Analyzing high speed rail passengers’ train choices based on new online booking data in China
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Sun et al. use SVMs and neural networks for analyzing preferences of high-speed rail customers in China.
“Sun et al. [770] use SVMs and neural networks for analyzing preferences of customers traveling by high speed rail in China.”
From Tackling climate change with machine learning (2023) · cites this paper as [770]
Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421 · see citing works
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918 · see citing works
Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565 · see citing works
Wu, Y., & Vapnik, V. N. (1999) · Technometrics · doi:10.2307/1271368 · Cited by 27,236 · see citing works
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
Cristianini, N., & Shawe-Taylor, J. (2000) · doi:10.1017/cbo9780511801389 · Cited by 13,855 · see citing works
A tutorial on support vector regression
Smola, A. J., & Schölkopf, B. (2004) · Statistics and Computing · doi:10.1023/b:stco.0000035301.49549.88 · Cited by 13,482 · see citing works
An Introduction to Support Vector Machines and Other Kernel‐based Learning Methods
(2001) · Kybernetes · doi:10.1108/k.2001.30.1.103.6 · Cited by 3,358 · see citing works
Revenue Management: Research Overview and Prospects
McGill, J., & Ryzin, G. J. V. (1999) · Transportation Science · doi:10.1287/trsc.33.2.233 · Cited by 1,131 · see citing works
Yield management at American airlines
Smith, B. C., Leimkuhler, J. F., & Darrow, R. M. (1992) · Interfaces · doi:10.1287/inte.22.1.8 · Cited by 697 · see citing works
Introduction to Neural Networks with Java
Jeff, H. T. (2005) · Cited by 641 · see citing works
How many hidden layers and nodes?
Stathakis, D. N. (2009) · International Journal of Remote Sensing · doi:10.1080/01431160802549278 · Cited by 435 · see citing works
Yield management: A tool for capacity‐considered service firms
Kimes, S. E. (1989) · Journal of Operations Management · doi:10.1016/0272-6963(89)90035-1 · Cited by 410 · see citing works
Survey Paper—Airline Yield Management An Overview of Seat Inventory Control
Belobaba, P. P. (1987) · Transportation Science · doi:10.1287/trsc.21.2.63 · Cited by 317 · see citing works
Estimating Primary Demand for Substitutable Products from Sales Transaction Data
Vulcano, G. J., van Ryzin, G., & Ratliff, R. M. (2012) · Operations Research · doi:10.1287/opre.1110.1012 · Cited by 280 · see citing works
Improving rail network velocity: A machine learning approach to predictive maintenance
Li, H., Parikh, D., He, Q., Qian, B., Li, Z., Fang, D., & Hampapur, A. (2014) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2014.04.013 · Cited by 213 · see citing works
Analyzing passenger train arrival delays with support vector regression
Marković, N., Milinković, S., Tikhonov, K. S., & Schonfeld, P. M. (2015) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2015.04.004 · Cited by 197 · see citing works
OM Practice —Choice-Based Revenue Management: An Empirical Study of Estimation and Optimization
Vulcano, G. J., Ryzin, G. J. V., & Chaar, W. (2009) · Manufacturing & Service Operations Management · doi:10.1287/msom.1090.0275 · Cited by 180 · see citing works
Travel Mode Choice Modeling with Support Vector Machines
Zhang, Y., & Xie, Y. (2008) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/2076-16 · Cited by 127 · see citing works
A latent class choice based model system for railway optimal pricing and seat allocation
Hetrakul, P., & Cirillo, C. (2013) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2013.10.005 · Cited by 123 · see citing works
A multi-flight recapture heuristic for estimating unconstrained demand from airline bookings
Ratliff, R. M., Rao, B. V., Narayan, C. P., & Yellepeddi, K. (2008) · Journal of Revenue and Pricing Management · doi:10.1057/rpm.2008.12 · Cited by 96 · see citing works
Weatherford, L. R., & Pölt, S. (2002) · Journal of Revenue and Pricing Management · doi:10.1057/palgrave.rpm.5170027 · Cited by 91 · see citing works
