{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/95397"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/95397","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Dynamic method to identify and visualize accident contributing factors at highway-rail grade crossings","abstract":"Highway-Rail grade crossings are important locations on a highway network and safety at these locations is a critical issue for both highway and railroad network. Even though safety at grade crossings have improved over the years, over 200 people still lose their lives every year at grade crossings across the United States. Also, the injury and fatality rates are significantly higher for grade crossing crashes than other types of traffic crashes. Therefore the need for improving the safety at grade crossings is relevant today and more so especially since the demand in both highway and railroad systems continue to increase. Safety improvements at grade crossing locations can be suggested by establishing the contributing factors for accidents at each individual grade crossing and spearheading the safety improvement recommendation based on those contributing factors. Manually identifying the most significant contributing factors from the accident database at each grade crossing is not feasible due to various reasons. This thesis presents a new, easy method to extract the most frequent nested accident trends and helps an analyst visualize these trends using a tree based structure. This procedure is called the M+C method. The work presented in this thesis builds on top of the previous research conducted in this area by considering a more comprehensive set of accident attributes and by introducing a data{driven method of determining the order of the contributing factors to accidents at each location. The algorithm presented in this paper is also implemented in a computer program using the C++ language to automate the procedure thus reducing human e ort and error. Various examples illustrating the utility of the procedure to extract accident trends and contributing factors at a grade crossing. The use of this procedure to simultaneously analyze multiple crossings including all crossings along a corridor, all crossings within a county, all crossings with single accident are demonstrated in this thesis. This procedure is also used to identify new attributes to be used in accident prediction models. This computerized procedure, combined with a user{friendly interface could be a very useful tool that practitioners can use for quick and easy analysis of accidents at grade crossings.","abstract_html":"Highway-Rail grade crossings are important locations on a highway network and safety at these locations is a critical issue for both highway and railroad network. Even though safety at grade crossings have improved over the years, over 200 people still lose their lives every year at grade crossings across the United States. Also, the injury and fatality rates are significantly higher for grade crossing crashes than other types of traffic crashes. Therefore the need for improving the safety at grade crossings is relevant today and more so especially since the demand in both highway and railroad systems continue to increase. Safety improvements at grade crossing locations can be suggested by establishing the contributing factors for accidents at each individual grade crossing and spearheading the safety improvement recommendation based on those contributing factors. Manually identifying the most significant contributing factors from the accident database at each grade crossing is not feasible due to various reasons. This thesis presents a new, easy method to extract the most frequent nested accident trends and helps an analyst visualize these trends using a tree based structure. This procedure is called the M+C method. The work presented in this thesis builds on top of the previous research conducted in this area by considering a more comprehensive set of accident attributes and by introducing a data{driven method of determining the order of the contributing factors to accidents at each location. The algorithm presented in this paper is also implemented in a computer program using the C++ language to automate the procedure thus reducing human e ort and error. Various examples illustrating the utility of the procedure to extract accident trends and contributing factors at a grade crossing. The use of this procedure to simultaneously analyze multiple crossings including all crossings along a corridor, all crossings within a county, all crossings with single accident are demonstrated in this thesis. This procedure is also used to identify new attributes to be used in accident prediction models. This computerized procedure, combined with a user{friendly interface could be a very useful tool that practitioners can use for quick and easy analysis of accidents at grade crossings.","abstract_has_math":false,"creators":["Mathew, Jacob"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Civil Engineering","degree_department":null,"school":null,"contributors":["Benekohal, Rahim F."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-03-01T15:49:27Z","date_published":"2017-03-01T15:49:27Z","updated_at":"2026-07-22T22:26:37Z","subjects":["grade crossings","accident visualization"],"languages":["en"],"rights":["Copyright 2016 Jacob Mathew"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/95397","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Benekohal, Rahim F."]