Understanding spatial-temporal travel demand of free-floating bike sharing connecting with metro stations

Abstract Free-floating bike sharing usage for metro access provides a decent solution to the first- and last-mile problem. A fundamental and still open problem is the spatial and temporal regularities of bike sharing usage integrated with metro stations, which are crucial to achieve a seamless connection and provide an efficient transport system. In this paper, we conduct the usage of bike-and-ride in Beijing as an example to address this issue from macro-level and micro-level perspectives. First, the macroscopic usages, including distinct characteristics of time-varying trips and scaling relationships of spatial distribution, are explored in urban and suburban areas. Then, by adequately deconstructing temporal-spatial trips of bike-and-ride, the bike sharing usage is revealed to follow a power-law distribution with different exponents on weekdays and weekends. Our results suggest that scale-free behaviors for microcosmic travel demand exist across the city. These vital phenomena switch within the same region on different time ranges such as morning and evening peaks but similar scaling relations on different days. The findings improve our understanding of usage patterns and demand distribution of this emerging transport mode and supply an indication of the dynamic deployment of the free-floating bike sharing integrating with the mass transit system.

[1]  Yu Zhang,et al.  Free-floating bike sharing: Solving real-life large-scale static rebalancing problems , 2017 .

[2]  Stephen B. Seidman,et al.  Network structure and minimum degree , 1983 .

[3]  Yung-Hsiang Cheng,et al.  Evaluating bicycle-transit users’ perceptions of intermodal inconvenience , 2012 .

[4]  Pan Liu,et al.  The station-free sharing bike demand forecasting with a deep learning approach and large-scale datasets , 2018, Transportation Research Part C: Emerging Technologies.

[5]  P. Rietveld,et al.  Acces to railway stations and its potential in increasing rail use , 2009 .

[6]  Alexis J. Comber,et al.  Who, Where, Why and When? Using Smart Card and Social Media Data to Understand Urban Mobility , 2019, ISPRS Int. J. Geo Inf..

[7]  Karst Teunis Geurs,et al.  Modelling observed and unobserved factors in cycling to railway stations: application to transit-oriented-developments in the Netherlands , 2015 .

[8]  Alison J. Heppenstall,et al.  Using graph structural information about flows to enhance short-term demand prediction in bike-sharing systems , 2020, Comput. Environ. Urban Syst..

[9]  Bin Jiang,et al.  Characterizing the human mobility pattern in a large street network. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[10]  Chi K. Tse,et al.  Spatial analysis of bus transport networks using network theory , 2018, Physica A: Statistical Mechanics and its Applications.

[11]  Xiaolu Zhou,et al.  Bike-sharing or taxi? Modeling the choices of travel mode in Chicago using machine learning , 2019, Journal of Transport Geography.

[12]  Daniel Sun,et al.  Measuring Vulnerability of Urban Metro Network from Line Operation Perspective , 2016 .

[13]  Linchuan Yang,et al.  Dockless bike-sharing as a feeder mode of metro commute? The role of the feeder-related built environment: Analytical framework and empirical evidence , 2020 .

[14]  Yuchuan Du,et al.  A model framework for discovering the spatio-temporal usage patterns of public free-floating bike-sharing system , 2019, Transportation Research Part C: Emerging Technologies.

[15]  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 .

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

[17]  Zhengbing He Spatial-temporal fractal of urban agglomeration travel demand , 2020 .

[18]  Ying Zhang,et al.  Mining bike-sharing travel behavior data: An investigation into trip chains and transition activities , 2018, Comput. Environ. Urban Syst..

[19]  W. Deng,et al.  Exploring bikesharing travel time and trip chain by gender and day of the week , 2015 .

[20]  Albert-László Barabási,et al.  Understanding individual human mobility patterns , 2008, Nature.

[21]  Sevgi Erdogan,et al.  Bicycle Sharing and Public Transit , 2015 .

[22]  Wang Wei,et al.  The Demand Analysis of Bike-and-ride in Rail Transit Stations based on Revealed and Stated Preference Survey , 2013 .

[23]  Michael Batty,et al.  A long-time limit of world subway networks , 2011, 1105.5294.

[24]  T. Geisel,et al.  The scaling laws of human travel , 2006, Nature.

[25]  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..

[26]  Katia Obraczka,et al.  Scale-Free Properties of Human Mobility and Applications to Intelligent Transportation Systems , 2018, IEEE Transactions on Intelligent Transportation Systems.

[27]  Michael J. Cassidy,et al.  Optimal design of transit networks fed by shared bikes , 2020 .

[28]  Jian John Lu,et al.  Exploring travel patterns and trip purposes of dockless bike-sharing by analyzing massive bike-sharing data in Shanghai, China , 2020 .

[29]  Yingling Fan,et al.  Public bicycle as a feeder mode to rail transit in China: The role of gender, age, income, trip purpose, and bicycle theft experience , 2017 .

[30]  Paolo Santi,et al.  Understanding spatio-temporal heterogeneity of bike-sharing and scooter-sharing mobility , 2020, Comput. Environ. Urban Syst..

