The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Predicting motor vehicle crashes using Support Vector Machine models
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Citations
Cites
(1999) · doi:10.1162/089976600300015565 · Cited by 2,799
A GA-based feature selection and parameters optimizationfor support vector machines
Huang, C.-L., & Wang, C.-J. (2006) · Expert Systems with Applications · doi:10.1016/j.eswa.2005.09.024 · Cited by 1,363
Miaou, S.-P. (1994) · Accident Analysis & Prevention · doi:10.1016/0001-4575(94)90038-8 · Cited by 770
Training v-Support Vector Classifiers: Theory and Algorithms
Chang, C.-C., & Lin, C.-J. (2001) · Neural Computation · doi:10.1162/089976601750399335 · Cited by 499
Ma, J., Kockelman, K. M., & Damien, P. (2008) · Accident Analysis & Prevention · doi:10.1016/j.aap.2007.11.002 · Cited by 394
Modeling accident frequencies as zero-altered probability processes: An empirical inquiry
Shankar, V., Milton, J., & Mannering, F. (1997) · Accident Analysis & Prevention · doi:10.1016/s0001-4575(97)00052-3 · Cited by 385
Lord, D. (2006) · Accident Analysis & Prevention · doi:10.1016/j.aap.2006.02.001 · Cited by 384
Self-citationMultivariate Poisson-Lognormal Models for Jointly Modeling Crash Frequency by Severity
Park, E. S., & Lord, D. (2007) · Transportation Research Record: Journal of the Transportation Research Board · doi:10.3141/2019-01 · Cited by 296
Self-citationLord, D., Guikema, S. D., & Geedipally, S. R. (2008) · Accident Analysis & Prevention · doi:10.1016/j.aap.2007.12.003 · Cited by 260
Self-citationPredicting motor vehicle collisions using Bayesian neural network models: An empirical analysis
Xie, Y., Lord, D., & Zhang, Y. (2007) · Accident Analysis & Prevention · doi:10.1016/j.aap.2006.12.014 · Cited by 253
Self-citation
Cited by
Lord, D., & Mannering, F. (2010) · Transportation Research Part A: Policy and Practice · doi:10.1016/j.tra.2010.02.001 · Cited by 1,759
Self-citationSupport Vector Machines Used to Estimate the Battery State of Charge
Álvarez Antón, J. C., García Nieto, P. J., Blanco Viejo, C., & Vilán Vilán, J. A. (2013) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2013.2243918 · Cited by 512
Mannering, F., Bhat, C. R., Shankar, V., & Abdel-Aty, M. (2020) · Analytic Methods in Accident Research · doi:10.1016/j.amar.2020.100113 · Cited by 367
Applications of machine learning methods for engineering risk assessment – A review
Hegde, J., & Rokseth, B. (2020) · Safety Science · doi:10.1016/j.ssci.2019.09.015 · Cited by 363
Utilizing support vector machine in real-time crash risk evaluation
Yu, R., & Abdel-Aty, M. (2013) · Accident Analysis & Prevention · doi:10.1016/j.aap.2012.11.027 · Cited by 317
Metadata
- Authors
- Li, X., Lord, D., Zhang, Y., & Xie, Y.
- Venue
- Accident Analysis & Prevention
- Publisher
- Elsevier BV
- Published
- 2008-07
- Volume
- 40
- Issue
- 4
- Pages
- 1611-1618
- ISSN
- 0001-4575 (ISSN-L)
- OpenAlex
- W1580407410
- Cited by
- 335
- References
- 22
- FWCI
- 39.02
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Predicting motor vehicle crashes using Support Vector Machine models. https://app.cochat.ai/papers/10.1016/j.aap.2008.04.010
- The paper
- Li, X., Lord, D., Zhang, Y., & Xie, Y. (2008). Predicting motor vehicle crashes using Support Vector Machine models. Accident Analysis & Prevention. https://doi.org/10.1016/j.aap.2008.04.010
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
