Travel preference of bicycle-sharing users: A multi-granularity sequential pattern mining approach
DOI:
https://doi.org/10.15837/ijccc.2022.1.4673Keywords:
public bicycle system, user riding preference, frequent pattern, sequential pattern, multi-granularityAbstract
Public bicycles are an indispensable part of green public transportation and are also a convenient and economical manner for the general public. In operation management, it is very important and imperative to understand the user demand and pattern of the public bicycle system. This paper took the public bicycle system in Hohhot as the research object, collected nearly 4 years of operating data, and studied the travel preferences of users in the public bicycle system in view of multiple granularities. Specifically, the data of car rental users at three time-granularities were obtained through data extraction technology. Finally, frequent pattern mining was performed on car rental data based on different time granularities and mapped to the user’s riding preference, and then the riding modes of different car rental users founded on different time granularities were determined. Finally, this article gave different management opinions based on the different riding preferences of public bicycle users in Hohhot.
References
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[29] Schimohr, K. and Scheiner, J.(2021). Spatial and temporal analysis of bike-sharing use in Cologne taking into account a public transit disruption, Journal of Transport Geography, 92, 103017, 2021. https://doi.org/10.1016/j.jtrangeo.2021.103017
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[35] Xing, Y.; Wang, K. and Lu, J.(2020). Exploring travel patterns and trip purposes of dockless bike-sharing by analyzing massive bike-sharing data in Shanghai China, Journal of Transport Geography, 87, 102787, 2020. https://doi.org/10.1016/j.jtrangeo.2020.102787
[36] Zhang, Y.; Brussel, M.; Thomas, T. and Van Maarseveen, M.(2018). Mining bike-sharing travel behavior data: an investigation into trip chains and transition activities, Computers Environment and Urban Systems, 69, 39-50, 2018. https://doi.org/10.1016/j.compenvurbsys.2017.12.004
[37] Zhang, Y.; Thomas, T.; Brussel, M. and Maarseveen, M.(2016). Expanding bicycle-sharing systems: lessons learnt from an analysis of usage, PLoS ONE, 11(12), e0168604, 2016. https://doi.org/10.1371/journal.pone.0168604
[38] Zhang, Y.; Wen, H.; Qiu, F.; Wang, Z. and Abbas, H.(2019). iBike: intelligent public bicycle services assisted by data analytics, Future Generation Computer Systems, 95, 187-197, 2019. https://doi.org/10.1016/j.future.2018.12.017
[39] Zhao, J.; Fan, S. and Yan, J.(2016). Overview of business innovations and research opportunities in blockchain and introduction to the special issue, Financial Innovation, 2(1), 1-7, 2016. https://doi.org/10.1186/s40854-016-0049-2
[40] Zhong, X. and Enke, D.(2019). Predicting the daily return direction of the stock market using hybrid machine learning algorithms, Financial Innovation. 5(1), 1-20, 2019. https://doi.org/10.1186/s40854-019-0138-0
[41] Zhou, X. and Chen, Y.(2015). Understanding spatiotemporal patterns of biking behavior by analyzing massive bike sharing data in Chicago, Plos ONE, 10(10): e0137922, 2015. https://doi.org/10.1371/journal.pone.0137922
[2] Borgnat, P.; Fleury, E.; Robardet, C. and Scherrer, A.(2009). Spatial analysis of dynamic movements of Vélo'v, Lyon's shared bicycle program, Proceedings of European Conference on Complex Systems, 1-6, 2009.
