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Patterns of wait time are extracted for each hour and each day of the week, then multiple machine learning models are trained to predict future wait times at DMV offices.",{"@graph":14,"@context":72},[15,34,55],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/technology/","Technology",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/wait-time-prediction-how-to-avoid-waiting-in-lines/309424/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/wait-time-prediction-how-to-avoid-waiting-in-lines/309424.png","ImageObject",300,407,{"name":42,"@type":43},"Ava Thompson","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-04","2026-09-20",true,{"@type":52,"interactionType":53,"userInteractionCount":33},"InteractionCounter",{"@type":54},"ViewAction",{"@type":56,"mainEntity":57},"FAQPage",[58,64,68],{"name":59,"@type":60,"acceptedAnswer":61},"What problem does the paper address?","Question",{"text":62,"@type":63},"It addresses how public service organizations must allocate resources effectively when demand varies unevenly across the day, causing long customer wait times.","Answer",{"name":65,"@type":60,"acceptedAnswer":66},"How is the dataset collected and what does it contain?",{"text":67,"@type":63},"The dataset is collected by scraping California DMV websites every 10 minutes during office hours on weekdays, producing about 0.7 million records over three months.",{"name":69,"@type":60,"acceptedAnswer":70},"What modeling approach is used for the wait time forecast?",{"text":71,"@type":63},"The paper frames wait time prediction as a time series prediction problem and evaluates multiple machine learning algorithms to predict future wait times.","https://schema.org",{"og:url":32,"og:type":74,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":76,"canonical":32},"index,follow",{"doc_id":78,"site_id":7},309424,1790479632,{"code":4,"msg":81,"data":82},"success",[83,87,91,95,100,104,109,114,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":84,"show_sort_weight":85,"slug":86},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":88,"show_sort_weight":89,"slug":90},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":92,"show_sort_weight":93,"slug":94},"Exam",70,"exam",{"id":96,"doc_module":4,"doc_module_name":25,"category_name":97,"show_sort_weight":98,"slug":99},5,"Comic",60,"comic",{"id":101,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":102,"slug":103},6,50,"technology",{"id":105,"doc_module":4,"doc_module_name":25,"category_name":106,"show_sort_weight":107,"slug":108},7,"Healthcare",40,"healthcare",{"id":110,"doc_module":4,"doc_module_name":25,"category_name":111,"show_sort_weight":112,"slug":113},8,"Research & Report",30,"research-report",{"id":115,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":96,"slug":129},19,"General","general",{"code":4,"msg":81,"data":131},{"doc_id":78,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":101,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":33,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":123,"language":139,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":12,"update_tm":143,"read_time":144},1649267921044,"https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1786009248482753345","Wait Time Prediction:  \nHow to Avoid Waiting in Lines?  \nYe Zhang  \nCarnegie Mellon University 5000 Forbes Avenue Pittsburgh, PA 15213, USA [yezhang@andrew.cmu.edu](yezhang@andrew.cmu.edu)  \nLe T. Nguyen  \nCarnegie Mellon University Moffet Field, CA, USA [le.nguyen@sv.cmu.edu](le.nguyen@sv.cmu.edu)  \nJoy Zhang  \nCarnegie Mellon University Moffet Field, CA, USA [joy.zhang@sv.cmu.edu](joy.zhang@sv.cmu.edu)  \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 the author(s) 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 permissions@acm.org](from permissions@acm.org).  \nUbiComp'13 Adjunct, September 8–12, 2013, Zurich, Switzerland.  \nCopyright is held by the owner/author(s) . Publication rights licensed to ACM. ACM 978-1-4503-2215-7/13/09 ...$15 .00. [http://dx.doi.org/10.1145/2494091.2495980](http://dx.doi.org/10.1145/2494091.2495980)  \nAbstract  \nOne of the challenges of organizations providing services to the public is the effective resource allocation. Many service providers such as hospitals, city halls or department of motor vehicles suffer from a service demand, which is unevenly distributed over the day. In this work, we evaluate techniques for predicting the service demand. We use the wait time dataset collected from the websites of California Department of Motor Vehicles (DMV) . We extract patterns of the service demand in form of wait time during each hour of a day and each day of a week. This information is used to train multiple machine learning models in order to predict the future wait time at DMV offices.  \nAuthor Keywords  \nTime Series Prediction, Machine Learning  \nACM Classification Keywords  \nI.2.6 [Artificial Intelligence]: Learning.  \nGeneral Terms  \nTime Series Prediction, Machine Learning  \nIntroduction  \nOne of the challenges of organizations providing services to the public is the effective resource  \nallocation. Many service providers such as hospitals, city halls or department of motor vehicles suffer from a service demand, which is unevenly distributed overtime. For example in city halls, people have to wait hours in a line when coming at the peak hours eventhough there are time periods during a day when the clerks have no one to serve. Due to the resource constraints (e.g. space availability in the offices) the issue cannot be easily addressed by only adjusting the number of workers for a specific time of a day.  \nIn the recent years, many organizations aimed to smoothen the peak in demands by making information about the current wait time available online [4 , 5 , 3] . The goal is to make people aware of the peak hours, so that they can avoid them. However, the current wait time information is rather obsolete as people rather need to know the expected wait time at a certain time in the future (e.g. in 3 hours or in the next days) .  \nIn this work, we evaluate approaches for predicting the expected wait time given a historical data. We use the real-world data set collected from the website of the California Department of Motor Vehicle (DMV) . DMV provide many services related to obtaining and maintaining a driver license and many vehicle related services such vehicle registration etc. California with a population of 37.5 million people has 24 million registered drivers owning in total 31 million vehicles [2] . This results in a large amount of people visiting DMV everyday especially at the peak hours. Based on our analysis a wait time duration at certain offices might differ as much as 3 hours, i.e., an ”unlucky“ customer might have to wait 3 hours more than an info","cbCaitF1KTbE6xms","https://ap.wps.com/l/cbCaitF1KTbE6xms","pdf",1463284,"English","# Abstract\n# Introduction\n# Dataset\n## Data collection\n## Office-level wait time patterns","[{\"question\":\"What problem does the paper address?\",\"answer\":\"It addresses how public service organizations must allocate resources effectively when demand varies unevenly across the day, causing long customer wait times.\"},{\"question\":\"How is the dataset collected and what does it contain?\",\"answer\":\"The dataset is collected by scraping California DMV websites every 10 minutes during office hours on weekdays, producing about 0.7 million records over three months.\"},{\"question\":\"What modeling approach is used for the wait time forecast?\",\"answer\":\"The paper frames wait time prediction as a time series prediction problem and evaluates multiple machine learning algorithms to predict future wait times.\"}]","Wait Time Prediction - How to Avoid Waiting in Lines | PDF",1789873848,25]