Handling Imbalanced Data in Road Crash Severity Prediction by Machine Learning Algorithms
Crash severity is undoubtedly a fundamental aspect of a crash event. Although machine learning algorithms for predicting crash severity have recently gained interest by the academic community, there is a significant trend towards neglecting the fact that crash datasets are acutely imbalanced. Overlo...
Gespeichert in:
| Veröffentlicht in: | Infrastructures (Basel) Jg. 5; H. 7; S. 61 |
|---|---|
| Hauptverfasser: | , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Basel
MDPI AG
20.07.2020
|
| Schlagworte: | |
| ISSN: | 2412-3811, 2412-3811 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Crash severity is undoubtedly a fundamental aspect of a crash event. Although machine learning algorithms for predicting crash severity have recently gained interest by the academic community, there is a significant trend towards neglecting the fact that crash datasets are acutely imbalanced. Overlooking this fact generally leads to weak classifiers for predicting the minority class (crashes with higher severity). In this paper, in order to handle imbalanced accident datasets and provide a better prediction for the minority class, the random undersampling the majority class (RUMC) technique is used. By employing an imbalanced and a RUMC-based balanced training set, we propose the calibration, validation, and evaluation of four different crash severity predictive models, including random tree, k-nearest neighbor, logistic regression, and random forest. Accuracy, true positive rate (recall), false positive rate, true negative rate, precision, F1-score, and the confusion matrix have been calculated to assess the performance. Outcomes show that RUMC-based models provide an enhancement in the reliability of the classifiers for detecting fatal crashes and those causing injury. Indeed, in imbalanced models, the true positive rate for predicting fatal crashes and those causing injury spans from 0% (logistic regression) to 18.3% (k-nearest neighbor), while for the RUMC-based models, it spans from 52.5% (RUMC-based logistic regression) to 57.2% (RUMC-based k-nearest neighbor). Organizations and decision-makers could make use of RUMC and machine learning algorithms in predicting the severity of a crash occurrence, managing the present, and planning the future of their works. |
|---|---|
| AbstractList | Crash severity is undoubtedly a fundamental aspect of a crash event. Although machine learning algorithms for predicting crash severity have recently gained interest by the academic community, there is a significant trend towards neglecting the fact that crash datasets are acutely imbalanced. Overlooking this fact generally leads to weak classifiers for predicting the minority class (crashes with higher severity). In this paper, in order to handle imbalanced accident datasets and provide a better prediction for the minority class, the random undersampling the majority class (RUMC) technique is used. By employing an imbalanced and a RUMC-based balanced training set, we propose the calibration, validation, and evaluation of four different crash severity predictive models, including random tree, k-nearest neighbor, logistic regression, and random forest. Accuracy, true positive rate (recall), false positive rate, true negative rate, precision, F1-score, and the confusion matrix