[3] Cerutti, P.; Martins, R.; Macke, J. and Sarate, J.(2019). "Green, but not as green as that": An analysis of a Brazilian bike-sharing system, Journal of Cleaner Production, 217, 185-193, 2019. https://doi.org/10.1016/j.jclepro.2019.01.240
[4] Chen, L.; Ma, X.; Nguyen, T.; Pan, G. and Jakubowicz, J.(2016). Understanding bike trip patterns leveraging bike sharing system open data, Frontiers of Computer Science, 77, 1-11, 2016. https://doi.org/10.1007/s11704-016-6006-4
[5] Fisch-Romito, V.; Guivarch, C. (2019). Transportation infrastructures in a low carbon world: An evaluation of investment needs and their determinants, Transportation Research Part D: Transport & Environment, 72, 203-219, 2019. https://doi.org/10.1016/j.trd.2019.04.014
[6] Fournier, N.; Christofa, E. and Knodler, M.(2017). A sinusoidal model for seasonal bicycle demand estimation, Transportation Research Part D: Transport & Environment, 50(1), 154-169, 2017. https://doi.org/10.1016/j.trd.2016.10.021
[7] Gallinucci, E.; Golfarelli, M.; Rizzi, S.; Abello, A. and Romero, O.(2018). Interactive multidimensional modeling of linked data for exploratory OLAP, Information Systems, 77, 86-104, 2018. https://doi.org/10.1016/j.is.2018.06.004
[8] Ge, Y.; Lu, H. and Peng, P.(2021). Mixed-order spectral clustering for complex networks, Pattern Recognition, 117, 107964, 2021. https://doi.org/10.1016/j.patcog.2021.107964
[9] Kaltenbrunner, A.; Meza, R.; Grivolla, J.; Codina, J. and Banchs, R.(2010). Urban cycles and mobility patterns: Exploring and predicting trends in a bicycle-based public transport system, Pervasive and Mobile Computing, 6, 455-466, 2010. https://doi.org/10.1016/j.pmcj.2010.07.002
[10] Kaspi, M.; Raviv, T. and Tzur, M.(2016). Detection of unusable bicycles in bike-sharing systems, Omega, 65, 10-16, 2016. https://doi.org/10.1016/j.omega.2015.12.003
[11] Keramati, A.; Ghaneei, H. and Mirmohammadi, S.(2016). Developing a prediction model for customer churn from electronic banking services using data mining, Financial Innovation, 2(1), 1-13, 2016. https://doi.org/10.1186/s40854-016-0029-6
[12] Khan, M.K.; Khan, M.I. and Rehan, M.(2020). The relationship between energy consumption, economic growth and carbon dioxide emissions in pakistan, Financial Innovation, 6(1), 1-13, 2020. https://doi.org/10.1186/s40854-019-0162-0
[13] Kou, G.; Lu, Y.; Peng, Y. and Shi, Y.(2012). Evaluation of classification algorithms using MCDM and rank correlation, International Journal of Information Technology & Decision Making, 11(1), 197-225, 2012. https://doi.org/10.1142/S0219622012500095
[14] Kou, G.; Xiao, H.; Cao, M. and Lee, L.(2021). Optimal computing budget allocation for the vector evaluated genetic algorithm in multi-objective simulation optimization, Automatica, 129, 109599, 2021. https://doi.org/10.1016/j.automatica.2021.109599
[15] Kou, Z.; Wang, X.; Chiu, S. and Cai, H.(2020). Quantifying greenhouse gas emissions reduction from bike share systems: a model considering real-world trips and transportation mode choice patterns, Resources Conservation and Recycling, 153, 104534, 2020. https://doi.org/10.1016/j.resconrec.2019.104534
[16] Kumar, D.(2021). Meteorological barriers to bike rental demands: a case of Washington D.C. using NCA approach, Case Studies on Transport Policy, 9(2), 830-841, 2021. https://doi.org/10.1016/j.cstp.2021.04.002
[17] Li, A.; Gao, K.; Zhao, P.; Qu, X. and Axhausen, K.W.(2021). High-resolution assessment of environmental benefits of dockless bike-sharing systems based on transaction data, Journal of Cleaner Production, 296, 126423, 2021. https://doi.org/10.1016/j.jclepro.2021.126423
[18] Li, T.; Kou, G. and Peng, Y.(2020). Improving malicious URLs detection via feature engineering: Linear and nonlinear space transformation methods, Information Systems, 91, 101494, 2020. https://doi.org/10.1016/j.is.2020.101494
[19] Liu, T.; Wang, Y.; Li, H. and Qi, Y.(2021). China's low-carbon governance at community level: A case study in Min'an community, Beijing, Journal of Cleaner Production, 311, 127530, 2021. https://doi.org/10.1016/j.jclepro.2021.127530
[20] Liu, Y. and Tian, L. (2020). A graded cluster system to mine virtual stations in free-floating bike-sharing system on multi-scale geographic view, Journal of Cleaner Production, 281, 124692, 2020. https://doi.org/10.1016/j.jclepro.2020.124692
[21] Luo, H.; Kou, Z.; Zhao, F. and Cai, H.(2019). Comparative life cycle assessment of station-based and dock-less bike sharing systems, Resources Conservation and Recycling, 146, 180-189, 2019. https://doi.org/10.1016/j.resconrec.2019.03.003
[22] Luo, H.; Zhao, F.; Chen, W. and Cai, H.(2020). Optimizing bike sharing systems from the life cycle greenhouse gas emissions perspective, Transportation Research Part C: Emerging Technologies, 117, 102705, 2020. https://doi.org/10.1016/j.trc.2020.102705
