812
Views
13
CrossRef citations to date
0
Altmetric
Research Articles

Analysis of the spatiotemporal riding modes of dockless shared bicycles based on tensor decomposition

, , , &
Pages 2225-2242 | Received 07 Jun 2019, Accepted 08 May 2020, Published online: 28 May 2020

References

  • Ai, Y., et al. 2018. A deep learning approach on short-term spatiotemporal distribution forecasting of dockless bike-sharing system. Neural Computing & Applications, 31, 1665–1677.
  • Anandkumar, A., et al., 2014. Tensor decompositions for learning latent variable models. The Journal of Machine Learning Research, 15 (1), 2773–2832.
  • Asif, M.T., et al. 2016. Matrix and tensor based methods for missing data estimation in large traffic networks. IEEE Transactions on Intelligent Transportation Systems, 2016, 1–10.
  • Bao, J., et al. 2017. Exploring bikesharing travel patterns and trip purposes using smart card data and online point of interests. Networks and Spatial Economics, 17 (4), 1231–1253. doi:10.1007/s11067-017-9366-x
  • Boss, D., et al., 2018. Using crowdsourced data to monitor change in spatial patterns of bicycle ridership. Journal of Transport & Health, 9, 226–233. doi:10.1016/j.jth.2018.02.008
  • Buck, D., et al., 2018. Are bikeshare users different from regular cyclists? Transportation Research Record: Journal of the Transportation Research Board, 2387, 112–119. doi:10.3141/2387-13
  • Cai, Z., 2018. Analysis and modelling of urban mobility based on big data. Thesis (PhD). Zhejiang University. (In Chinese)
  • Cao, M., et al. 2019a. Analysis of the cycling flow between origin and destination for dockless shared bicycles based on singular value decomposition. ISPRS International Journal of Geo-Information, 8 (12), 573. doi:10.3390/ijgi8120573
  • Cao, M., et al. 2019b. Effects of free-floating shared bicycles on urban public transportation. ISPRS International Journal of Geo-Information, 8 (8), 323. doi:10.3390/ijgi8080323
  • Chakaravarthy, V.T., et al., 2017. On optimizing distributed tucker decomposition for sparse tensors. In: 2017 IEEE International Parallel and Distributed Processing Symposium, 29 May-2 June 2017 Orlando, 1038–1047.
  • Cichocki, A., Zdunek, R., and Amari, S.I., 2007. Nonnegative matrix and tensor factorization [lecture notes]. IEEE Signal Processing Magazine, 25 (1), 142–145. doi:10.1109/MSP.2008.4408452
  • Cong, F., et al., 2015. Tensor decomposition of EEG signals: a brief review. Journal of Neuroscience Methods, 248, 59–69. doi:10.1016/j.jneumeth.2015.03.018
  • Cui, X., 2018. Influencing factors of public participation willingness in shared bicycles and intervention strategies. Journal of Discrete Mathematical Sciences and Cryptography, 21 (6), 1437–1442. doi:10.1080/09720529.2018.1527811
  • DeMaio, P., 2009. Bike-sharing: history, impacts, models of provision, and future. Journal of Public Transportation, 12 (4), 3. doi:10.5038/2375-0901.12.4.3
  • Deng, L., Xie, Y., and Huang, D., 2017. Bicycle-sharing facility planning base on riding spatio-temporal data. Planners, 33 (10), 82–88. [In Chinese].
  • Dong, Y., et al., 2018. Revealing travel patterns of sharing-bikes in a spatial-temporal manner using non-negative matrix factorization method. In: 18th COTA International Conference of Transportation Professionals, 5-8 July 2018 Beijing.
  • Du, M. and Cheng, L., 2018. Better understanding the characteristics and influential factors of different travel patterns in free-floating bike sharing: evidence from Nanjing, China. Sustainability, 10 (4), 1244. doi:10.3390/su10041244
  • Fishman, E., et al. 2015. Factors in influencing bike share membership: analysis of Melbourne and Brisbane. Transportation Research Part A: Policy and Practice, 71, 17–30.