have been calculated to assess the performance. Outcomes show that RUMC-based models provide an enhancement in the reliability of the classifiers for detecting fatal crashes and those causing injury. Indeed, in imbalanced models, the true positive rate for predicting fatal crashes and those causing injury spans from 0% (logistic regression) to 18.3% (k-nearest neighbor), while for the RUMC-based models, it spans from 52.5% (RUMC-based logistic regression) to 57.2% (RUMC-based k-nearest neighbor). Organizations and decision-makers could make use of RUMC and machine learning algorithms in predicting the severity of a crash occurrence, managing the present, and planning the future of their works. |
| Author | Fiorentini, Nicholas Losa, Massimo |
| Author_xml | – sequence: 1 givenname: Nicholas orcidid: 0000-0002-8769-8610 surname: Fiorentini fullname: Fiorentini, Nicholas – sequence: 2 givenname: Massimo surname: Losa fullname: Losa, Massimo |
| BookMark | eNqFkVuLFDEQhYOs4LruX5CAz6O5dnfAl2W87MCI4uXBp1CdVGYy9CRrklmYf2-PI7KI4FMVxTlfFaeekouUExLynLOXUhr2KqZQoLZycO1QsGrWM9bxR-RSKC4WcuD84kH_hFzXumOMCTb0w8AvyfdbSH6KaUNX-xEmSA49fQMNaEz0cwZPlzN_S7_gPZbYjvRTQR9diznR8Ug_gNvGhHSNUNKJcjNt8qzb7usz8jjAVPH6d70i3969_bq8Xaw_vl8tb9YLJ03fFqDNKJ3pNPPBBzkgY04DRxSux87AqIORTCpujGA6KONRKa-56KXWyLi8Iqsz12fY2bsS91CONkO0vwa5bCyUFt2EdpQeROc8jMIoNWcQNAySz-vHTnYqzKwXZ9ZdyT8OWJvd5UNJ8_lWKNH1fNCczarurHIl11ow_NnKmT19xf77K7Px9V9GFxucsmwF4vQ_-0-DYZtD |
| CitedBy_id | crossref_primary_10_1007_s12530_023_09563_4 crossref_primary_10_1016_j_aap_2022_106769 crossref_primary_10_3390_computers13020049 crossref_primary_10_1109_ACCESS_2024_3366990 crossref_primary_10_1371_journal_pone_0269022 crossref_primary_10_3390_ijerph18041966 crossref_primary_10_1016_j_aap_2021_106240 crossref_primary_10_3390_sym12101620 crossref_primary_10_1016_j_amar_2025_100405 crossref_primary_10_1016_j_engappai_2024_109086 crossref_primary_10_1016_j_eswa_2023_121118 crossref_primary_10_1016_j_jth_2023_101671 crossref_primary_10_1080_17457300_2021_1928233 crossref_primary_10_1142_S0218126625300090 crossref_primary_10_1680_jtran_24_00071 crossref_primary_10_1038_s41598_022_25361_5 crossref_primary_10_1007_s41062_024_01626_y crossref_primary_10_3103_S1060992X23040082 crossref_primary_10_1016_j_compbiomed_2022_106393 crossref_primary_10_1371_journal_pone_0262941 crossref_primary_10_3233_IDA_216398 crossref_primary_10_1080_19439962_2024_2311408 crossref_primary_10_1177_03611981241239962 crossref_primary_10_1177_03611981221090519 crossref_primary_10_3390_info11120557 crossref_primary_10_1371_journal_pone_0281901 crossref_primary_10_1016_j_aap_2021_106496 crossref_primary_10_3390_su12155972 crossref_primary_10_1038_s41598_025_08935_x crossref_primary_10_3390_computers14050186 crossref_primary_10_1371_journal_pone_0255828 crossref_primary_10_3390_ijerph17207466 crossref_primary_10_1007_s00521_022_07769_2 crossref_primary_10_1080_12265934_2024_2346166 crossref_primary_10_3390_app122211354 crossref_primary_10_1080_17457300_2023_2202660 crossref_primary_10_1177_03611981221111367 crossref_primary_10_1016_j_aap_2021_106094 crossref_primary_10_1016_j_aap_2021_106090 crossref_primary_10_1080_03081060_2023_2177651 crossref_primary_10_1007_s40999_025_01108_x crossref_primary_10_1080_03081060_2023_2216202 crossref_primary_10_1371_journal_pone_0314133 crossref_primary_10_1109_ACCESS_2025_3571837 crossref_primary_10_1016_j_procs_2024_05_192 