{"id":39290,"date":"2023-05-08T10:21:16","date_gmt":"2023-05-08T14:21:16","guid":{"rendered":"https:\/\/ise.ncsu.edu\/?p=39290"},"modified":"2023-05-08T10:21:16","modified_gmt":"2023-05-08T14:21:16","slug":"helping-first-responders","status":"publish","type":"post","link":"https:\/\/webpublishing.oit.ncsu.edu\/ise-ncsu-edu\/2023\/05\/08\/helping-first-responders\/","title":{"rendered":"Model Aims to Help First Responders Reach Accident Sites Faster"},"content":{"rendered":"\n<p class=\"has-slightly-smaller-font-size\"><a href=\"https:\/\/news.ncsu.edu\/2023\/05\/helping-first-responders\/\">Original article<\/a> by <a href=\"https:\/\/news.ncsu.edu\/author\/wmshipma\/\">Matt Shipman<\/a> at <a href=\"https:\/\/university-communications.ncsu.edu\/\">University Communications<\/a>. Photo credit: Yassine Khalfalli.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">FOR IMMEDIATE RELEASE<\/h2>\n\n\n\n<p>Leila Hajibabai  |  <a href=\"mailto:lhajiba@ncsu.edu\">lhajiba@ncsu.edu<\/a><\/p>\n\n\n\n<p>Matt Shipman  |  <a href=\"mailto:matt_shipman@ncsu.edu\">matt_shipman@ncsu.edu<\/a><\/p>\n\n\n\n<p>Researchers at North Carolina State University have developed a complex model to improve how quickly first responders \u2013 such as police and EMTs \u2013 reach the scene of vehicle accidents. In computational testing, the model outperformed the existing techniques for getting first responders to accident sites quickly.<\/p>\n\n\n\n<p>\u201cThe goal was to figure out the most efficient way to get first responders to an accident,\u201d says Leila Hajibabai, corresponding author of a paper on the work and an assistant professor in NC&nbsp;State\u2019s Edward P. Fitts Department of Industrial and Systems Engineering. \u201cWhere should first responders be based in order to respond to the most likely sites of accidents? Is it always best for the closest first responders to go to an accident site, or \u2013 depending on traffic \u2013 could it be faster for first responders who are farther away to respond? How does accident severity affect response times? These are some of the questions our model addresses.\u201d<\/p>\n\n\n\n<p>To that end, the researchers developed a model that both maximizes the coverage area so that response units can respond to as many possible accident sites as possible and minimizes the amount of time it would take respondents to reach accident sites. The model also accounts for \u201chot spots,\u201d prioritizing the efficiency of response times to locations where accidents are most likely to happen based on historical data.<\/p>\n\n\n\n<p>\u201cThe model can be used for both long-term planning and for allocating incident response resources on a day-to-day basis,\u201d Hajibabai says.<\/p>\n\n\n\n<p>For long-term planning, the model can help authorities optimize the location of respondents \u2013 i.e., help them determine where to locate first-responder infrastructure. On a day-to-day basis, the model could also help authorities determine which respondents are best placed to reach an accident most quickly.<\/p>\n\n\n\n<p>To test the model, the researchers drew on data collected by the North Carolina Department of Transportation regarding 10,983 traffic incidents that occurred in 10,672 different locations in Raleigh, N.C. The researchers used the data to test how efficiently the model performed as compared to the two current state-of-the-art techniques used to allocate incident response resources.<\/p>\n\n\n\n<p>\u201cOur model outperformed the existing models in terms of improving response times, regardless of the size of the traffic problem they were responding to,\u201d Hajibabai says.<\/p>\n\n\n\n<p class=\"has-text-align-left\">What\u2019s more, the researchers made the model sufficiently flexible to incorporate changes in the number of resources available for incident response.<\/p>\n\n\n\n<p>\u201cBudgets and other circumstances can change significantly over time, and it\u2019s important for our model to be able to incorporate changes in how many respondents are available,\u201d Hajibabai says.<\/p>\n\n\n\n<p>The researchers are now testing the limits of the model.<\/p>\n\n\n\n<p>\u201cAre there things we can do to make the model even faster? This is a proof-of-concept paper, and the results are excellent,\u201d Hajibabai says. \u201cNow we want to see what we can do to make it even better.\u201d The researchers are looking for partners to conduct pilot studies with their model.<\/p>\n\n\n\n<p>The paper, \u201c<a href=\"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/mice.13016\" target=\"_blank\" rel=\"noreferrer noopener\">Joint Column Generation and Lagrangian Relaxation Technique for Incident Respondent Location and Allocation<\/a>,\u201d is published open access in the journal&nbsp;<em>Computer-Aided Civil and Infrastructure Engineering<\/em>. First author of the paper is Asya Atik, a Ph.D. student at NC&nbsp;State.