Basu, D., & Hunt, J. D. (2012) · Transportation Research Part A Policy and Practice · doi:10.1016/j.tra.2012.05.010 · Cited by 83 · see citing works
Tortum, A., Yayla, N., & Gökdağ, M. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.07.032 · Cited by 62 · see citing works
Accommodating taste heterogeneity in railway passenger choice models based on internet booking data
Hetrakul, P., & Cirillo, C. (2013) · Journal of Choice Modelling · doi:10.1016/j.jocm.2013.04.003 · Cited by 46 · see citing works
McGill, J. (1995) · Annals of Operations Research · doi:10.1007/bf02031947 · Cited by 42 · see citing works
High-speed railway scheduling based on user preferences
Espinosa-Aranda, J. L., Garćıa-Ródenas, R., Ramírez-Flores, M. D. C., López-Garćıa, M. L., & Angulo, E. (2015) · European Journal of Operational Research · doi:10.1016/j.ejor.2015.05.052 · Cited by 41 · see citing works
Revenue Management in Railroad Applications
Kraft, E. R., Srikar, B. N., & Phillips, R. L. (2000) · RePEc: Research Papers in Economics · doi:10.22004/ag.econ.317551 · Cited by 22 · see citing works
Seat inventory control methods for Chinese passenger railways
Bao, Y., Liu, J., Ma, M., & Meng, L. (2014) · Journal of Central South University · doi:10.1007/s11771-014-2109-y · Cited by 12 · see citing works
Advance purchase behaviors of air tickets
Chiou, Y., & Liu, C.-H. (2016) · Journal of Air Transport Management · doi:10.1016/j.jairtraman.2016.07.010 · Cited by 12 · see citing works
Discrete choice model for Amtrak Acela Express revenue management
Cirillo, C., Hetrakul, P., & Toobaie, S. (2011) · Journal of Revenue and Pricing Management · doi:10.1057/rpm.2011.28 · Cited by 11 · see citing works
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Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
Choice modelling in the age of machine learning - Discussion paper
van Cranenburgh, S., Wang, S., Vij, A., Pereira, F. C., & Walker, J. L. (2021) · Journal of Choice Modelling · doi:10.1016/j.jocm.2021.100340 · Cited by 155 · see citing works
Wear mechanism of Cu-based brake pad for high-speed train braking at speed of 380 km/h
Xiao, J., Xiao, S.-X., Chen, J., & Zhang, C. (2020) · Tribology International · doi:10.1016/j.triboint.2020.106357 · Cited by 130 · see citing works
Ma, X., Luan, C., Fan, S., Deng, J., Zhang, L., & Cheng, L. (2020) · Tribology International · doi:10.1016/j.triboint.2020.106686 · Cited by 79 · see citing works
Self-citationA review on artificial intelligence in high-speed rail
Yin, M., Li, K., & Cheng, X. (2020) · Transportation Safety and Environment · doi:10.1093/tse/tdaa022 · Cited by 70 · see citing works
Investigation on braking performance and wear mechanism of full-carbon/ceramic braking pairs
Ma, X., Fan, S., Sun, H., Luan, C., Deng, J., Zhang, L., & Cheng, L. (2019) · Tribology International · doi:10.1016/j.triboint.2019.105981 · Cited by 60 · see citing works
Self-citationExploring the aspects of MaaS adoption based on college students’ preferences
Kriswardhana, W., & Esztergár‐Kiss, D. (2023) · Transport Policy · doi:10.1016/j.tranpol.2023.03.018 · Cited by 58 · see citing works
Why did you predict that? Towards explainable artificial neural networks for travel demand analysis
Alwosheel, A., van Cranenburgh, S., & Chorus, C. G. (2021) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2021.103143 · Cited by 38 · see citing works
Kamandanipour, K., Nasiri, M. M., Konur, D., & Yakhchali, S. H. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113568 · Cited by 33 · see citing works
Lyratzakis, A., Tsompanakis, Y., & Psarropoulos, P. N. (2019) · Transportation Geotechnics · doi:10.1016/j.trgeo.2019.100312 · Cited by 32 · see citing works
Heterogeneity in the transfer time of air-rail intermodal passengers based on ticket booking data
Yuan, Y., Yang, M., Feng, T., Ma, Y., Ren, Y., & Ruan, X. (2022) · Transportation Research Part A Policy and Practice · doi:10.1016/j.tra.2022.09.022 · Cited by 20 · see citing works
