Abstract
One important factor in determining whether commuters will use public transport is spatial accessibility rooted in the first-mile problem. This study explores commuter behaviour in terms of how they utilize bike-sharing to manage the first-mile accessibility of a public transportation station. Historical data from Taipei Metro smart cards were analyzed using RFM (recency, frequency, and monetary) segmentation models to identify commuter segments. This study reveals two significant findings: comprehensive spatiotemporal characteristics and homogeneous behavioural patterns are derived from clustering algorithms. The city's penetration pricing strategy for bike-sharing motivates modal splitting transfer between bike-sharing and transit (MSTBT). In addition, we observed a supplementary and utilitarian relationship between bike-sharing and the metro. A convenient transportation network improves first-mile accessibility, thus the frequency of MSTBT usage is a key metric for measuring engagement. The findings provide a useful reference for urban planners promoting the design and development of sustainable transportation systems.
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The data used and analysed during the current study are available from the corresponding author on reasonable request.
References
Adnan M, Altal S, Bellemans T, Yasar AUH, Shakshuki EM (2018) Last-mile travel and bicycle sharing system in small/medium sized cities: user’s preferences investigation using hybrid choice model. J Ambient Intell Humaniz Comput 10(12):4721–4731. https://doi.org/10.1007/s12652-018-0849-5
Anwar AHBM, Yang J (2017) Examining the effects of transport policy on modal shift from private car to public bus. Procedia Eng 180:1413–1422. https://doi.org/10.1016/j.proeng.2017.04.304
Ashraf T, Hossen A, Dey K, El-Dabaja S, Aljeri M, Naik B (2021) Impacts of bike sharing program on subway ridership in New York City. Transp Res Rec J Transp Res Board 2675(9):924–934
Asiabi PKT, Tavoli R (2015) A review of different data mining techniques in customer segmentation. J Adv Comput Res 6(3):51–63
Aslantaş G, Gençgül M, Rumelli M, Özsaraç M, Bakırlı G (2023) Customer segmentation using K-means clustering algorithm and RFM model. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen Ve Mühendislik Dergisi 25(74):491–503. https://doi.org/10.21205/deufmd.2023257418
Boecker L, Anderson E, Uteng TP, Throndsen T (2020) Bike sharing use in conjunction to public transport: exploring spatiotemporal, age and gender dimensions in Oslo, Norway. Transp Res Part A: Policy Pract 138(1):380–401
Brons M, Givoni M, Rietveld P (2009) Access to railway stations and its potential in increasing rail use. Transp Res Part A: Policy Pract 43(1):136–149
Campbell KB, Brakewood C (2017) Sharing riderers: how bikesharing impacts bus ridership in New York City. Transp Res Part A: Policy Pract 100:264–282. https://doi.org/10.1016/j.tra.2017.04.017
Campbell AA, Cherry CR, Ryerson MS, Yang X (2016) Factors influencing the choice of shared bicycles and shared electric bikes in Beijing. Transp Res Part C: Emerg Technol 67:399–414
Chen AH, Liang YC, Chang WJ, Siauw HY, Minanda V (2022) RFM model and K-means clustering analysis of transit traveller profiles: a case study. J Adv Transp. https://doi.org/10.1155/2022/1108105. (Article ID 1108105)
Chiang WY (2014) Applying data mining with a new model on customer relationship management systems: a case of airline industry in Taiwan. Transp Lett 6(2):89–97
Cornet Y, Lugano G, Georgouli C, Milakis D (2022) Worthwhile travel time: a conceptual framework of the perceived value of enjoyment, productivity and fitness while travelling. Transp Rev 42:580–603. https://doi.org/10.1080/01441647.2021.1983067
Damant-Sirois G, El-Geneidy AM (2015) Who cycles more? Determining cycling frequency through a segmentation approach in Montreal, Canada. Transp Res Part A: Policy and Pract 77:113–125
Du Y, Deng F, Liao F (2019) A model framework for discovering the spatiotemporal usage patterns of public free-floating bike-sharing system. Transp Res Part C: Emerg Technol 103:39–55