crossref_primary_10_3390_math13020310 crossref_primary_10_1016_j_trpro_2023_11_051 crossref_primary_10_3390_su15139878 crossref_primary_10_3390_app12020856 crossref_primary_10_1007_s12145_024_01649_0 crossref_primary_10_3390_info15030145 crossref_primary_10_1007_s00138_022_01284_z crossref_primary_10_1016_j_aap_2021_106149 crossref_primary_10_1016_j_eswa_2024_124602 crossref_primary_10_1177_03611981211033278 crossref_primary_10_1007_s13177_024_00440_1 crossref_primary_10_1016_j_heliyon_2023_e21187 crossref_primary_10_1080_13588265_2022_2028471 crossref_primary_10_3390_ijerph17207598 crossref_primary_10_14254_jsdtl_2022_7_2_1 crossref_primary_10_1016_j_aap_2023_107271 crossref_primary_10_1016_j_jth_2025_102022 crossref_primary_10_1016_j_jsr_2021_12_007 crossref_primary_10_3390_app15010253 crossref_primary_10_1177_03611981251351888 crossref_primary_10_1080_19439962_2025_2554089 crossref_primary_10_3103_S1060992X24700103 crossref_primary_10_1109_TITS_2022_3207798 crossref_primary_10_3390_s21103377 crossref_primary_10_1177_03611981231171151 crossref_primary_10_4271_09_13_01_0006 crossref_primary_10_1080_13588265_2022_2074643 crossref_primary_10_3390_rs14143275 crossref_primary_10_3390_futuretransp2040052 crossref_primary_10_1016_j_chaos_2023_113245 crossref_primary_10_3390_ijerph192013693 crossref_primary_10_1080_08839514_2025_2452675 crossref_primary_10_1186_s12889_022_14678_5 crossref_primary_10_1155_2022_1438190 crossref_primary_10_3390_app15094793 crossref_primary_10_3390_computers11050080 crossref_primary_10_1139_cjce_2023_0503 crossref_primary_10_1080_17457300_2022_2075397 crossref_primary_10_1186_s12544_024_00686_6 crossref_primary_10_3390_app12136368 crossref_primary_10_3390_su15032352 crossref_primary_10_3390_electronics14173377 crossref_primary_10_3390_su151310668 crossref_primary_10_1007_s00521_024_10939_z crossref_primary_10_1016_j_amar_2025_100372 |
| Cites_doi | 10.1002/for.2425 10.1080/15389588.2017.1363891 10.1109/TIT.1967.1053964 10.1109/ACCESS.2018.2874979 10.1016/j.aap.2006.04.009 10.1016/j.jth.2017.01.009 10.1037/h0072400 10.1016/j.aap.2010.10.002 10.1016/j.physa.2018.10.060 10.1007/978-3-642-25832-9_24 10.1016/j.aap.2019.01.007 10.1016/j.aap.2019.105274 10.1080/13588265.2019.1616885 10.1111/mice.12485 10.1016/j.trc.2017.11.014 10.1109/TITS.2020.2994126 10.3390/app7060476 10.1016/j.aap.2016.02.011 10.1007/978-1-4899-7993-3_565-2 10.1007/s00521-019-04695-8 10.1109/LT.2018.8368509 10.1109/JEEIT.2019.8717393 10.1177/0361198119841571 10.1016/j.aap.2011.08.016 10.3141/1746-02 10.1145/1656274.1656278 10.1016/j.aap.2018.10.016 10.1609/aaai.v30i1.10011 10.1016/j.aap.2008.04.010 10.1177/0361198119845899 10.1016/j.aap.2008.09.009 10.1016/j.ssci.2019.07.008 10.1016/j.aap.2017.08.008 10.1016/j.neucom.2013.05.059 10.1145/1007730.1007735 10.3233/IDA-2002-6504 10.1111/0885-9507.00064 10.1016/j.aap.2019.105371 10.1016/j.eswa.2013.05.027 10.1613/jair.953 10.2307/2280041 10.1109/ACCESS.2019.2903319 10.1016/j.aap.2016.08.004 10.1016/j.aap.2019.02.008 10.1016/j.aap.2005.03.019 10.1007/978-3-030-34069-8_17 |
| ContentType | Journal Article |
| Copyright | 2020. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: 2020. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | AAYXX CITATION 8FE 8FG ABUWG AFKRA ARAPS AZQEC BENPR BGLVJ CCPQU DWQXO HCIFZ P5Z P62 PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS DOA |