Airborne antibiotic resistome and human health risk in railway stations during COVID-19 pandemic
Bai, H., He, L.-Y., Gao, F.-Z., Wu, D.-L., Yao, K.-S., Zhang, M., Jia, W., He, L.-X., Zou, H., Yao, M., et al. (2023) · Environment International · doi:10.1016/j.envint.2023.107784 · Cited by 20 · see citing works
Model of Price Optimization as a Part of Hotel Revenue Management—Stochastic Approach
Petříček, M., Chalupa, Š., & Melas, D. (2021) · Mathematics · doi:10.3390/math9131552 · Cited by 18 · see citing works
Learning-based dynamic ticket pricing for passenger railway service providers
Kamandanipour, K., Yakhchali, S. H., & Tavakkoli-Moghaddam, R. R. (2022) · Engineering Optimization · doi:10.1080/0305215x.2022.2030324 · Cited by 16 · see citing works
Liu, L., Liu, Y., & Ye, X. (2024) · Transportation Letters · doi:10.1080/19427867.2024.2327805 · Cited by 16 · see citing works
Zhang, P., Wei, D., Ding, X., Zhang, L., & Qu, X. (2024) · Engineering Failure Analysis · doi:10.1016/j.engfailanal.2024.109033 · Cited by 14 · see citing works
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Pan, H., Ya, G., Shen, Q., Moudon, A. V., Tuo, J., & Habib, K. N. (2023) · Transport Policy · doi:10.1016/j.tranpol.2023.09.011 · Cited by 13 · see citing works
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Guan, X., Qin, J., Mao, C., & Zhou, W. (2023) · Mathematics · doi:10.3390/math11040857 · Cited by 11 · see citing works
Su, H., Peng, S., Deng, L., Xu, W., & Zeng, Q. (2021) · Mathematical Problems in Engineering · doi:10.1155/2021/8420206 · Cited by 11 · see citing works
Smeele, N. V. R., Chorus, C. G., Schermer, M., & de Bekker-Grob, E. W. (2023) · Social Science & Medicine · doi:10.1016/j.socscimed.2023.115910 · Cited by 10 · see citing works
Zhou, W., Zou, Z., Chai, N., & Xu, G. (2023) · Mathematics · doi:10.3390/math11061412 · Cited by 9 · see citing works
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Schmid, B., Bécker, F., Molloy, J., Axhausen, K. W., Lüdering, J., Hagen, J., & Blome, A. (2022) · Journal of Rail Transport Planning & Management · doi:10.1016/j.jrtpm.2022.100320 · Cited by 9 · see citing works
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van Cranenburgh, S., Wang, S., Vij, A., Pereira, F. C., & Walker, J. L. (2021) · arXiv (Cornell University) · Cited by 9 · see citing works
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Wen, T., Wang, J., Liu, L., Cai, B., & Roberts, C. (2025) · Fundamental Research · doi:10.1016/j.fmre.2025.07.012 · Cited by 7 · see citing works
Chen, W., Zheng, B., Liu, J., Li, L., & Ren, X. (2021) · Electronics · doi:10.3390/electronics10243179 · Cited by 7 · see citing works
Chen, S., Sun, Y., Zhao, T., Jia, M., & Tang, T. (2024) · Natural Hazards Review · doi:10.1061/nhrefo.nheng-1976 · Cited by 6 · see citing works
Self-citationEstimation of Time‐Varying Passenger Demand for High Speed Rail System
Wei, T., Shi, F., & Xu, G. (2019) · Complexity · doi:10.1155/2019/1568941 · Cited by 6 · see citing works
Wen, K., Xie, J., Chen, A., Wong, S. C., Zhan, S., Lo, S., & Qiang, L. (2021) · Transportmetrica A Transport Science · doi:10.1080/23249935.2021.1912206 · Cited by 6 · see citing works
Yi, Z., Liu, X. C., Wei, R., & Grubesic, T. H. (2020) · Journal of Transportation Engineering Part A Systems · doi:10.1061/jtepbs.0000486 · Cited by 5 · see citing works
Advancing a major US airline’s practice in flight-level checked baggage prediction
Chen, S., Park, C., Guo, Q., & Sun, Y. (2024) · Intelligent Transportation Infrastructure · doi:10.1093/iti/liae001 · Cited by 4 · see citing works
Self-citationModelling the Passenger Choice Behaviour of Buying High-Speed Railway Tickets
Yan, Z., Jian, M., Xiaojuan, L. I., & Jinxin, C. A. O. (2022) · PROMET - Traffic&Transportation · doi:10.7307/ptt.v34i3.3942 · Cited by 4 · see citing works
Sun, H., Jing, P., Fu, X., & Xu, J. (2025) · Cities · doi:10.1016/j.cities.2025.106536 · Cited by 2 · see citing works
Mobility-as-a-Service (MaaS) and High-Speed Rail Operators: Do Not Let the Train Pass!
