Behavior analysis of carsharing users based on deep belief network and improved two-stage clustering

References

• Atasoy, B., T. Ikeda, X. Song, et al. 2015. “The Concept and Impact Analysis of a Flexible Mobility on Demand System.” Transportation Research Part C: Emerging Technologies 56:373–392.
   Web of Science ®Google Scholar
• Bi, J., R. Zhi, D. F. Xie, et al. 2020. “Capturing the Characteristics of Car-Sharing Users: Data-Driven Analysis and Prediction Based on Classification.” Journal of Advanced Transportation 2020 (6): 1–11.
   Google Scholar
• Chen, T. D., and K. M. Kockelman. 2016. “Carsharing’s Life-cycle Impacts on Energy Use and Greenhouse Gas Emissions.” Transportation Research Part D: Transport and Environment 47: 276–284. doi:10.1016/j.trd.2016.05.012.
   Web of Science ®Google Scholar
• Clewlow, R. R. 2016. “Carsharing and Sustainable Travel Behavior: Results from the San Francisco Bay Area.” Transport Policy 51: 158–164. doi:10.1016/j.tranpol.2016.01.013.
   Web of Science ®Google Scholar
• Cui, H., and X. Qin. 2015. “The Video Recommendation System Based on DBN[C].” 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (CIT/IUCC/DASC/PICOM). IEEE.
   Google Scholar
• Efthymiou, D., and C. Antoniou. 2016. “Modeling the Propensity to Join Carsharing Using Hybrid Choice Models and Mixed Survey Data.” TRANSPORT POLICY 51: 143–149. doi:10.1016/j.tranpol.2016.07.001.
   Web of Science ®Google Scholar
• Firnkorn, J., and M. Müller. 2015. “Free-floating Electric Carsharing-fleets in Smart Cities: The Dawning of a Post-private Car Era in Urban Environments.” Environ Sci Policy 45: 30–40. doi:10.1016/j.envsci.2014.09.005.
   Web of Science ®Google Scholar
• Fleury, S., A. Tom, E. Jamet, E. Colas-Maheux, et al. 2017. “What Drives Corporate Carsharing Acceptance? A French Case Study”. Transportation Research Part F: Traffic Psychology and Behaviour 45: 218–227. doi:10.1016/j.trf.2016.12.004.
   Web of Science ®Google Scholar
• Higgs, B., and M. Abbas. 2015. “Segmentation and Clustering of Car-Following Behavior: Recognition of Driving Patterns[J].” IEEE Transactions on Intelligent Transportation Systems 16 (1): 81–90. doi:10.1109/TITS.2014.2326082.
   Web of Science ®Google Scholar
• Hinton, G. E. 1986. “Learning and Relearning in Boltzmann Machines.” Parallel Distributed Processing 1.
   Google Scholar
• Hui, Y., M. Ding, K. Zheng, D. Lou, et al. 2017a. “Observing Trip Chain Characteristics of Round-Trip Carsharing Users in China: A Case Study Based on GPS Data in Hangzhou City.” Sustainability-basel 9 (6): 949. doi:10.3390/su9060949.
   Web of Science ®Google Scholar
• Hui, Y., W. Wang, M. Ding, Y. Liu, et al. 2017b. “Behavior Patterns of Long-term Car-sharing Users in China*”. Transportation Research Procedia 25: 4662–4678. doi:10.1016/j.trpro.2017.05.303.
   Google Scholar
• Hui, Y., W. Wang, and Q. Sun. 2016. “Study on the Use Behavior Characteristics of CarSharing Members–Taking Hangzhou Car Fanxiang as an Example.” Transportation and Port & Shipping 3 (5): 24–29.
   Google Scholar
• Jiang, M. Y., Y. C. Liang, X. Feng, X. Fan, Z. Pei, Y. Xue, R. Guan, et al. 2018. “Text Classification Based on Deep Belief Network and Softmax Regression.” Neural Computing & Applications 29 (1): 61–70. doi:10.1007/s00521-016-2401-x.
   Web of Science ®Google Scholar
• Ko, J., H. Ki, and S. Lee. 2017. “Factors Affecting Carsharing Program Participants’ Car Ownership Changes.” Transportation Letters 1–11.
   Web of Science ®Google Scholar
• Li, C., Z. Ding, J. Yi, Y. Lv, G. Zhang, et al. 2018. “Deep Belief Network Based Hybrid Model for Building Energy Consumption Prediction.” Energies 11 (1): 242. doi:10.3390/en11010242.
   Web of Science ®Google Scholar
• McCarthy, O. T., B. Caulfield, and M. O’Mahony. 2016. “Technology Engagement and Privacy: A Cluster Analysis of Reported Social Network Use among Transport Survey Respondents.” Transportation Research Part C Emerging Technologies 63: 195–206. doi:10.1016/j.trc.2015.12.015.
   Web of Science ®Google Scholar
• Niels, T., and K. Bogenberger. 2017. “Booking Behavior of Free-Floating Carsharing Users.” Transportation Research Record: Journal of the Transportation Research Board 2650 (1): 123–132. doi:10.3141/2650-15.
   Google Scholar
• Nobis, C. 2018. “Carsharing as Key Contribution to Multimodal and Sustainable Mobility Behavior: Carsharing in Germany.” Transport Res Rec.
   Google Scholar
• Qian, C., W. Li, M. Ding, et al. 2017. “Mining Carsharing Use Patterns from Rental Data: A Case Study of Chefenxiang in Hangzhou, China”. Transportation Research Procedia 25: 2583–2602. doi:10.1016/j.trpro.2017.05.307.
   Google Scholar
• Schmöller, S., S. Weikl, J. Müller, K. Bogenberger, et al. 2015. “Empirical Analysis of Free-floating Carsharing Usage: The Munich and Berlin Case”. Transportation Research Part C: Emerging Technologies 56: 34–51. doi:10.1016/j.trc.2015.03.008.
   Web of Science ®Google Scholar
• Shaheen, S. A., and A. P. Cohen. 2007. “Growth in Worldwide Carsharing.” Transportation Research Record: Journal of the Transportation Research Board 1992 (1): 81–89. doi:10.3141/1992-10.
   Web of Science ®Google Scholar
• Stasko, T. H., A. B. Buck, and H. Oliver Gao. 2013. “Carsharing in a University Setting: Impacts on Vehicle Ownership, Parking Demand, and Mobility in Ithaca, NY.” Transport Policy 30: 262–268. doi:10.1016/j.tranpol.2013.09.018.
   Web of Science ®Google Scholar
• Wang, X., C. Sun, R. Wang, T. Wei, et al. 2020. “Two-Stage Optimal Scheduling Strategy for Large-Scale Electric Vehicles”. IEEE Access 8: 13821–13832. doi:10.1109/ACCESS.2020.2966825.
   Google Scholar
• Wappelhorst, S., M. Sauer, D. Hinkeldein, A. Bocherding, T. Glaß, et al. 2014. “Potential of Electric Carsharing in Urban and Rural Areas”. Transportation Research Procedia 4: 374–386. doi:10.1016/j.trpro.2014.11.028.
   Google Scholar
• Zoepf, S. M., and D. R. Keith. 2016. “User Decision-making and Technology Choices in the U.S. Carsharing Market.” Transport Policy 51: 150–157. doi:10.1016/j.tranpol.2016.01.010.
   Web of Science ®Google Scholar