| DOI | 10.3390/infrastructures5070061 |
| DatabaseName | CrossRef ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials Local Electronic Collection Information ProQuest Central Technology collection ProQuest One Community College ProQuest Central SciTech Premium Collection Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef Publicly Available Content Database Advanced Technologies & Aerospace Collection Technology Collection ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest One Academic Eastern Edition ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Technology Collection ProQuest SciTech Collection ProQuest Central China ProQuest Central Advanced Technologies & Aerospace Database ProQuest One Applied & Life Sciences ProQuest One Academic UKI Edition ProQuest Central Korea ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New) |
| DatabaseTitleList | Publicly Available Content Database CrossRef |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Open Access Full Text url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: PIMPY name: ProQuest Publicly Available Content url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 2412-3811 |
| ExternalDocumentID | oai_doaj_org_article_b3da26cdab2944208f5a8313c9b6364f 10_3390_infrastructures5070061 |
| GroupedDBID | 8FE 8FG AADQD AAFWJ AAYXX ADBBV AFFHD AFKRA AFPKN AFZYC ALMA_UNASSIGNED_HOLDINGS ARAPS BCNDV BENPR BGLVJ CCPQU CITATION GROUPED_DOAJ HCIFZ MODMG M~E OK1 P62 PHGZM PHGZT PIMPY PQGLB PROAC ABUWG AZQEC DWQXO PKEHL PQEST PQQKQ PQUKI PRINS |
| ID | FETCH-LOGICAL-c397t-a59b3c9650dfdf38e00c5a1ee2c7e69ab5f93034199205f49de44d5127355e013 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 102 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000623637800009&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2412-3811 |
| IngestDate | Fri Oct 03 12:51:12 EDT 2025 Sun Nov 09 08:27:15 EST 2025 Tue Nov 18 22:52:07 EST 2025 Sat Nov 29 07:15:38 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 7 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c397t-a59b3c9650dfdf38e00c5a1ee2c7e69ab5f93034199205f49de44d5127355e013 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0002-8769-8610 |
| OpenAccessLink | https://doaj.org/article/b3da26cdab2944208f5a8313c9b6364f |
| PQID | 2426718510 |
| PQPubID | 2055405 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_b3da26cdab2944208f5a8313c9b6364f proquest_journals_2426718510 crossref_primary_10_3390_infrastructures5070061 crossref_citationtrail_10_3390_infrastructures5070061 |
| PublicationCentury | 2000 |
| PublicationDate | 2020-07-20 |
| PublicationDateYYYYMMDD | 2020-07-20 |
| PublicationDate_xml | – month: 07 year: 2020 text: 2020-07-20 day: 20 |
| PublicationDecade | 2020 |
| PublicationPlace | Basel |
| PublicationPlace_xml | – name: Basel |
| PublicationTitle | Infrastructures (Basel) |
| PublicationYear | 2020 |
| Publisher | MDPI AG |
| Publisher_xml | – name: MDPI AG |