<\/p>\n\n\n\n<p class=\"has-text-align-center\">-shipman-<\/p>\n\n\n\n<p><strong>Note to Editors:<\/strong>&nbsp;The study abstract follows.<\/p>\n\n\n\n<p><strong>\u201cJoint Column Generation and Lagrangian Relaxation Technique for Incident Respondent Location and Allocation\u201d<\/strong><\/p>\n\n\n\n<p><em>Authors<\/em>: Asya Atik and Leila Hajibabai, North Carolina State University<\/p>\n\n\n\n<p><em>Published<\/em>: May 4,&nbsp;<em>Computer-Aided Civil and Infrastructure Engineering<\/em><\/p>\n\n\n\n<p><em>DOI<\/em>: 10.1111\/mice.13016<\/p>\n\n\n\n<p><strong>Abstract:<\/strong>&nbsp;Incident response operations require effective planning of resources to ensure timely clearance of roadways and avoidance of secondary incidents. This study formulates a mixed-integer linear program to minimize the total expected travel time and maximize the demand covered. The model accounts for the location, severity, frequency of incidents, dispatching locations, and availability of incident respondents. An integrated methodology that includes column generation and Lagrangian relaxation with a density-based clustering technique that defines incident hot-spots is proposed. The hybrid approach is applied to an empirical case study in Raleigh, NC. A network instance with 10,672 incident sites, clustered with a search distance (\u03b5) of 5 min, is solved efficiently with an optimality gap of 1.37% in 2 min. A Benders decomposition technique is implemented to conduct benchmark analyses. The numerical results suggest that the proposed algorithm can solve the problem efficiently and outperform the benchmark solutions.<\/p>\n","protected":false,"raw":"<!-- wp:paragraph {\"fontSize\":\"slightly-smaller\"} -->\n<p class=\"has-slightly-smaller-font-size\"><a href=\"https:\/\/news.ncsu.edu\/2023\/05\/helping-first-responders\/\">Original article<\/a> by <a href=\"https:\/\/news.ncsu.edu\/author\/wmshipma\/\">Matt Shipman<\/a> at <a href=\"https:\/\/university-communications.ncsu.edu\/\">University Communications<\/a>. Photo credit: Yassine Khalfalli.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:heading -->\n<h2>FOR IMMEDIATE RELEASE<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:paragraph -->\n<p>Leila Hajibabai  |  <a href=\"mailto:lhajiba@ncsu.edu\">lhajiba@ncsu.edu<\/a><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>Matt Shipman  |  <a href=\"mailto:matt_shipman@ncsu.edu\">matt_shipman@ncsu.edu<\/a><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>Researchers at North Carolina State University have developed a complex model to improve how quickly first responders \u2013 such as police and EMTs \u2013 reach the scene of vehicle accidents. In computational testing, the model outperformed the existing techniques for getting first responders to accident sites quickly.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>\u201cThe goal was to figure out the most efficient way to get first responders to an accident,\u201d says Leila Hajibabai, corresponding author of a paper on the work and an assistant professor in NC&nbsp;State\u2019s Edward P. Fitts Department of Industrial and Systems Engineering. \u201cWhere should first responders be based in order to respond to the most likely sites of accidents? Is it always best for the closest first responders to go to an accident site, or \u2013 depending on traffic \u2013 could it be faster for first responders who are farther away to respond? How does accident severity affect response times? These are some of the questions our model addresses.\u201d<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>To that end, the researchers developed a model that both maximizes the coverage area so that response units can respond to as many possible accident sites as possible and minimizes the amount of time it would take respondents to reach accident sites. The model also accounts for \u201chot spots,\u201d prioritizing the efficiency of response times to locations where accidents are most likely to happen based on historical data.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>\u201cThe model can be used for both long-term planning and for allocating incident response resources on a day-to-day basis,\u201d Hajibabai says.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>For long-term planning, the model can help authorities optimize the location of respondents \u2013 i.e., help them determine where to locate first-responder infrastructure. On a day-to-day basis, the model could also help authorities determine which respondents are best placed to reach an accident most quickly.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>To test the model, the researchers drew on data collected by the North Carolina Department of Transportation regarding 10,983 traffic incidents that occurred in 10,672 different locations in Raleigh, N.C. The researchers used the data to test how efficiently the model performed as compared to the two current state-of-the-art techniques used to allocate incident response resources.