38]  P. Midgley The role of smart bike-sharing systems in urban mobility , 2009 .

[39]  Jennifer L. Dungan,et al.  A balanced view of scale in spatial statistical analysis , 2002 .

[40]  Xiaoming Yuan,et al.  A proximal point algorithm revisit on the alternating direction method of multipliers , 2013 .

[41]  Francesco Pinna,et al.  Cagliari and smart urban mobility: Analysis and comparison , 2016 .

[42]  Peng-Sheng You,et al.  A two-phase heuristic approach to the bike repositioning problem , 2019, Applied Mathematical Modelling.

[43]  Joseph Warrington,et al.  Two-stage stochastic approximation for dynamic rebalancing of shared mobility systems , 2018, Transportation Research Part C: Emerging Technologies.

[44]  Christine M. Hoehner,et al.  Perceived and objective environmental measures and physical activity among urban adults. , 2005, American journal of preventive medicine.

[45]  F. Bull,et al.  Developing a framework for assessment of the environmental determinants of walking and cycling. , 2003, Social science & medicine.

[46]  Zihan Hong,et al.  Hybrid cluster-regression approach to model bikeshare station usage , 2017, Transportation Research Part A: Policy and Practice.

[47]  H. Stanley,et al.  Robustness of network of networks under targeted attack. , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[48]  Rosa Maria Dangelico,et al.  “Green Marketing”: An analysis of definitions, strategy steps, and tools through a systematic review of the literature , 2017 .

[49]  Henry Y. K. Lau,et al.  A time-space network flow approach to dynamic repositioning in bicycle sharing systems , 2017 .

[50]  Frédéric Meunier,et al.  Bike sharing systems: Solving the static rebalancing problem , 2013, Discret. Optim..

[51]  J. MacQueen Some methods for classification and analysis of multivariate observations , 1967 .

[52]  Lixin Tian,et al.  Resilience of networks with community structure behaves as if under an external field , 2018, Proceedings of the National Academy of Sciences.

[53]  Ezgi Eren,et al.  A review on bike-sharing: The factors affecting bike-sharing demand , 2020 .

[54]  Jun Hu,et al.  A strategic repositioning algorithm for bicycle-sharing schemes , 2014 .

[55]  C. E. Gehlke,et al.  Certain Effects of Grouping upon the Size of the Correlation Coefficient in Census Tract Material , 1934 .

[56]  Weihong Guo,et al.  Incentive measures to avoid the illegal parking of dockless shared bikes: the relationships among incentive forms, intensity and policy compliance , 2020 .

[57]  Jan Brinkmann,et al.  Dynamic Lookahead Policies for Stochastic-Dynamic Inventory Routing in Bike Sharing Systems , 2019, Comput. Oper. Res..

[58]  S. Washington,et al.  Barriers to bikesharing : an analysis from Melbourne and Brisbane , 2014 .

[59]  Robert B. Noland,et al.  Bikeshare Trip Generation in New York City , 2016 .

[60]  Jennifer Dill,et al.  Factors Affecting Bicycling Demand , 2007 .

[61]  Xiaohu Zhang,et al.  Understanding the usage of dockless bike sharing in Singapore , 2018 .

[62]  Santo Fortunato,et al.  Community detection in networks: A user guide , 2016, ArXiv.

[63]  Boniphace Kutela,et al.  Towards a Better Understanding of Effectiveness of Bike-share Programs: Exploring Factors Affecting Bikes Idle Duration , 2017 .

[64]  Ritsuko Ozaki,et al.  Pro‐environmental products: marketing influence on consumer purchase decision , 2008 .

[65]  Alexander Zipf,et al.  A local scale-sensitive indicator of spatial autocorrelation for assessing high- and low-value clusters in multiscale datasets , 2015, Int. J. Geogr. Inf. Sci..

[66]  Xuewu Chen,et al.  Estimating the parking demand of free-floating bike sharing: A journey-data-based study of Nanjing, China , 2020, Journal of Cleaner Production.

[67]  Thomas F. Thornton,et al.  Challenges of collaborative governance in the sharing economy: The case of free-floating bike sharing in Shanghai , 2018, Journal of Cleaner Production.

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

[69]  Robert J. Schneider,et al.  Pilot Models for Estimating Bicycle Intersection Volumes , 2011 .

[70]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[71]  W. Y. Szeto,et al.  Solving a static repositioning problem in bike-sharing systems using iterated tabu search , 2014 .

[72]  Kees Maat,et al.  Commuting by Bicycle: An Overview of the Literature , 2010 .

[73]  R. Alexander Rixey,et al.  Station-Level Forecasting of Bikesharing Ridership , 2013 .

[74]  W. Y. Szeto,et al.  Dynamic green bike repositioning problem – A hybrid rolling horizon artificial bee colony algorithm approach , 2017 .

[75]  Luca Bertolini,et al.  Land use and public transport integration in small cities and towns: Assessment methodology and application , 2019, Journal of Transport Geography.

[76]  A. Jusoh,et al.  Important Motivators for Buying Green Products , 2014 .

[77]  Jia-zhen Huo,et al.  Sustainable co-governance of smart bike-sharing schemes based on consumers’ perspective , 2020 .