Dutta S, Bhattacharya S, Guin KK (2015) Data mining in market segmentation: a literature review and suggestions. In: Proceedings of fourth international conference on soft computing for problem solving: SocProS 2014, Volume 1. Springer India, pp 87–98
Elias W, Shiftan Y (2012) The influence of individual’s risk perception and attitudes on travel behavior. Transp Res Part A: Policy Pract 46(8):1241–1251
Ernawati E, Baharin SSK, Kasmin F (2021) A review of data mining methods in RFM-based customer segmentation. J Phys: Conf Ser 1869:012085. https://doi.org/10.1088/1742-6596/1869/1/012085
Fan AH, Chen XM, Wan T (2019) How have travelers changed mode choices for first/last mile trips after the introduction of bicycle-sharing systems: an empirical study in Beijing China. J Adv Transp 2019:5426080. https://doi.org/10.1155/2019/5426080
Fishman E (2015) Bikeshare: a review of recent literature. Transp Rev 36(1):92–113. https://doi.org/10.1080/01441647.2015.1033036
Fishman E, Washington S, Haworth N (2013) Bike share: a synthesis of the literature. Transp Rev 33(2):148–165
Fontes T, Arantes M, Figueiredo PV, Novais P (2022) A cluster-based approach using smartphone data for bike-sharing docking stations identification: Lisbon case study. Smart Cities 5(1):251–275. https://doi.org/10.3390/smartcities5010016
Gončarovs P (2017) Data analytics in CRM processes: a literature review. Inf Technol Manag Sci 20(1):103–108. https://doi.org/10.1515/itms-2017-0018
Holiencinova M, Kadekova Z, Holota T, Nagyova L (2020) Smart solution of traffic congestion through bike sharing system in a small city. Mobile Netw Appl 25:868–875
Hosford K, Lear SA, Fuller D, Teschke K, Therrien S, Winters M (2018) Who is the near market for bicycle sharing? Identifying current, potential, and unlikely users of a public bicycle share program in Vancouver, Canada. BMC Public Health 18:1326. https://doi.org/10.1186/s12889-018-6246-3
Jimenez P, Nogal M, Caulfield B, Pilla F (2016) Perceptually important points of mobility patterns to characterize bike sharing systems: the Dublin case. J Transp Geogr 54:228–239
Jin HT, Jin FJ, Wang J, Sun W, Dong LB (2019) Competition and cooperation between shared bicycles and public transit: a case study of Beijing. Sustainability 11(5):1323
Karesdotter E, Page J, Mortberg U, Nasstrom H, Kalantari Z (2022) First mile/last mile problems in smart and sustainable cities: a case study in Stockholm County. J Urban Technol 29(2):115–137
Kaufman SM, Gordon-Koven L, Levenson N, Moss ML (2015) Citi Bike: the first two years. Rudin Center for Transportation. Robert F Wagner School of Public Service, New York University
Kong H, Jin ST, Sui DZ (2020) Deciphering the relationship between bikesharing and public transit: modal substitution, integration, and complementation. Transp Res Part D: Transp Environ 85:102392
Kowald M, Gutjar M, Roeth K, Schiller C, Dannewald T (2022) Mode choice effects on bike sharing systems. Fut Transp 12(9):4391. https://doi.org/10.3390/app12094391
Krygsman S, Dijst M, Arentze T (2004) Multimodal public transport: an analysis of travel time elements and the interconnectivity ratio. Transp Policy 11(1):265–275
Kutela B, Orochena N, Teng HL (2022) Analysis and O-D demand estimation of a public bike-sharing program in Las Vegas. J Transp Technol 12(2):172–192
Leth U, Shibayama T, Brezina T (2017) Competition or supplement? Tracing the relationship of public transport and bike-sharing in Vienna. GI Forum 5:137–151
Leurent F (2011) Transport capacity constraints on the mass transit system: a system analysis. Eur Transp Res Rev 3:11–21
Libey DR (2003) Libey on Recency, Frequency, and Monetary Value. E-Book, UNKNO
Liu Y, Ji YJ, Feng T, Shi ZZB (2020) Use frequency of metro-bikeshare integration: evidence from Nanjing, China. Sustainability 12(4):1426
Lloyd SP (1982) Least squares quantization in PCM. IEEE Trans Inf Theory 28:129–137
Ma T, Liu C, Erdogan S (2015) Bicycle sharing and public transit: does capital bikeshare affect metrorail ridership in Washington, DC? Transp Res Rec J Transp Res Board 2534(1):1–9
Ma XW, Cao RM, Jin YC (2019) Spatiotemporal clustering analysis of bicycle sharing system with data mining approach. Information 10:163. https://doi.org/10.3390/info10050163