[31]  Jiang Zhang,et al.  Simple spatial scaling rules behind complex cities , 2017, Nature Communications.

[32]  Simon Washington,et al.  Bike Share: A Synthesis of the Literature , 2013 .

[33]  Kaiquan Cai,et al.  Effective usage of shortest paths promotes transportation efficiency on scale-free networks , 2013 .

[34]  Yuanyuan Guo,et al.  Built environment effects on the integration of dockless bike-sharing and the metro , 2020 .

[35]  Wei Wang,et al.  An Association Rule Based Method to Integrate Metro-Public Bicycle Smart Card Data for Trip Chain Analysis , 2018 .

[36]  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 .

[37]  Mutao Huang,et al.  An Integrated Graphic Modeling System for Three-Dimensional Hydrodynamic and Water Quality Simulation in Lakes , 2019, ISPRS Int. J. Geo Inf..

[38]  B. Loo,et al.  Rail-Based Transit-Oriented Development: Lessons from New York City and Hong Kong , 2010 .

[39]  Maria Bordagaray,et al.  Capturing the conditions that introduce systematic variation in bike-sharing travel behavior using data mining techniques , 2016 .

[40]  Cheng-Min Feng,et al.  Public bike system pricing and usage in Taipei , 2017 .

[41]  Yan-jie Ji,et al.  Exploring Spatially Varying Influences on Metro-Bikeshare Transfer: A Geographically Weighted Poisson Regression Approach , 2018 .

[42]  Gitakrishnan Ramadurai,et al.  Topological properties of bus transit networks considering demand and service utilization weight measures , 2020 .

[43]  Xin Li,et al.  Revealing Spatio-Temporal Patterns and Influencing Factors of Dockless Bike Sharing Demand , 2020, IEEE Access.

[44]  Piet Rietveld,et al.  The accessibility of railway stations: the role of the bicycle in The Netherlands , 2000 .

[45]  Yountaik Leem,et al.  Bicycle-based transit-oriented development as an alternative to overcome the criticisms of the conventional transit-oriented development , 2016 .

[46]  Hai-Jun Huang,et al.  Scale-free resilience of real traffic jams , 2018, Proceedings of the National Academy of Sciences.

[47]  Xiaolu Zhou,et al.  Understanding Spatiotemporal Patterns of Biking Behavior by Analyzing Massive Bike Sharing Data in Chicago , 2015, PloS one.

[48]  Miaojia Lu,et al.  Improving the sustainability of integrated transportation system with bike-sharing: A spatial agent-based approach , 2018, Sustainable Cities and Society.

[49]  H. Stanley,et al.  Gravity model in dockless bike-sharing systems within cities. , 2021, Physical review. E.

[50]  Céline Robardet,et al.  Shared Bicycles in a City: a Signal Processing and Data Analysis Perspective , 2011, Adv. Complex Syst..

[51]  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 .

[52]  Ziyou Gao,et al.  Switch between critical percolation modes in city traffic dynamics , 2017, Proceedings of the National Academy of Sciences.

[53]  Elliot W. Martin,et al.  Evaluating public transit modal shift dynamics in response to bikesharing: a tale of two U.S. cities , 2014 .

[54]  Huijun Sun,et al.  Extreme unbalanced mobility network in bike sharing system , 2021 .

[55]  M. Majder-Łopatka,et al.  The Influence of Hydrogen on the Indications of the Electrochemical Carbon Monoxide Sensors , 2019, Sustainability.

[56]  Wen-Xu Wang,et al.  Traffic-driven epidemic outbreak on complex networks: How long does it take? , 2012, Chaos.

[57]  Ying-Cheng Lai,et al.  Universal model of individual and population mobility on diverse spatial scales , 2017, Nature Communications.

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

[59]  Rui Wang,et al.  Bicycle-Transit Integration in the United States, 2001–2009 , 2013 .

[60]  Shengxiao Li,et al.  Bicycle-metro integration in a growing city: The determinants of cycling as a transfer mode in metro station areas in Beijing , 2017 .

[61]  L. Meng,et al.  The analysis of catchment areas of metro stations using trajectory data generated by dockless shared bikes , 2019, Sustainable Cities and Society.

[62]  Lijun Sun,et al.  Spatio-Temporal Usage Patterns of Dockless Bike-Sharing Service Linking to a Metro Station: A Case Study in Shanghai, China , 2020, Sustainability.

[63]  Yan-jie Ji,et al.  Use Frequency of Metro–Bikeshare Integration: Evidence from Nanjing, China , 2020, Sustainability.

[64]  Sasu Tarkoma,et al.  Explaining the power-law distribution of human mobility through transportation modality decomposition , 2014, Scientific Reports.

[65]  Qing Shen,et al.  Intermodal Transfer between Bicycles and Rail Transit in Shanghai, China , 2010 .

[66]  Michael Duncan,et al.  The Impact of Transit-oriented Development on Housing Prices in San Diego, CA , 2011, Urban studies.