[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121410-en":3,"doc-seo-121410-105":30,"detail-sidebar-cat-0-en-105":91},{"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},121410,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","A Comparative Analysis of Machine Learning Models for Time Prediction in Food Delivery Operations - Research focus","Accurate time estimation underpins customer satisfaction and operational efficiency in the fast-growing food delivery sector. This paper analyzes key factors influencing delivery durations and evaluates multiple machine learning models for delivery-time forecasting. A dataset from a food delivery company on Kaggle is used, covering address and order timestamps, delivery time, weather, traffic intensity, and delivery-person profile data. Models including Linear Regression, Decision Trees, Random Forests, XGBRegressor, and KNN regression are assessed via MAE, RMSE, and R²; ensemble performance with XGBRegressor reaches R²=0.82, and feature-importance analysis clarifies drivers of prediction.","RESEARCH ARTICLE  \nA Comparative Analysis of Machine Learning Models for Time Prediction in Food Delivery Operations  \nElmas Yalçinkayaa , Ouranıa Areta Hızıroğlu a†  \na Department of Management Information Systems , İzmir Bakırçay University, Izmir, Türkiye,† [ourania.areta@bakircay.edu.tr](ourania.areta@bakircay.edu.tr), corresponding author  \nRECEIVED MARCH 26, 2024 ACCEPTED APRIL 29, 2024  \nCITATION Yalçinkaya , E. , & Areta Hızıroğlu, O. (2024) . A comparative analysis of machine learning models for time prediction in food delivery operations. Artificial Intelligence Theory and Applications, 4(1), 43-56.  \nAbstract  \nAccurate time estimation is crucial for ensuring customer satisfaction and operational efficiency in the growing food delivery sector. This paper focuses on comprehensively analyzing factors affecting food delivery times and assessing the effectiveness of machine learning models in forecasting delivery times. For this purpose, authors incorporated a detailed dataset from a food delivery company on the Kaggle platform, encompassing delivery address, order time, delivery time, weather conditions, traffic intensity, and delivery person's profile information. The study evaluated the effectiveness and performance of various machine learning models such as Linear Regression, Decision Trees, Random Forests, XGBRegressor and the k-nearest neighbors (KNN) regression model using metrics like MAE, RMSE, and R² . The results demonstrate that ensemble methods— XGBRegressor—outperformed the other models in accurately predicting delivery times , achieving an R-squared score of 0.82. Additionally, a thorough analysis of feature importance uncovered the factors influencing delivery time estimation. This study offers insights into leveraging machine learning techniques to optimize food delivery operations and enhance customer satisfaction. The discoveries can assist food delivery platforms in deploying effective time estimation models and emphasizing factors for predictions.  \nKeywords: machine learning; time estimation; feature importance; food delivery  \n1. Introduction  \nIn the last few years, food delivery has experienced major expansion with the inception of the online platforms that connect customers, delivery drivers and restaurants. One of the difficulties that customers face is the uncertainty surrounding delivery times that prompts researchers to come up with predictive models that will allow estimations of delivery duration. These models tackle issues of planning, organization, and control to optimize operations of the delivery sector [1] . Moreover, there is a substantial body of research on speeding up the delivery process by using optimization methods that help in the more efficient routing of the vehicles [1] . Studies on delivery platforms require management decisions on issues such as the delivery times and subsidy administration to radically improve profits [2] . In this regard, cutting down on the delivery times is of vital importance for improving the level of customer satisfaction and for holding an edge among competitors. The industry of food delivery is in the process of ever-changing  \nPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than AITA must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission [and/or a fee. Request permissions from info@aitajournal.com](and/or a fee. Request permissions from info@aitajournal.com)  \nArtificial Intelligence Theory and Applications , ISSN: 2757-9778. ISBN: 978-605-69730-2-4 © 2024 İzmir Bakırçay University  \ntrends, wherein the most attractive features are being created fi","cbCaie7JgxIqGdrb","https://ap.wps.com/l/cbCaie7JgxIqGdrb","pdf",475060,1,14,"English","en",105,"# Introduction\n## Factors affecting food delivery times\n## Motivation and objectives\n# Related concepts\n## Machine learning for time estimation\n# Data and methodology\n## Dataset description and predictors\n## Model evaluation metrics\n# Results and discussion\n## Model performance comparison\n## Feature importance analysis\n# Conclusion\n## Practical implications for delivery operations","[{\"question\":\"What problem does the paper address in food delivery operations?\",\"answer\":\"The paper addresses uncertainty in delivery times by studying factors that affect delivery duration and using machine learning to forecast it for better planning and customer satisfaction.\"},{\"question\":\"What dataset and input variables are used for time prediction?\",\"answer\":\"The study uses a Kaggle dataset from a food delivery company including delivery address/order time, delivery time, weather conditions, traffic intensity, and delivery-person profile information.\"},{\"question\":\"Which model performed best and how is performance measured?\",\"answer\":\"XGBRegressor outperformed other models using MAE, RMSE, and R², achieving an R² score of 0.82 for delivery-time prediction.\"}]","A Comparative Analysis of Machine Learning Models for Time Prediction in Food Delivery Operations - Research focus | PDF",1785735544,35,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-comparative-analysis-of-machine-learning-models-for-time-prediction-in-food-delivery-operations-research-focus","",{"@graph":36,"@context":85},[37,54,68],{"@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/a-comparative-analysis-of-machine-learning-models-for-time-prediction-in-food-delivery-operations-research-focus/121410/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the paper address in food delivery operations?","Question",{"text":75,"@type":76},"The paper addresses uncertainty in delivery times by studying factors that affect delivery duration and using machine learning to forecast it for better planning and customer satisfaction.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What dataset and input variables are used for time prediction?",{"text":80,"@type":76},"The study uses a Kaggle dataset from a food delivery company including delivery address/order time, delivery time, weather conditions, traffic intensity, and delivery-person profile information.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performed best and how is performance measured?",{"text":84,"@type":76},"XGBRegressor outperformed other models using MAE, RMSE, and R², achieving an R² score of 0.82 for delivery-time prediction.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]