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>\u201cOur model outperformed the existing models in terms of improving response times, regardless of the size of the traffic problem they were responding to,\u201d Hajibabai says.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"align\":\"left\"} -->\n<p class=\"has-text-align-left\">What\u2019s more, the researchers made the model sufficiently flexible to incorporate changes in the number of resources available for incident response.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>\u201cBudgets and other circumstances can change significantly over time, and it\u2019s important for our model to be able to incorporate changes in how many respondents are available,\u201d Hajibabai says.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>The researchers are now testing the limits of the model.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>\u201cAre there things we can do to make the model even faster? This is a proof-of-concept paper, and the results are excellent,\u201d Hajibabai says. \u201cNow we want to see what we can do to make it even better.\u201d The researchers are looking for partners to conduct pilot studies with their model.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>The paper, \u201c<a href=\"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/mice.13016\" target=\"_blank\" rel=\"noreferrer noopener\">Joint Column Generation and Lagrangian Relaxation Technique for Incident Respondent Location and Allocation<\/a>,\u201d is published open access in the journal&nbsp;<em>Computer-Aided Civil and Infrastructure Engineering<\/em>. First author of the paper is Asya Atik, a Ph.D. student at NC&nbsp;State.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"align\":\"center\"} -->\n<p class=\"has-text-align-center\">-shipman-<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p><strong>Note to Editors:<\/strong>&nbsp;The study abstract follows.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p><strong>\u201cJoint Column Generation and Lagrangian Relaxation Technique for Incident Respondent Location and Allocation\u201d<\/strong><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p><em>Authors<\/em>: Asya Atik and Leila Hajibabai, North Carolina State University<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p><em>Published<\/em>: May 4,&nbsp;<em>Computer-Aided Civil and Infrastructure Engineering<\/em><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p><em>DOI<\/em>: 10.1111\/mice.13016<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p><strong>Abstract:<\/strong>&nbsp;Incident response operations require effective planning of resources to ensure timely clearance of roadways and avoidance of secondary incidents. This study formulates a mixed-integer linear program to minimize the total expected travel time and maximize the demand covered. The model accounts for the location, severity, frequency of incidents, dispatching locations, and availability of incident respondents. An integrated methodology that includes column generation and Lagrangian relaxation with a density-based clustering technique that defines incident hot-spots is proposed. The hybrid approach is applied to an empirical case study in Raleigh, NC. A network instance with 10,672 incident sites, clustered with a search distance (\u03b5) of 5 min, is solved efficiently with an optimality gap of 1.37% in 2 min. A Benders decomposition technique is implemented to conduct benchmark analyses. The numerical results suggest that the proposed algorithm can solve the problem efficiently and outperform the benchmark solutions.<\/p>\n<!-- \/wp:paragraph -->"},"excerpt":{"rendered":"<p>Leila Hajibabai and her team&#8217;s model outperformed existing techniques for quickly getting first responders to accident sites. <\/p>\n","protected":false},"author":0,"featured_media":39294,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"source":"","ncst_custom_author":"","ncst_show_custom_author":false,"ncst_dynamicHeaderBlockName":"","ncst_dynamicHeaderData":"","ncst_content_audit_freq":"","ncst_content_audit_date":"","ncst_content_audit_display":false,"ncst_backToTopFlag":"","footnotes":""},"categories":[1],"tags":[1769,1770,1421],"_ncst_magazine_issue":[],"class_list":["post-39290","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news","tag-asya-atik","tag-first-responders","tag-leila-hajibabai"],"displayCategory":null,"acf":{"ncst_posts_meta_modified_date":null},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Model Aims to Help First Responders Reach Accident Sites Faster | ISE 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