Prediction of arrival times of freight traffic on US railroads using support vector regression
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ML can contribute by improving predictions of estimated time of arrival or weight/volume of expected freight.
“ML can contribute by improving predictions of the estimated time of arrival (for example, of freight trains [56]) or the weight or volume of expected freight (for example, for roll-on/roll-off transport-often abbreviated as Ro-Ro [560]).”
From Tackling climate change with machine learning (2023) · cites this paper as [56]
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Cites
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421 · see citing works
Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565 · see citing works
Regularization and Variable Selection Via the Elastic Net
Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957 · see citing works
A Tutorial on Support Vector Machines for Pattern Recognition
Burges, C. J. C. (1998) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009715923555 · Cited by 16,437 · see citing works
A training algorithm for optimal margin classifiers
Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992) · Proceedings of the fifth annual workshop on Computational learning theory · doi:10.1145/130385.130401 · Cited by 11,992 · see citing works
Schölkopf, B., & Smola, A. J. (2001) · The MIT Press eBooks · doi:10.7551/mitpress/4175.001.0001 · Cited by 11,934 · see citing works
Ridge regression: Biased estimation for nonorthogonal problems
Hoerl, A. E., & Kennard, R. W. (1970) · Technometrics · doi:10.1080/00401706.1970.10488634 · Cited by 9,648 · see citing works
Ridge Regression: Biased Estimation for Nonorthogonal Problems
Hoerl, A. E., & Kennard, R. W. (2000) · Technometrics · doi:10.2307/1271436 · Cited by 6,553 · see citing works
Learning With Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Williams, C. K. I. (2003) · Journal of the American Statistical Association · doi:10.1198/jasa.2003.s269 · Cited by 4,390 · see citing works
Dziuda, D. M. (2024) · Cambridge University Press eBooks · doi:10.1017/9781009006767.012 · Cited by 827 · see citing works
Zhang, F., & O’Donnell, L. J. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-815739-8.00007-9 · Cited by 827 · see citing works
Ridge Regression Learning Algorithm in Dual Variables
Saunders, C. J., Gammerman, A., & Vovk, V. (1998) · ePrints Soton (University of Southampton) · Cited by 791 · see citing works
Dynamic Bus Arrival Time Prediction with Artificial Neural Networks
Chien, S., Ding, Y., & Wei, C. (2002) · Journal of Transportation Engineering · doi:10.1061/(asce)0733-947x(2002)128:5(429 · Cited by 362 · see citing works
Models for rail transportation
Assad, A. A. (1980) · Transportation Research Part A General · doi:10.1016/0191-2607(80)90017-5 · Cited by 305 · see citing works
A review of travel time estimation and forecasting for Advanced Traveller Information Systems
Mori, U., Mendiburu, A., Álvarez, M., & Lozano, J. A. (2014) · Transportmetrica A Transport Science · doi:10.1080/23249935.2014.932469 · Cited by 244 · see citing works
Conflict Resolution and Train Speed Coordination for Solving Real-Time Timetable Perturbations
D’Ariano, A., Pranzo, M., & Hansen, I. A. (2007) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2006.888605 · Cited by 236 · 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
Railway traffic control and train scheduling based oninter-train conflict management
Şahin, İ. (1999) · Transportation Research Part B Methodological · doi:10.1016/s0191-2615(99)00004-1 · Cited by 192 · see citing works
Reliability and heterogeneity of railway services
Vromans, M. J. C. M., Dekker, R., & Kroon, L. (2004) · European Journal of Operational Research · doi:10.1016/j.ejor.2004.10.010 · Cited by 180 · see citing works
Punctuality of railway operations and timetable stability analysis
Goverde, R. M. P. (2005) · Research Repository (Delft University of Technology) · Cited by 141 · see citing works
D’Ariano, A., & Pranzo, M. (2008) · Networks and Spatial Economics · doi:10.1007/s11067-008-9088-1 · Cited by 138 · see citing works
Scheduling freight trains traveling on complex networks
Mu, S., & Dessouky, M. M. (2011) · Transportation Research Part B Methodological · doi:10.1016/j.trb.2011.05.021 · Cited by 116 · see citing works
A Structured Model for Rail Line Simulation and Optimization
Petersen, E. R., & Taylor, A. J. (1982) · Transportation Science · doi:10.1287/trsc.16.2.192 · Cited by 111 · see citing works
Statistical estimation of railroad congestion delay
Gorman, M. F. (2008) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2008.08.004 · Cited by 103 · see citing works
Marinov, M. V., & Viegas, J. M. (2010) · Simulation Modelling Practice and Theory · doi:10.1016/j.simpat.2010.08.009 · Cited by 96 · see citing works
Online Data-Driven Adaptive Prediction of Train Event Times
(2014) · doi:10.1109/tits.2014.2347136 · Cited by 95 · see citing works
Real-time scheduling of freight railroads
Kraay, D. R., & Harker, P. T. (1995) · Transportation Research Part B Methodological · doi:10.1016/0191-2615(94)00034-w · Cited by 93 · see citing works
Modeling train movements through complex rail networks
Lu, Q., Dessouky, M. M., & Leachman, R. C. (2004) · ACM Transactions on Modeling and Computer Simulation · doi:10.1145/974734.974737 · Cited by 92 · see citing works
