[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118371-en":3,"doc-seo-118371-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},118371,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Automated machine learning for analysis and prediction of vehicle crashes","Study presents a graphical interface and machine learning workflow for predicting vehicle traffic injury and fatality risk using NYC vehicle crash data. The approach supports a specified date range and ZIP-code level analysis, extending beyond prior work that mainly examined accident causes. A citywide collision review is paired with a support vector machine model to estimate accident likelihood and resulting harm. Visualization-based outputs enable accurate, efficient insights supporting transportation experts, policymakers, and insurance risk quantification.","Automated machine learning for analysis and prediction of  \nvehicle crashes  \nAbhishek Saxena1, Stefan A. Robila2  \n1Department of Data Science, Montclair State University, New Jersey, United States of America 2Department of Computer Science, Montclair State University, New Jersey, United States of America  \nArticle history:  \nReceived Jun 2, 2022 Revised Jul 23, 2022 Accepted Aug 15, 2022  \nKeywords:  \nMachine learning Open data  \nSupport vector machines Vehicular crash data Visualization  \nCorresponding Author:  \nThis work discusses the study and development of a graphical interface and implementation of a machine learning model for vehicle traffic injury and fatality prediction for a specified date range and for a certain zip (US postal) code based on the New York City's (NYC) vehicle crash data set. While previous studies focused on accident causes, little insight has been offered into how such data may be utilized to forecast future incidents, Studies that have historically concentrated on certain road segment types, such as highways and other streets, and a specific geographic region, this study offers a citywide review of collisions. Using cutting-edge database and networking technology, a user-friendly interface was created to display vehicle crash series. Following this, a support vector machine learning model was built to evaluate the likelihood of an accident and the consequent injuries and deaths at the zip code level for all of NYC and to better mitigate such events. Using the visualization and prediction approach, the findings show that it is efficient and accurate. Aside from transportation experts and government policymakers, the machine learning approach deliver useful insights to the insurance business since it quantifies collision risk data collected at specific places.  \nThis is an open access article under the CC BY-SA license.  \nStefan A. Robila  \nDepartment of Computer Science, Montclair State University Normal Ave, Montclair, New Jersey, United States of America Email: [robilas@montclair.edu](robilas@montclair.edu)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nDespite over a century of continuous technological progress and countless safety innovations, vehicular road crashes continue to constitute a significant proportion of deaths and injuries globally, while atthe same time generating losses estimated at close to two trillion US dollars/year [1] . While novel technologies such as driver assist, self-driving cars, traffic flow management, dedicated and traffic lanes, or enforcement campaigns may change the current trends, large scale analysis of data can lead to complementary insights [2] . Yet processing large data sets requires different approaches that combine geospatial-focused visualization with data mining and clustering. Meaningful results also require access to open, reliable, and renewable data streams.  \nSeveral large data sets are available. In US, the US fatal accidents dataset fatality analysis reporting system (FARS) is updated yearly by the national highway traffic safety administration as way to assist policymakers as well as provide consistent information to insurance companies [3] . Users can interact with the data by submitting queries on fatal accident frequency or download it for further analysis. Such data has been extensively used to test both ML models as well as derive accident trends. Li et al. [4] environmental factors such as road surface, weather, and light conditions, or human factors (such as alcohol consumption) were  \nevaluated as indicators for increased fatality rate. Das et al. [5] focused the analysis on scooters and noted that within a recent five-year period fatal accidents for these vehicles increased 76%(compared to an overall 2% decrease in all accidents) . Using cluster correspondence analysis, the authors identified compounding factors for the crashes and proposed their findings as input for rider and driver training and certification. Yuan et al. [6] used late","cbCaivhY2RHFVBAa","https://ap.wps.com/l/cbCaivhY2RHFVBAa","pdf",497396,1,"English","en",105,"# 1. INTRODUCTION\n## Background and data sources\n## Related work on accident prediction","[{\"question\":\"What problem does the study address?\",\"answer\":\"It develops an interface and a machine learning model to analyze and predict vehicle crash outcomes, including injury and fatality risk, using NYC crash data.\"},{\"question\":\"How is the prediction performed and at what geographic level?\",\"answer\":\"A support vector machine model evaluates accident likelihood and the consequent injuries and deaths at the ZIP code level across all of NYC.\"},{\"question\":\"Why are visualization and an interactive interface important in the proposed approach?\",\"answer\":\"The workflow uses visualization to display crash series and help users explore patterns, producing efficient and accurate insights for stakeholders such as policymakers and insurers.\"}]","Automated machine learning for analysis and prediction of vehicle crashes | PDF",1785683318,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"automated-machine-learning-for-analysis-and-prediction-of-vehicle-crashes","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/automated-machine-learning-for-analysis-and-prediction-of-vehicle-crashes/118371/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What problem does the study address?","Question",{"text":74,"@type":75},"It develops an interface and a machine learning model to analyze and predict vehicle crash outcomes, including injury and fatality risk, using NYC crash data.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How is the prediction performed and at what geographic level?",{"text":79,"@type":75},"A support vector machine model evaluates accident likelihood and the consequent injuries and deaths at the ZIP code level across all of NYC.",{"name":81,"@type":72,"acceptedAnswer":82},"Why are visualization and an interactive interface important in the proposed approach?",{"text":83,"@type":75},"The workflow uses visualization to display crash series and help users explore patterns, producing efficient and accurate insights for stakeholders such as policymakers and insurers.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":28,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":28,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]