| References | Li (ref_11) 2008; 40 (ref_23) 2013; 40 Theofilatos (ref_18) 2019; 2673 ref_58 ref_57 Mosteller (ref_55) 1968; Volume 2 ref_56 ref_10 Tang (ref_8) 2019; 132 ref_53 ref_52 ref_51 ref_19 Chawla (ref_42) 2002; 16 Singh (ref_6) 2018; 171 Tang (ref_32) 2019; 122 Cover (ref_50) 1967; 13 ref_59 Iranitalab (ref_28) 2017; 108 Xiao (ref_16) 2019; 517 Wang (ref_14) 2019; 124 Delen (ref_15) 2017; 4 Hall (ref_46) 2009; 11 ref_25 Harb (ref_13) 2009; 41 Cateni (ref_2) 2014; 135 ref_21 ref_20 Haleem (ref_12) 2011; 43 ref_29 Li (ref_31) 2012; 45 Zhang (ref_9) 2020; 35 Larson (ref_54) 1931; 22 ref_35 ref_34 Mokhtarimousavi (ref_22) 2019; 2673 ref_30 Zhang (ref_5) 1997; 12 ref_39 ref_38 ref_37 ref_47 Chen (ref_26) 2016; 90 ref_45 ref_44 ref_43 ref_41 ref_40 ref_1 Chang (ref_24) 2006; 38 Wu (ref_36) 2018; 19 ref_3 ref_49 ref_48 Laaha (ref_17) 2019; 127 ref_4 Singh (ref_7) 2016; 96 Alkheder (ref_27) 2017; 36 Krishnaveni (ref_33) 2011; 23 |
| References_xml | – volume: 36 start-page: 100 year: 2017 ident: ref_27 article-title: Severity Prediction of Traffic Accident Using an Artificial Neural Network publication-title: J. Forecast. doi: 10.1002/for.2425 – volume: 19 start-page: 179 year: 2018 ident: ref_36 article-title: An evaluation scheme for assessing the effectiveness of intersection movement assist (IMA) on improving traffic safety publication-title: Traffic Inj. Prev. doi: 10.1080/15389588.2017.1363891 – volume: 13 start-page: 21 year: 1967 ident: ref_50 article-title: Nearest Neighbor Pattern Classification publication-title: IEEE Trans. Inf. Theory doi: 10.1109/TIT.1967.1053964 – ident: ref_30 doi: 10.1109/ACCESS.2018.2874979 – volume: 38 start-page: 1019 year: 2006 ident: ref_24 article-title: Analysis of traffic injury severity: An application of non-parametric classification tree techniques publication-title: Accid. Anal. Prev. doi: 10.1016/j.aap.2006.04.009 – volume: 4 start-page: 118 year: 2017 ident: ref_15 article-title: Investigating injury severity risk factors in automobile crashes with predictive analytics and sensitivity analysis methods publication-title: J. Transp. Heal. doi: 10.1016/j.jth.2017.01.009 – volume: 22 start-page: 45 year: 1931 ident: ref_54 article-title: The shrinkage of the coefficient of multiple correlation publication-title: J. Educ. Psychol. doi: 10.1037/h0072400 – volume: 43 start-page: 461 year: 2011 ident: ref_12 article-title: Analyzing angle crashes at unsignalized intersections using machine learning techniques publication-title: Accid. Anal. Prev. doi: 10.1016/j.aap.2010.10.002 – ident: ref_1 – volume: Volume 2 start-page: 80 year: 1968 ident: ref_55 article-title: Data Analysis, Including Statistics publication-title: The Handbook of Social Psychology – volume: 517 start-page: 29 year: 2019 ident: ref_16 article-title: SVM and KNN ensemble learning for traffic incident detection publication-title: Phys. A Stat. Mech. its Appl. doi: 10.1016/j.physa.2018.10.060 – ident: ref_49 doi: 10.1007/978-3-642-25832-9_24 – volume: 124 start-page: 180 year: 2019 ident: ref_14 article-title: Expressway crash risk prediction using back propagation neural network: A brief investigation on safety resilience publication-title: Accid. Anal. Prev. doi: 10.1016/j.aap.2019.01.007 – volume: 132 start-page: 105274 year: 2019 ident: ref_8 article-title: Application of a model-based recursive partitioning algorithm to predict crash frequency publication-title: Accid. Anal. Prev. doi: 10.1016/j.aap.2019.105274 – ident: ref_29 doi: 10.1080/13588265.2019.1616885 – ident: ref_52 – ident: ref_48 – volume: 35 start-page: 258 year: 2020 ident: ref_9 article-title: An ensemble machine learning-based modeling framework for analysis of traffic crash frequency publication-title: Comput. Civ. Infrastruct. Eng. doi: 10.1111/mice.12485 – ident: ref_41 – volume: 23 start-page: 40 year: 