[23] Mateo-Babiano, I.; Bean, R.; Corcoran, J. and Pojani, B.(2016). How does our natural and built environment affect the use of bicycle sharing?, Transportation Research Part A: Policy and Practice, 94, 295-307, 2016. https://doi.org/10.1016/j.tra.2016.09.015
[24] Niyazmand, T. and Izadi, I.(2019). Pattern mining in alarm flood sequences using a modified PrefixSpan algorithm, ISA Transactions, 90, 287-293, 2019. https://doi.org/10.1016/j.isatra.2018.12.050
[25] Panda, A. and Nanda, S.(2017). Short-term and long-term interconnectedness of stock returns in western europe and the global market, Financial Innovation, 7(1), 1-36, 2021. https://doi.org/10.1186/s40854-016-0051-8
[26] Peng, Y.; Kou, G.; Shi, Y. and Chen, Z.(2008). A descriptive framework for the field of data mining and knowledge discovery, International Journal of Information Technology & Decision Making, 7(4), 639-682, 2008. https://doi.org/10.1142/S0219622008003204
[27] Randriamanamihaga, A.; Come, E.; Oukhellou, L. and Govaert, G.(2014). Clustering the Vélib' dynamic origin/destination flows using a family of poisson mixture models, Neurocomputing, 141, 124-138, 2014. https://doi.org/10.1016/j.neucom.2014.01.050
[28] Reiss, S. and Bogenberger, K.(2015). GPS-data analysis of munich's free-floating bike sharing system and application of an operator-based relocation strategy, Proceedings of IEEE International Conference on Intelligent Transportation Systems, 584-589, 2015. https://doi.org/10.1109/ITSC.2015.102
[29] Schimohr, K. and Scheiner, J.(2021). Spatial and temporal analysis of bike-sharing use in Cologne taking into account a public transit disruption, Journal of Transport Geography, 92, 103017, 2021. https://doi.org/10.1016/j.jtrangeo.2021.103017
[30] Si, H.; Shi, J.; Wu, G.; Chen, J. and Zhao, X.(2019). Mapping the bike sharing research published from 2010 to 2018: a scientometric review, Journal of Cleaner Production, 213, 415-427, 2019. https://doi.org/10.1016/j.jclepro.2018.12.157
[31] Soza-Parra, J.; Raveau, S. and Muoz, J.(2021). Travel preferences of public transport users under uneven headways, Transportation Research Part A: Policy and Practice, 147, 61-75, 2021. https://doi.org/10.1016/j.tra.2021.02.012
[32] Vogel, P. and Mattfeld, D.(2011). Strategic and operational planning of bike-sharing systems by data mining: a case study, Proceedings of International Conference on Computational Logistics, 127-141, 2011. https://doi.org/10.1007/978-3-642-24264-9_10
[33] Wang, H.; Kou, G. and Peng, Y.(2021). Multi-class misclassification cost matrix for credit ratings in peer-to-peer lending, Journal of the Operational Research Society, 72(4), 923-934, 2021. https://doi.org/10.1080/01605682.2019.1705193
[34] Wang, Z.; Xue, M.; Zhao, Y. and Zhang, B.(2020). Trade-off between environmental benefits and time costs for public bicycles: An empirical analysis using streaming data in China, Science of The Total Environment, 715, 136847, 2020. https://doi.org/10.1016/j.scitotenv.2020.136847
[35] Xing, Y.; Wang, K. and Lu, J.(2020). Exploring travel patterns and trip purposes of dockless bike-sharing by analyzing massive bike-sharing data in Shanghai China, Journal of Transport Geography, 87, 102787, 2020. https://doi.org/10.1016/j.jtrangeo.2020.102787
[36] Zhang, Y.; Brussel, M.; Thomas, T. and Van Maarseveen, M.(2018). Mining bike-sharing travel behavior data: an investigation into trip chains and transition activities, Computers Environment and Urban Systems, 69, 39-50, 2018. https://doi.org/10.1016/j.compenvurbsys.2017.12.004
[37] Zhang, Y.; Thomas, T.; Brussel, M. and Maarseveen, M.(2016). Expanding bicycle-sharing systems: lessons learnt from an analysis of usage, PLoS ONE, 11(12), e0168604, 2016. https://doi.org/10.1371/journal.pone.0168604
[38] Zhang, Y.; Wen, H.; Qiu, F.; Wang, Z. and Abbas, H.(2019). iBike: intelligent public bicycle services assisted by data analytics, Future Generation Computer Systems, 95, 187-197, 2019. https://doi.org/10.1016/j.future.2018.12.017
[39] Zhao, J.; Fan, S. and Yan, J.(2016). Overview of business innovations and research opportunities in blockchain and introduction to the special issue, Financial Innovation, 2(1), 1-7, 2016. https://doi.org/10.1186/s40854-016-0049-2
[40] Zhong, X. and Enke, D.(2019). Predicting the daily return direction of the stock market using hybrid machine learning algorithms, Financial Innovation. 5(1), 1-20, 2019. https://doi.org/10.1186/s40854-019-0138-0
[41] Zhou, X. and Chen, Y.(2015). Understanding spatiotemporal patterns of biking behavior by analyzing massive bike sharing data in Chicago, Plos ONE, 10(10): e0137922, 2015. https://doi.org/10.1371/journal.pone.0137922
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