  • Fishman, E., Washington, S., and Haworth, N., 2014. Bike share’s impact on car use: evidence from the United States, Great Britain, and Australia. Transportation Research Part D: Transport & Environment, 31, 13–20. doi:10.1016/j.trd.2014.05.013
  • Fu, Y. and Huang, T.S., 2008. Image classification using correlation tensor analysis. IEEE Transactions on Image Processing, 17 (2), 226–234. doi:10.1109/TIP.2007.914203
  • He, B., et al. 2018. A simple line clustering method for spatial analysis with origin-destination data and its application to bike-sharing movement data. ISPRS International Journal of Geo-Information, 7 (6), 203. doi:10.3390/ijgi7060203
  • Jain, T., et al., 2018. Does the role of a bicycle share system in a city change over time? A longitudinal analysis of casual users and long-term subscribers. Journal of Transport Geography, 71, 45–57. doi:10.1016/j.jtrangeo.2018.06.023
  • Ji, Y., et al. 2018. Exploring spatially varying influences on metro-bikeshare transfer: A geographically weighted poisson regression approach. Sustainability, 10 (5), 1526. doi:10.3390/su10051526
  • Khoromskij, B. and Khoromskaia, V., 2007. Low rank Tucker-type tensor approximation to classical potentials. Open Mathematics, 5 (3), 523–550.
  • Kim, K., 2018. Investigation on the effects of weather and calendar events on bike-sharing according to the trip patterns of bike rentals of stations. Journal of Transport Geography, 66, 309–320. doi:10.1016/j.jtrangeo.2018.01.001
  • Kolda, T.G. and Bader, B.W., 2009. Tensor decompositions and applications. SIAM Review, 51 (3), 455–500. doi:10.1137/07070111X
  • Lenzen, M. and McBain, B., 2012. Using tensor calculus for scenario modelling. Environmental Modelling and Software, 37, 41–54. doi:10.1016/j.envsoft.2012.02.020
  • Li, X., et al. 2018. Free-floating bike sharing in Jiangsu: users’ behaviors and influencing factors. Energies, 11 (7), 1664. doi:10.3390/en11071664
  • Lim, L.H. and Comon, P., 2010. Multiarray signal processing: tensor decomposition meets compressed sensing. Comptes Rendus Mecanique, 338 (6), 311–320. doi:10.1016/j.crme.2010.06.005
  • Liu, J., et al., 2018. Identifying functional regions based on the spatio-temporal pattern of taxi trajectories. Journal of Geo-information Science, 20 (11), 1550–1561. [In Chinese].
  • Moayedi, A., Abbaspour, R.A., and Chehreghan, A., 2019. An evaluation of the efficiency of similarity functions in density-based clustering of spatial trajectories. Annals of GIS, 25 (4), 313–327. doi:10.1080/19475683.2019.1679254
  • Mooney, S.J., et al., 2019. Freedom from the station: spatial equity in access to dockless bike share. Journal of Transport Geography, 74, 91–96. doi:10.1016/j.jtrangeo.2018.11.009
  • Munoz-Mendez, F., et al., 2018. Community structures, interactions and dynamics in London’s bicycle sharing network. In: Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers, 8–12 October 2018, Singapore, 1015–1023.
  • O’ Brien, O., Cheshire, J., and Batty, M., 2014. Mining bicycle sharing data for generating insights into sustainable transport systems. Journal of Transport Geography, 34, 262–273. doi:10.1016/j.jtrangeo.2013.06.007
  • Pei, T., et al. 2011. Detecting arbitrarily shaped clusters using ant colony optimization. International Journal of Geographical Information Science, 25 (10), 1575–1595. doi:10.1080/13658816.2010.533674
  • Pfrommer, J., et al. 2013. Dynamic vehicle redistribution and online price incentives in shared mobility systems. IEEE Transactions on Intelligent Transportation Systems, 15 (4), 1567–1578. doi:10.1109/TITS.2014.2303986
  • Sergios, T. and Konstantinos, K., 2009. Pattern recognition. 4th. Cambridge: Academic Press.