Martin EW, Shaheen SA (2014) Evaluating public transit modal shift dynamics in response to bikesharing: a tale of two US Cities. J Transp Geogr 41:315–324
Matrai T, Toth J (2020) Cluster analysis of public bike sharing systems for categorization. Sustainability 12(14):5501. https://doi.org/10.3390/su12145501
Miglautsch JR (2000) Thoughts on RFM scoring. J Database Mark Cust Strateg Manag 8:67–72
Morton C (2017) Appraising the market for bicycle sharing schemes: perceived service quality, satisfaction, and behavioral intention in London. Case Stud Transp Policy 6(1):102–111
Muetzel CM, Scheiner J (2022) Investigating spatio-temporal mobility patterns and changes in metro usage under the impact of COVID-19 using Taipei Metro smart card data. Public Transp 14:343–366. https://doi.org/10.1007/s12469-021-00280-2
Paul L, Ramanan TR (2019) An RFM and CLV analysis for customer retention and customer relationship management of a logistics firm. Int J Appl Manag Sci 11(4):333–351
Radzimski A, Dziecielski M (2021) Exploring the relationship between bike-sharing and public transport in Poznan, Poland. Transp Res Part A: Policy Pract 45:189–202
Rahman M, Akther MS, Recker W (2022) The first-and-last-mile of public transportation: a study of access and egress travel characteristics of Dhaka’s suburban commuters. J Public Transp 24(1):100025
Rogers WP III, Chen N, Looye JW (2023) Beyond traditional TOD: Integrating multiuse paths and bike share into public transit to address the first/last mile issue. Urban Rail Transit 9:42–56
Romanillos G, Austwick MZ, Ettema D, De Kruijf J (2016) Big data and cycling. Transp Rev 36(1):114–133
Sathishkumar VE, Park J, Cho Y (2020) Using data mining techniques for bike sharing demand prediction in metropolitan city. Comput Commun 153:353–366
Soto JJ, Cantillo V, Arellana J (2021) Market segmentation for incentivizing sustainable transport polices. Transp Res Part D: Transp Environ 99:103013. https://doi.org/10.1016/j.trd.2021.103013
Ushakov D, Dudukalov E, Mironenko E, Shatila K (2022) Big data analytics in smart cities’ transportation infrastructure modernization. Transp Res Procedia 63:2385–2391. https://doi.org/10.1016/j.trpro.2022.06.274
Westland JC, Mou J, Yi DF (2019) Demand cycles and market segmentation in bicycle sharing. Inf Process Manag 56(4):1592–1604
Wibowo S, Olszewski PS (2005) Modeling walking accessibility to public transport terminals: case study of Singapore mass rapid transit. J East Asia Soc Transp Stud 6:147–156
Wichman CJ, Cunningham B (2020) Notching for free: do cyclists reveal the value of time? Working paper. Resources for the future. Washington DC
Wu YR, Li WF, Yu Q, Li J (2022) Analysis of the relationship between dockless bicycle-sharing and the metro: connection, competition, and complementation. J Adv Transp 2022:5664004
Yao Y, Zhang YF, Tian LX, Zhou NX, Li ZL, Wang MG (2019) Analysis of network structure of urban bike-sharing system: a case study based on real-time data of a public bicycle system. Sustainability 11:5425
Yoseph F, Malim N, Hashimah N, Heikkila M, Brezulianu A, Geman O, Rostam P, Aqilah N (2020) The impact of big data market segmentation using data mining and clustering techniques. J Intell Fuzzy Syst 38(5):6159–6173
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This research was funded by the Ministry of Science and Technology, Taiwan, R.O.C.; grant numbers: MOST 110-2221-E-033-033 and MOST 111-2221-E-033-029.
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Conceptualization, AH-LC; methodology, AH-LC, and W-JC; software, W-JC and AH-LC; validation, AH-LC, and W-JC; formal analysis, AH-LC, W-JC and KC; writing—original draft preparation, AH-LC and KC; writing—review and editing, AH-LC and KC; visualization, AH-LC; supervision, AH-LC; project administration, AH-LC; funding acquisition, AH-LC All authors have read and agreed to the published version of the manuscript.
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Chen, A.H.L., Cheng, K. & Chang, WJ. Unravelling commuters' modal splitting behaviour in mass transportation service operation. Public Transp 15, 813–838 (2023). https://doi.org/10.1007/s12469-023-00330-x
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DOI: https://doi.org/10.1007/s12469-023-00330-x