2011 ident: ref_33 article-title: A perspective analysis of traffic accident using data mining techniques publication-title: Int. J. Comput. Appl. – ident: ref_39 doi: 10.1016/j.trc.2017.11.014 – ident: ref_35 doi: 10.1109/TITS.2020.2994126 – ident: ref_45 – ident: ref_59 doi: 10.3390/app7060476 – volume: 90 start-page: 128 year: 2016 ident: ref_26 article-title: Investigating driver injury severity patterns in rollover crashes using support vector machine models publication-title: Accid. Anal. Prev. doi: 10.1016/j.aap.2016.02.011 – ident: ref_56 doi: 10.1007/978-1-4899-7993-3_565-2 – ident: ref_10 doi: 10.1007/s00521-019-04695-8 – ident: ref_20 doi: 10.1109/LT.2018.8368509 – ident: ref_21 doi: 10.1109/JEEIT.2019.8717393 – ident: ref_53 – volume: 2673 start-page: 169 year: 2019 ident: ref_18 article-title: Comparing Machine Learning and Deep Learning Methods for Real-Time Crash Prediction publication-title: Transp. Res. Rec. doi: 10.1177/0361198119841571 – volume: 45 start-page: 478 year: 2012 ident: ref_31 article-title: Using support vector machine models for crash injury severity analysis publication-title: Accid. Anal. Prev. doi: 10.1016/j.aap.2011.08.016 – ident: ref_3 – ident: ref_25 doi: 10.3141/1746-02 – volume: 11 start-page: 10 year: 2009 ident: ref_46 article-title: The WEKA data mining software publication-title: ACM SIGKDD Explor. Newsl. doi: 10.1145/1656274.1656278 – ident: ref_47 – volume: 122 start-page: 226 year: 2019 ident: ref_32 article-title: Crash injury severity analysis using a two-layer Stacking framework publication-title: Accid. Anal. Prev. doi: 10.1016/j.aap.2018.10.016 – ident: ref_19 doi: 10.1609/aaai.v30i1.10011 – volume: 40 start-page: 1611 year: 2008 ident: ref_11 article-title: Predicting motor vehicle crashes using Support Vector Machine models publication-title: Accid. Anal. Prev. doi: 10.1016/j.aap.2008.04.010 – volume: 2673 start-page: 680 year: 2019 ident: ref_22 article-title: Improved Support Vector Machine Models for Work Zone Crash Injury Severity Prediction and Analysis publication-title: Res. Artic. Transp. Res. Rec. doi: 10.1177/0361198119845899 – volume: 41 start-page: 98 year: 2009 ident: ref_13 article-title: Exploring precrash maneuvers using classification trees and random forests publication-title: Accid. Anal. Prev. doi: 10.1016/j.aap.2008.09.009 – ident: ref_34 doi: 10.1016/j.ssci.2019.07.008 – volume: 108 start-page: 27 year: 2017 ident: ref_28 article-title: Comparison of four statistical and machine learning methods for crash severity prediction publication-title: Accid. Anal. Prev. doi: 10.1016/j.aap.2017.08.008 – volume: 135 start-page: 32 year: 2014 ident: ref_2 article-title: A method for resampling imbalanced datasets in binary classification tasks for real-world problems publication-title: Neurocomputing doi: 10.1016/j.neucom.2013.05.059 – ident: ref_44 doi: 10.1145/1007730.1007735 – ident: ref_4 doi: 10.3233/IDA-2002-6504 – volume: 12 start-page: 287 year: 1997 ident: ref_5 article-title: Instance-Based Learning for Highway Accident Frequency Prediction publication-title: Comput. Civ. Infrastruct. Eng. doi: 10.1111/0885-9507.00064 – ident: ref_40 doi: 10.1016/j.aap.2019.105371 – volume: 40 start-page: 6047 year: 2013 ident: ref_23 article-title: Analysis of traffic accident severity using Decision Rules via Decision Trees publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2013.05.027 – volume: 16 start-page: 321 year: 2002 ident: ref_42 article-title: SMOTE: Synthetic minority over-sampling technique publication-title: J. Artif. Intell. Res. doi: 10.1613/jair.953 – ident: ref_51 doi: 10.2307/2280041 – ident: ref_58 doi: 10.1109/ACCESS.2019.2903319 – volume: 96 start-page: 108 year: 2016 ident: ref_7 article-title: M5 model tree based predictive modeling of road accidents on non-urban sections of highways in India publication-title: Accid. Anal. Prev. doi: 10.1016/j.aap.2016.08.004 – volume: 127 start-page: 134 year: 2019 ident: ref_17 article-title: A comparison of statistical learning methods for deriving determining factors of accident occurrence from an imbalanced high resolution dataset publication-title: Accid. Anal. Prev. doi: 10.1016/j.aap.2019.02.008 – ident: ref_37 doi: 10.1016/j.aap.2005.03.019 – ident: ref_43 – volume: 171 start-page: 253 year: 2018 ident: ref_6 article-title: Support vector machine model for prediction of accidents on non-urban sections of highways publication-title: Proc. Inst. Civ. Eng. Transp. – ident: ref_57 – ident: ref_38 doi: 10.1007/978-3-030-34069-8_17 |
| SSID | ssj0002087881 |
| Score | 2.5047603 |
| Snippet | Crash severity is undoubtedly a fundamental aspect of a crash event. Although machine learning algorithms for predicting crash severity have recently gained... |
| SourceID | doaj proquest crossref |
| SourceType | Open Website Aggregation Database Enrichment Source Index Database |
| StartPage | 61 |
| SubjectTerms | Algorithms Classification Classifiers crash severity Crashes Datasets Decision making Decision trees k-nearest neighbor Machine learning machine learning classification algorithms Neural networks Performance evaluation Prediction models Property damage random classification tree random forest random undersampling the majority class Regression Roads & highways Studies |
| SummonAdditionalLinks | – databaseName: Advanced Technologies & Aerospace Database dbid: P5Z link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LT8MwDI5gcIADb8RgoBy4VuuatmtOiKfGgWniIQ0uVR7OQIJtbAOJf4_dZQMEggO3qm2kNHbsz477mbH9qOYApIqC0AkMUFCFApVCQnkrje4wzkLtimYT9WYza7dlyyfchr6scmITC0Nte4Zy5FVyJWhHUYUO-s8BdY2i01XfQmOWzRFLArVuaCV30xxLFGbElj7-MVhgdF9FqQ3UmJn1BcNZBEPkw7_4pIK6_5tlLtzN2fJ_J7rCljzQ5IdjzVhlM9BdY4uf6AfX2W2DKBbwkp8_aSpxNGD5iRop_tDllz1l-TF-xj2_AtR3ROu8NaBjHRIl12_8oqjDBO4pWjv88LGDExndPw032M3Z6fVxI_CtFgKDgGQUqERqYSTCNeusExmEoUlUDSAydUil0omT6OxiKlYNExdLC3FsESzUEa8AwshNVur2urDFuBORgDRF5B5CnEmXOSFSqdENSsBorFZmyWSxc-N5yKkdxmOO8QgJKf9ZSGVWnY7rj5k4_hxxRLKcvk1M2sWN3qCT-42Za2FVlBqrdCRjqjVwicpEDRdDpyKNXZlVJmLO_fYe5h8y3v798Q5biChAD-tojiqshFOEXTZvXkcPw8Feoa3vODX2Hw priority: 102 providerName: ProQuest |
| Title | Handling Imbalanced Data in Road Crash Severity Prediction by Machine Learning Algorithms |
| URI | https://www.proquest.com/docview/2426718510 https://doaj.org/article/b3da26cdab2944208f5a8313c9b6364f |
| Volume | 5 |