  • Shaheen, S.A., Guzman, S., and Zhang, H., 2010. Bikesharing in Europe, the Americas, and Asia: past, present, and future. Transportation Research Record, 2143 (1), 159–167. doi:10.3141/2143-20
  • Shashua, A. and Hazan, T., 2005. Non-negative tensor factorization with applications to statistics and computer vision. In: Proceedings of the 22nd international conference on Machine learning, 7-11 August 2005 Bonn, 792–799.
  • Shen, Y., Zhang, X., and Zhao, J., 2018. Understanding the usage of dockless bike sharing in Singapore. International Journal of Sustainable Transportation, 12 (9), 686–700. doi:10.1080/15568318.2018.1429696
  • Sidiropoulos, N.D., et al. 2017. Tensor decomposition for signal processing and machine learning. IEEE Transactions on Signal Processing, 65 (13), 3551–3582. doi:10.1109/TSP.2017.2690524
  • Song, C., et al. 2019. Detecting arbitrarily shaped clusters in origin-destination flows using ant colony optimization. International Journal of Geographical Information Science, 33 (1), 134–154. doi:10.1080/13658816.2018.1516287
  • Sørensen, M. and De Lathauwer, L., 2013. Blind signal separation via tensor decomposition with vandermonde factor: canonical polyadic decomposition. IEEE Transactions on Signal Processing, 61 (22), 5507–5519. doi:10.1109/TSP.2013.2276416
  • Van Erven, T. and Harremos, P., 2014. Rényi divergence and Kullback-Leibler divergence. IEEE Transactions on Information Theory, 60 (7), 3797–3820. doi:10.1109/TIT.2014.2320500
  • Xin, F., et al. 2018. Cyclist satisfaction evaluation model for free-floating bike-sharing system: a case study of Shanghai. Transportation Research Record, 2672 (31), 21–32. doi:10.1177/0361198118770193
  • Xu, Y., et al., 2019. Unravel the landscape and pulses of cycling activities from a dockless bike-sharing system. Computers, Environment and Urban Systems, 75, 184–203. doi:10.1016/j.compenvurbsys.2019.02.002
  • Yan, Y., et al. 2018. Visual analytics of bike-sharing data based on tensor factorization. Journal of Visualization, 21 (3), 495–509. doi:10.1007/s12650-017-0463-1
  • Yang, M. and Zacharias, J., 2016. Potential for revival of the bicycle in Beijing. International Journal of Sustainable Transportation, 10 (6), 517–527. doi:10.1080/15568318.2015.1012281
  • Yang, Y., et al., 2019. A spatiotemporal and graph-based analysis of dockless bike sharing patterns to understand urban flows over the last mile. Computers, Environment and Urban Systems, 77, 101361. doi:10.1016/j.compenvurbsys.2019.101361
  • Yu, S., Zhang, X., and Zhao, J., 2018. Understanding the usage of dockless bike sharing in singapore. International Journal of Sustainable Transportation, 12 (9), 686–700.
  • Zademach, H.M. and Musch, A.K., 2018. Bicycle-sharing systems in an alternative/diverse economy perspective: a sympathetic critique. Local Environment, 23 (7), 734–746. doi:10.1080/13549839.2018.1434494
  • Zaltz Austwick, M., et al. 2013. The structure of spatial networks and communities in bicycle sharing systems. PloS One, 8 (9), e74685. doi:10.1371/journal.pone.0074685
  • Zhang, C. and Schmöcker, J.D., 2019. A Markovian model of user adaptation with case study of a shared bicycle scheme. Transportmetrica B: Transport Dynamics, 7 (1), 223–236.
  • Zhang, Q., et al., 2018a. High-order possibilistic c-means algorithms based on tensor decompositions for big data in IoT. Information Fusion, 39, 72–80. doi:10.1016/j.inffus.2017.04.002
  • Zhang, Y., et al., 2018b. Mining bike-sharing travel behavior data: an investigation into trip chains and transition activities. Computers, Environment and Urban Systems, 69, 39–50. doi:10.1016/j.compenvurbsys.2017.12.004
  • Zhu, Y. and Diao, M., 2019. Understanding the spatiotemporal patterns of public bicycle usage: a case study of hangzhou, china. International Journal of Sustainable Transportation, 14 (3), 163–176.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.