[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127840-en":3,"doc-seo-127840-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127840,1099523885336,"Violet","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Predictive Analytics of Road Traffic Incidents - A Machine Learning Approach - Thesis","Traffic accidents rank among the world’s most serious concerns due to high death, injury, and fatality counts and substantial annual financial losses. Road travel is essential to modern society, yet rising accident rates impose significant economic costs. Accidents can result from multiple contributing factors, and better recognition and prediction can reduce both the severity and extent of their impacts. This project applies machine learning to forecast traffic incident severity using the US Accidents dataset from Kaggle and builds Random Forest, Decision Tree, and KNN models in Python.","Rochester Institute of Technology  \nRIT Digital Institutional Repository  \nTheses  \nSpring 2024  \nPredictive Analytics of Road Traffic Incidents, A Machine Learning Approach  \nMaryam Essa Jaji[me3101@rit.edu](me3101@rit.edu)  \nFollow this and additional works at: [https://repository.rit.edu/theses](https://repository.rit.edu/theses)  \nRecommended Citation  \nEssa Jaji, Maryam, \"Predictive Analytics of Road Traffic Incidents, A Machine Learning Approach\" (2024) . Thesis. Rochester Institute of Technology. Accessed from  \nThis Thesis is brought to you for free and open access by the RIT Libraries. For more information, please contact [repository@rit.edu](repository@rit.edu).  \nPredictive Analytics of Road Traffic Incidents, A Machine  \nLearning Approach  \nby  \nMaryam Essa Haji  \nA Thesis Submitted in Partial Fulfilment of the Requirements for the Degree of Master of  \nScience in Professional Studies: Data Analytics  \nDepartment of Graduate Programs & Research  \nRochester Institute of Technology  \nRIT Dubai  \nSpring 2024  \nAcknowledgments  \nFirst and foremost, I want to thank Allah, the Almighty, for providing me with the courage, knowledge, and grace to pursue and complete my academic journey.  \nMy parents have been the most consistent support system in my life, and I am thankful to Allah for them. Their philanthropic deeds of kindness and moral support have given me the strength to complete this order.  \nIn addition, I would like to thank Professor Sanjay Modak, the department chair, and my professors for their wise guidance and the pleasant environment that has allowed me to grow and learn with them. The perspective I’ve developed as a result of their constant pursuit of their students’ success has enriched my life.  \nAnother person to whom I am particularly grateful is my friends and his groups. During my time at college, we have been each other’s largest supporter when it comes to school. We’ve fostered and encouraged each other.  \nThank you, Dr. Esan Ullah Warriach, my project advisor, and matching guru, for his continued backing during difficult times. Dr. Ehsan’s perseverance and expert advice turned what appeared to be an impossible challenge into a manageable one. During the most challenging periods, he viewed anything in me when I had no idea how much I could do in a short period. Thank you foryour thoughtfulness and counsel.  \nI would also like to thank my friend, my coworker, who helped me understand my lessons and project. His advice and encouragement were always a source of strength for me. My coworker and friend merit a distinct word of gratitude for making the lessons and project simpler to solve by providing advice, tips, and suggestions.  \nAbstract  \nTraffic accidents rank among the world's most serious concerns due to the high number of deaths, injuries, and fatalities as well as the enormous financial losses they cause every year. Road travel is a necessary component of modern civilization, but because of the rise in traffic accidents, it costs the world economy billions of dollars and over a million deaths annually. Road accidents can be caused by a variety of elements. It can be possible to take action to lessen the severity and extent of the effects if these elements are better recognized and predicted.  \nThe goal of this project is to use machine learning techniques to forecast the severity of traffic incidents. Utilizing the US Accidents dataset sample from Kaggle, the project develops a Random Forest classifier prediction model, Decision Tree model, and K-Nearest Neighbor (KNN) model. To predict the severity of accidents, the model utilizes the use of several data, including timerelated factors, road characteristics, and weather. Python programming language has been used to develop the predictive model. The project desires to improve public safety and minimize the effect of road traffic events by offering actionable data for emergency response teams and traffic management. The outcomes obtained have incr","cbCaikRA9jKXefWW","https://ap.wps.com/l/cbCaikRA9jKXefWW","pdf",1173270,1,57,"English","en",105,"# Acknowledgments\n# Abstract\n# List of Figures\n# Chapter 1\n## Introduction\n## Problem Statement\n## Project Goals\n## Aims and Objectives\n## Research Methodology\n## Data Preprocessing","[{\"question\":\"What is the objective of the project?\",\"answer\":\"The project aims to use machine learning techniques to forecast the severity of traffic incidents and improve public safety through actionable insights.\"},{\"question\":\"Which dataset and models are used for severity prediction?\",\"answer\":\"It uses the US Accidents dataset sample from Kaggle and builds three models: Random Forest, Decision Tree, and K-Nearest Neighbor (KNN).\"},{\"question\":\"How is accident severity predicted and how accurate are the results?\",\"answer\":\"Predictions use features related to time, road characteristics, and weather. Reported accuracies are approximately 84% for Random Forest, 76% for Decision Tree, and 83% for KNN, with scope for improvement for minority classes.\"}]","Predictive Analytics of Road Traffic Incidents - A Machine Learning Approach - Thesis | PDF",1785942275,144,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"predictive-analytics-of-road-traffic-incidents-a-machine-learning-approach-thesis","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/predictive-analytics-of-road-traffic-incidents-a-machine-learning-approach-thesis/127840/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the objective of the project?","Question",{"text":76,"@type":77},"The project aims to use machine learning techniques to forecast the severity of traffic incidents and improve public safety through actionable insights.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which dataset and models are used for severity prediction?",{"text":81,"@type":77},"It uses the US Accidents dataset sample from Kaggle and builds three models: Random Forest, Decision Tree, and K-Nearest Neighbor (KNN).",{"name":83,"@type":74,"acceptedAnswer":84},"How is accident severity predicted and how accurate are the results?",{"text":85,"@type":77},"Predictions use features related to time, road characteristics, and weather. Reported accuracies are approximately 84% for Random Forest, 76% for Decision Tree, and 83% for KNN, with scope for improvement for minority classes.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]