| WOSCitedRecordID | wos000623637800009&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Open Access Full Text customDbUrl: eissn: 2412-3811 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002087881 issn: 2412-3811 databaseCode: DOA dateStart: 20160101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2412-3811 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002087881 issn: 2412-3811 databaseCode: M~E dateStart: 20160101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 2412-3811 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002087881 issn: 2412-3811 databaseCode: P5Z dateStart: 20161201 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2412-3811 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002087881 issn: 2412-3811 databaseCode: BENPR dateStart: 20161201 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Publicly Available Content customDbUrl: eissn: 2412-3811 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002087881 issn: 2412-3811 databaseCode: PIMPY dateStart: 20161201 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LT8MwDI4QcIAD4ikGA-XAtVrXpI8cYYDYYVM1QBpcqqRxBgg2tA0k_j12201DIO3CpaqiRG1t1_7cuJ8ZOwuaDkDpwPOdwAQFTcjTEYT03cpgOJSJb1zRbCLudpN-X6ULrb6oJqykBy4F1zDC6iDKrTaBkrQX7EKdiKbIlYlEJB15Xz9WC8nUS7G9lhBPevlLsMC8voH6GuuSk_UDE1mEQRS9f0SjgrT_l08uAs31NtuqECI_L-9sh63AcJdtLvAG7rGHG-JGwFPefjNUm5iD5Zd6qvnzkPdG2vIW3sUTvwU0VITZPB3TfgzpgJsv3ikKKIFX3KoDfv46GOG8p7fJPru_vrpr3XhVjwQvRyQx9XSoDMoCcZZ11okEfD8PdRMgyGOIlDahUxilJFWZ-qGTyoKUFqN8jEADEP8dsNXhaAiHjDsRCIgihNw-yES5xAkRKYPxSwGmUc0aC2eyyvKKQJz6WLxmmEiQjLO_ZVxjjfm695JCY-mKC1LFfDZRYBcDaBhZZRjZMsOosfpMkVn1Xk4yAiQYjdERHf3HNY7ZRkD5tx-jt6mzVXwQOGHr-ef0eTI-ZWsXV920d1qYJh7T8BHH0nYnffgGGAfsYw |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1LbxMxEB6VFAl64FlEoIAPcFxlY3s36wNCpaVK1CaKoEjtabHX47RSm5QkBfVP8RuZ2UcAgeDUA7fV7nq1tj_PNzMezwC8lN2AaKyM4qDIQCEIRTbFhP1WjuhQZ7ELZbGJ3miUHR2Z8Rp8a87CcFhlIxNLQe1nBfvIO0wlJEcJQm8uPkdcNYp3V5sSGhUs9vHqK5lsi9eDXZrfV1LuvTvc6Ud1VYGoIO5dRjYxThWGNBMffFAZxnGR2C6iLHqYGuuSYEiua47LjJOgjUetPfFij6gZSWOi796Adc1gb8H6eDAcH6-8OjLOOD97dRRZKRN3CCdzW-WCvSQDmtQv1hp-YcGyWMBvXFAS3N7d_21o7sGdWpUW2xX278MaTh_Axk8JFh_CcZ-TSNClGJw7DuIs0Itdu7TidCrez6wXOzRsJ-ID0oome0SM57xxxWAV7koMy0hTFHUS2onYPptQx5cn54tN-HgtfXsErelsio9BBCUVpinZJjHqzIQsKJUaR0RvkOzNbhuSZnLzos60zgU_znKyuBgU-Z9B0YbOqt1FlWvkny3eMnZWb3Ou8PLGbD7Ja9GTO-WtTAtvnTSaoylCYjPVpcFwqUp1aMNWA6u8FmCL_Aemnvz98Qu41T8cHuQHg9H-U7gt2R0R90j4bkGLfhefwc3iy_J0MX9erxUBn64bg98BZhdTNg |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1LbxMxEB6VghAceCNCC_gAx1Uce18-oKo0RI0KUcRDKly2tnecVmqTkgSq_rX-Omb2EUAgOPXAbbW7Xq3tz_PNjMczAM9VLyAaqyIZNBkoBKHIppiw38oRHca5dKEqNpGNRvn-vhmvwUV7FobDKluZWAnqcubZR95lKiE5ShDqhiYsYtwfbJ1-ibiCFO-0tuU0aojs4fkZmW-Ll8M-zfULpQavP-zsRk2FgcgTDy8jmxinvSEtpQxl0DlK6RPbQ1Q-w9RYlwRDMj7mGE2ZhNiUGMclcWRGNI2kPdF3r8DVjGxMDiccJ59X_h0lc87UXh9K1trILiFmbuussF_JlCZFjPWHX_iwKhvwGytUVDe4_T8P0h241SjYYrteEXdhDaf34OZPaRfvw6ddTi1Bl2J44ji002Mp-nZpxdFUvJvZUuzQEB6K90jrnKwUMZ7zdhZDWLhz8baKP0XRpKadiO3jCXV8eXiyeAAfL6VvD2F9OpviIxBBK41pShaLxDg3IQ9ap8YR_RskK7TXgaSd6MI3-de5DMhxQXYYA6T4M0A60F21O60zkPyzxSvG0eptziBe3ZjNJ0UjkAqnS6tSX1qnTMwxFiGxue7RYLhUp3HowGYLsaIRa4viB74e__3xM7hOwCveDEd7G3BDsY9CZiSRN2Gd_hafwDX_bXm0mD-tFo2Ag8sG4HeYQVqZ |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Handling+Imbalanced+Data+in+Road+Crash+Severity+Prediction+by+Machine+Learning+Algorithms&rft.jtitle=Infrastructures+%28Basel%29&rft.au=Fiorentini%2C+Nicholas&rft.au=Losa%2C+Massimo&rft.date=2020-07-20&rft.issn=2412-3811&rft.eissn=2412-3811&rft.volume=5&rft.issue=7&rft.spage=61&rft_id=info:doi/10.3390%2Finfrastructures5070061&rft.externalDBID=n%2Fa&rft.externalDocID=10_3390_infrastructures5070061 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2412-3811&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2412-3811&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2412-3811&client=summon |