Impact of Train Type Heterogeneity on Single-Track Railway Capacity
Dingler, M. H., Lai, Y.-C. “., & Barkan, C. P. L. (2009) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/2117-06 · Cited by 91 · see citing works
National Rail Freight Infrastructure Capacity and Investment Study
Grenzeback, L. R., Hunt, D. T. E., & Beagan, D. (2007) · Cited by 88 · see citing works
A delay estimation technique for single and double-track railroads
Murali, P., Dessouky, M. M., Ordonez, F. I., et al. (2009) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2009.04.016 · Cited by 86 · see citing works
Two Moments Estimation of the Delay on Single-Track Rail Lines with Scheduled Traffic
Chen, B., & Harker, P. T. (1990) · Transportation Science · doi:10.1287/trsc.24.4.261 · Cited by 64 · see citing works
Railroad capacity tools and methodologies in the U.S. and Europe
Pouryousef, H., Lautala, P. T., & White, T. M. (2015) · 西南交通大学学报：英文版 · doi:10.1007/s40534-015-0069-z · Cited by 62 · see citing works
Modelling delay risks associated with train schedules
Higgins, A., Kozan, E., & Ferreira, L. (1995) · Transportation Planning and Technology · doi:10.1080/03081069508717561 · Cited by 54 · see citing works
Hallowell, S. F., & Harker, P. T. (1998) · Transportation Research Part A Policy and Practice · doi:10.1016/s0965-8564(97)00009-8 · Cited by 48 · see citing works
Data Driven Approaches for Passenger Train Delay Estimation
Wang, R., & Work, D. B. (2015) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2015.94 · Cited by 42 · see citing works
Self-citationImproving train service reliability by applying an effective timetable robustness strategy
Khoshniyat, F., & Peterson, A. (2017) · Journal of Intelligent Transportation Systems · doi:10.1080/15472450.2017.1326114 · Cited by 29 · see citing works
Determining the Causes of Train Delay
Dingler, M. H., Koenig, A., Sogin, S. L., et al. (2010) · Cited by 22 · see citing works
Advanced Prediction of Train Arrival and Crossing Times at Highway-Railroad Grade Crossings
Estes, R. M., & Rilett, L. R. (2000) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/1708-08 · Cited by 15 · see citing works
The State of U.S. Railroads: A Review of Capacity and Performance Data
Weatherford, B. A., Willis, H. H., & Ortiz, D. S. (2008) · RAND Corporation eBooks · doi:10.7249/tr603 · Cited by 12 · see citing works
U.S. and European Freight Railways: The Differences That Matter
Furtado, F. M. B. A. (2013) · Journal of the Transportation Research Forum · doi:10.5399/osu/jtrf.52.2.4174 · Cited by 10 · see citing works
Estimation of run times in a freight rail transportation network
Bonsra, K., & Harbolovic, J. (2012) · Cited by 3 · see citing works
Flagged first, then by citations
Research works printed but not linked: 5
- P. Kecman and R. M. Goverde, "An online railway traffic prediction model," in RailCopenhagen2013: 5th International Conference on Rail- way Operations Modelling and Analysis, Copenhagen, Denmark, 13-15 May 2013. International Association of Railway Operations Research (IAROR), 2013.
- A. H. Lovett, C. T. Dick, and C. P. Barkan, "Determining freight train delay costs on railroad lines in North America," Proceedings of RailTokyo 2015, 2015.
- H. Khadilkar, S. Salsingikar, and S. K. Sinha, "A machine learning ap- proach for scheduling railway networks," in Proceedings of the 7th In- ternational Conference on Railway Operations Modelling and Analysis, RailLille2017. International Association of Railway Operations Re- search (IAROR), April 2017.
- X. Chapuis, "Arrival time prediction using neural networks," in Proceed- ings of the 7th International Conference on Railway Operations Mod- elling and Analysis, RailLille2017. International Association of Railway Operations Research (IAROR), April 2017.
- A. Smola and S. Vishwanathan, Introduction to Machine Learning. Cambridge, United Kingdom: Cambridge University Press, 2008.
Cited by
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Tackling climate change with machine learning
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
- Authors
- Barbour, W., Martinez Mori, J. C., Kuppa, S., & Work, D. B.
- Affiliations
- University of Illinois Urbana-Champaign (US)
- CSX (United States) (US)
- Corresponding author
- Work, Daniel B.
- Venue
- Transportation Research Part C: Emerging Technologies
- Publisher
- Elsevier BV
- Published
- 2018-08
- Volume
- 93
- Pages
- 211-227
- ISSN
- 0968-090X (ISSN-L)
- OpenAlex
- W2742989960
- Cited by
- 116
- References
- 45
- Citations by year
- 2026: 11 · 2025: 19 · 2024: 19 · 2023: 14 · 2022: 24 · 2021: 9 · 2020: 13 · 2019: 5 · 2018: 2
- FWCI
- 5.69
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- Transportation Research Part C Emerging Technologies (publishedVersion)
- Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign) (submittedVersion)
- Funders
- U.S. Department of Transportation
- Office of the Assistant Secretary for Research and Technology
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Railway Systems and Energy Efficiency
- Physical Sciences › Engineering › Building and Construction › Traffic Prediction and Management Techniques
- Social Sciences › Business, Management and Accounting › Strategy and Management › Transport and Economic Policies
- Keywords
- estimated time of arrival prediction, support vector regression, train scheduling, feature engineering, railway capacity
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