},{"key":"dc:creator","label":"Author","values":["Mathew, Jacob"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2017-03-01T15:49:27Z","2016-12-09","2016-12"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"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":["grade crossings","accident visualization"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2016 Jacob Mathew"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/95397"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Highway-Rail grade crossings are important locations on a highway network and safety at these locations is a critical issue for both highway and railroad network. Even though safety at grade crossings have improved over the years, over 200 people still lose their lives every year at grade crossings across the United States. Also, the injury and fatality rates are significantly higher for grade crossing crashes than other types of traffic crashes. Therefore the need for improving the safety at grade crossings is relevant today and more so especially since the demand in both highway and railroad systems continue to increase. Safety improvements at grade crossing locations can be suggested by establishing the contributing factors for accidents at each individual grade crossing and spearheading the safety improvement recommendation based on those contributing factors. Manually identifying the most significant contributing factors from the accident database at each grade crossing is not feasible due to various reasons. This thesis presents a new, easy method to extract the most frequent nested accident trends and helps an analyst visualize these trends using a tree based structure. This procedure is called the M+C method. The work presented in this thesis builds on top of the previous research conducted in this area by considering a more comprehensive set of accident attributes and by introducing a data{driven method of determining the order of the contributing factors to accidents at each location. The algorithm presented in this paper is also implemented in a computer program using the C++ language to automate the procedure thus reducing human e ort and error. Various examples illustrating the utility of the procedure to extract accident trends and contributing factors at a grade crossing. The use of this procedure to simultaneously analyze multiple crossings including all crossings along a corridor, all crossings within a county, all crossings with single accident are demonstrated in this thesis. This procedure is also used to identify new attributes to be used in accident prediction models. This computerized procedure, combined with a user{friendly interface could be a very useful tool that practitioners can use for quick and easy analysis of accidents at grade crossings.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2017-02-28 without embargo terms","The student, Jacob Mathew, accepted the attached license on 2016-12-09 at 08:35.","The student, Jacob Mathew, submitted this Thesis for approval on 2016-12-09 at 08:43.","This Thesis was approved for publication on 2016-12-09 at 09:53.","DSpace SAF Submission Ingestion Package generated from Vireo submission #10438 on 2017-02-28 at 14:55:21","Made available in DSpace on 2017-03-01T15:49:27Z (GMT). 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Also, the injury and fatality rates are significantly higher for grade crossing crashes than other types of traffic crashes. Therefore the need for improving the safety at grade crossings is relevant today and more so especially since the demand in both highway and railroad systems continue to increase. Safety improvements at grade crossing locations can be suggested by establishing the contributing factors for accidents at each individual grade crossing and spearheading the safety improvement recommendation based on those contributing factors. Manually identifying the most significant contributing factors from the accident database at each grade crossing is not feasible due to various reasons. This thesis presents a new, easy method to extract the most frequent nested accident trends and helps an analyst visualize these trends using a tree based structure. This procedure is called the M+C method. The work presented in this thesis builds on top of the previous research conducted in this area by considering a more comprehensive set of accident attributes and by introducing a data{driven method of determining the order of the contributing factors to accidents at each location. The algorithm presented in this paper is also implemented in a computer program using the C++ language to automate the procedure thus reducing human e ort and error. Various examples illustrating the utility of the procedure to extract accident trends and contributing factors at a grade crossing. The use of this procedure to simultaneously analyze multiple crossings including all crossings along a corridor, all crossings within a county, all crossings with single accident are demonstrated in this thesis. This procedure is also used to identify new attributes to be used in accident prediction models. This computerized procedure, combined with a user{friendly interface could be a very useful tool that practitioners can use for quick and easy analysis of accidents at grade crossings.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2017-02-28 without embargo terms","The student, Jacob Mathew, accepted the attached license on 2016-12-09 at 08:35.","The student, Jacob Mathew, submitted this Thesis for approval on 2016-12-09 at 08:43.","This Thesis was approved for publication on 2016-12-09 at 09:53.","DSpace SAF Submission Ingestion Package generated from Vireo submission #10438 on 2017-02-28 at 14:55:21","Made available in DSpace on 2017-03-01T15:49:27Z (GMT). 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