Moyano, A., Tejero-Beteta, C., & Sánchez‐Cambronero, S. (2023) · Sustainability · doi:10.3390/su15118474 · Cited by 2 · see citing works
Hu, H., Yue, Y., Fu, H., & Li, J. (2024) · Transportation Research Part A Policy and Practice · doi:10.1016/j.tra.2024.104154 · Cited by 2 · see citing works
Li, J., Peng, Q., & Wen, C. (2023) · Journal of Advanced Transportation · doi:10.1155/2023/8923716 · Cited by 2 · see citing works
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Su, Z., Zhang, P., Song, X., & Li, Y. (2025) · Applied Sciences · doi:10.3390/app15105269 · Cited by 1 · see citing works
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Shao, Y., & Ma, M. (2021) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-030-81007-8_73 · Cited by 1 · see citing works
A novel parallel hybrid model for forecasting the stock price
Zhu, C., Xu, Z., & Han, L. (2022) · International Conference on Machine Learning and Natural Language Processing (MLNLP) · doi:10.1145/3578741.3578774
Qasim, S., & Farooquie, J. A. (2023) · Research Square · doi:10.21203/rs.3.rs-3660620/v1
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Catenazzo, G. (2023) · International Scientific Conference on Economics, Management and Information Technologies (ICEMIT) · doi:10.46793/icemit23.001c
Abdullah, A. S., Kailash, P. J. S., Ramesh, D., & Aravind Shankar, R. (2023) · International Conference on Recent Trends in Advance Computing (ICRTAC) · doi:10.1109/icrtac59277.2023.10480805
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- Authors
- Sun, Y., Jiang, Z., Gu, J., Zhou, M., Li, Y., & Zhang, L.
- Affiliations
- Florida State University (US)
- Tongji University (CN)
- University of Maryland, College Park (US)
- Corresponding author
- Jiang, Zhibin
- Venue
- Transportation Research Part C: Emerging Technologies
- Publisher
- Elsevier BV
- Published
- 2018-12
- Volume
- 97
- Pages
- 96-113
- ISSN
- 0968-090X (ISSN-L)
- OpenAlex
- W2898530914
- Cited by
- 50
- References
- 29
- Citations by year
- 2026: 6 · 2025: 5 · 2024: 5 · 2023: 11 · 2022: 7 · 2021: 8 · 2020: 5 · 2019: 3
- FWCI
- 4.95
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Transportation Research Part C Emerging Technologies (publishedVersion)
- Funders
- National Natural Science Foundation of China (61473210)
- Fundamental Research Funds for the Central Universities
- China Railway Corporation (2016X005-G)
- Topics
- Social Sciences › Transportation › Transportation Planning and Optimization
- Social Sciences › Economics, Econometrics and Finance › General Economics, Econometrics and Finance › Aviation Industry Analysis and Trends
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Railway Systems and Energy Efficiency
- Keywords
- high-speed rail, support vector regression, artificial neural networks, load factor
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