{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/108066"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/108066","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"A machine learning model for vehicle crash type prediction","abstract":"Travel safety research works include the studies of risk factor investigation, crash detection, and crash frequency prediction. However, the existing studies are focused on the macro level, paying little attention to the specific crash type. In this study, eXtreme Gradient Boosting (XGBoost) method is applied to predict the occurrence of different types of crashes. A two-layer model is proposed. The first layer is used to distinguish potential crashes from crash-free observations and the second layer is used for crash type recognition. The results show that the proposed model can detect the potential accident and identify the crash type successfully, with accuracy levels of over 99% and 62%, respectively. Besides the crash type prediction model, this study provides a detailed analysis of the impacts of different risk factors on different types of crashes. From the traffic management perspective, the results of this study can prepare traffic managers for the potential threatens well in advance. From travelers’ perspective, the results of this study can be used to warn travelers of potential dangers before the trip so that a better trip planning can be made as well as alert them of the potential dangers during the trip. All of these actions are important for the travel safety management and can help protect people’s life and property.","abstract_html":"Travel safety research works include the studies of risk factor investigation, crash detection, and crash frequency prediction. However, the existing studies are focused on the macro level, paying little attention to the specific crash type. In this study, eXtreme Gradient Boosting (XGBoost) method is applied to predict the occurrence of different types of crashes. A two-layer model is proposed. The first layer is used to distinguish potential crashes from crash-free observations and the second layer is used for crash type recognition. The results show that the proposed model can detect the potential accident and identify the crash type successfully, with accuracy levels of over 99% and 62%, respectively. Besides the crash type prediction model, this study provides a detailed analysis of the impacts of different risk factors on different types of crashes. From the traffic management perspective, the results of this study can prepare traffic managers for the potential threatens well in advance. From travelers’ perspective, the results of this study can be used to warn travelers of potential dangers before the trip so that a better trip planning can be made as well as alert them of the potential dangers during the trip. All of these actions are important for the travel safety management and can help protect people’s life and property.","abstract_has_math":false,"creators":["Li, Xiyue"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Civil Engineering","degree_department":null,"school":null,"contributors":["Meidani, Hadi"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-08-26T21:58:09Z","date_published":"2020-08-26T21:58:09Z","updated_at":"2026-07-22T22:24:47Z","subjects":["Crash type, Crash prediction, Machine learning"],"languages":["en"],"rights":["Copyright 2020 Xiyue Li"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/108066","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Meidani, Hadi"]},{"key":"dc:creator","label":"Author","values":["Li, Xiyue"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-08-26T21:58:09Z","2020-05-15","2020-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Crash type, Crash prediction, Machine learning"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2020 Xiyue Li"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/108066"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Travel safety research works include the studies of risk factor investigation, crash detection, and crash frequency prediction. However, the existing studies are focused on the macro level, paying little attention to the specific crash type. In this study, eXtreme Gradient Boosting (XGBoost) method is applied to predict the occurrence of different types of crashes. A two-layer model is proposed. The first layer is used to distinguish potential crashes from crash-free observations and the second layer is used for crash type recognition. The results show that the proposed model can detect the potential accident and identify the crash type successfully, with accuracy levels of over 99% and 62%, respectively. Besides the crash type prediction model, this study provides a detailed analysis of the impacts of different risk factors on different types of crashes. From the traffic management perspective, the results of this study can prepare traffic managers for the potential threatens well in advance. From travelers’ perspective, the results of this study can be used to warn travelers of potential dangers before the trip so that a better trip planning can be made as well as alert them of the potential dangers during the trip. All of these actions are important for the travel safety management and can help protect people’s life and property.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2020-08-25 without embargo terms","The student, Xiyue Li, accepted the attached license on 2020-05-14 at 13:28.","The student, Xiyue Li, submitted this Thesis for approval on 2020-05-14 at 15:11.","This Thesis was approved for publication on 2020-05-15 at 10:05.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15395 on 2020-08-25 at 17:14:46","Made available in DSpace on 2020-08-26T21:58:09Z (GMT). No. of bitstreams: 2 LI-THESIS-2020.pdf: 1583968 bytes, checksum: 8fa62f36235fe5491342c4967d166f3f (MD5) LICENSE.txt: 4205 bytes, checksum: 0ac4d7d9673c655fe046f8462b609633 (MD5) Previous issue date: 2020-05-15"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["A machine learning model for vehicle crash type prediction"]}]}],"canonical_facts":{"dc:contributor":["Meidani, Hadi"],"dc:creator":["Li, Xiyue"],"dc:date":["2020-08-26T21:58:09Z","2020-05-15","2020-05"],"dc:description":["Travel safety research works include the studies of risk factor investigation, crash detection, and crash frequency prediction. However, the existing studies are focused on the macro level, paying little attention to the specific crash type. In this study, eXtreme Gradient Boosting (XGBoost) method is applied to predict the occurrence of different types of crashes. A two-layer model is proposed. The first layer is used to distinguish potential crashes from crash-free observations and the second layer is used for crash type recognition. The results show that the proposed model can detect the potential accident and identify the crash type successfully, with accuracy levels of over 99% and 62%, respectively. Besides the crash type prediction model, this study provides a detailed analysis of the impacts of different risk factors on different types of crashes. From the traffic management perspective, the results of this study can prepare traffic managers for the potential threatens well in advance. From travelers’ perspective, the results of this study can be used to warn travelers of potential dangers before the trip so that a better trip planning can be made as well as alert them of the potential dangers during the trip. All of these actions are important for the travel safety management and can help protect people’s life and property.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2020-08-25 without embargo terms","The student, Xiyue Li, accepted the attached license on 2020-05-14 at 13:28.","The student, Xiyue Li, submitted this Thesis for approval on 2020-05-14 at 15:11.","This Thesis was approved for publication on 2020-05-15 at 10:05.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15395 on 2020-08-25 at 17:14:46","Made available in DSpace on 2020-08-26T21:58:09Z (GMT). No. of bitstreams: 2 LI-THESIS-2020.pdf: 1583968 bytes, checksum: 8fa62f36235fe5491342c4967d166f3f (MD5) LICENSE.txt: 4205 bytes, checksum: 0ac4d7d9673c655fe046f8462b609633 (MD5) Previous issue date: 2020-05-15"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/108066"],"dc:language":["en"],"dc:rights":["Copyright 2020 Xiyue Li"],"dc:subject":["Crash type, Crash prediction, Machine learning"],"dc:title":["A machine learning model for vehicle crash type prediction"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Civil Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:47Z"}