[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123106-en":3,"doc-seo-123106-105":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":4,"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},123106,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Traffic Signal Wait Time Estimation Using Machine Learning","Urban driving is often hindered by unpredictable wait times at intersections and traffic signals, which can frustrate drivers and undermine route planning from conventional navigation apps. This disclosure presents machine learning prediction models that generate real-time wait-time estimates at intersections along a route. Predictions use historical traffic data plus current traffic patterns, weather information, and other available signals. Visual and audible guidance can explain why waits are long, and model performance is continually refined using user feedback and updated traffic data.","Technical Disclosure Commons  \nDefensive Publications Series  \n17 Sep 2024  \nTraffic Signal Wait Time Estimation Using Machine Learning Ajay Prasad  \nFollow this and additional works at: [https://www.tdcommons.org/dpubs_series](https://www.tdcommons.org/dpubs_series)  \nRecommended Citation  \nPrasad, Ajay, \"Traffic Signal Wait Time Estimation Using Machine Learning\", Technical Disclosure Commons,(September 17, 2024)  \n[https://www.tdcommons.org/dpubs_series/7364](https://www.tdcommons.org/dpubs_series/7364)  \nThis work is licensed under a Creative Commons Attribution 4.0 License.  \nThis Article is brought to you for free and open access by Technical Disclosure Commons. It has been accepted for inclusion in Defensive Publications Series by an authorized administrator of Technical Disclosure Commons.  \nTraffic Signal Wait Time Estimation Using Machine Learning  \nABSTRACT  \nUrban areas include a large number of traffic intersections and signals. Wait times at traffic signals can be long at times and can lead to user frustration. While digital map applications can provide predictions of overall time of travel for a journey and can also indicate congested parts of the route, these cannot reduce the frustration of waiting at a traffic signal. This disclosure describes the use of machine learning prediction models to automatically generate  \nreal-time predictions of wait times at intersections along a route. The predictions are based on  \nhistorical data as well as on current traffic patterns, weather information, and other available  \ninformation. Users are provided visual and/or audible guidance on wait times, including detailed explanations ofthe reason for long wait times. The prediction models are updated based on user  \nfeedback regarding accuracy of prediction as well as based on updated traffic data.  \nKEYWORDS  \n● Traffic light  \n● Traffic signal  \n● Intersection  \n● Signal timer  \n● Wait time  \n● User frustration  \n● Traffic pattern  \n● Digital map  \nPublished by Technical Disclosure Commons, 2024 2  \nBACKGROUND  \nDriving in densely populated cities is often hampered by unpredictable waiting times at traffic signals and intersections. Current digital map/ navigation applications provide illustrations of traffic congestion and inform users regarding estimated travel time to their destination. However, these applications cannot precisely predict wait times at traffic signals or intersections along a route. Variations in wait times can disrupt travel planning for users, causing inefficiencies and potential delays, e.g., if the estimated travel time is lower than actual time due to higher than estimated wait times at intersections.  \nModern traffic management systems rely on sensors and cameras for real time traffic information and adjust traffic signals accordingly, e.g., to decongest an intersection, improve  \noverall traffic flow in a region etc. However, such systems require significant infrastructure  \ninvestments in terms of cameras and other sensors, as well as computers that determine signal  \ntiming or other traffic guidance. Such traffic management systems are not integrated with digital  \nmap/navigation apps. As a result, accurately predicting wait times at traffic signals is not  \nfeasible.  \nDESCRIPTION  \nThis disclosure describes techniques that address the problem of unpredictable wait times at traffic signals or intersections. Wait times at traffic junctions are calculated and displayed within a digital map/ navigation application. Historical traffic data, including time-of-day traffic patterns, for various intersections in a region is obtained and analyzed using machine learning  \n(ML) techniques. Additional data such as current vehicular traffic conditions can also be  \nobtained and used for real time prediction. With sufficient data and training, the trained machine  \n[https://www.tdcommons.org/dpubs_series/7364](https://www.tdcommons.org/dpubs_series/7364) 3  \nlearning models can generate accurate predictions","cbCaihCRurwTmJU8","https://ap.wps.com/l/cbCaihCRurwTmJU8","pdf",172703,1,9,"English","en",105,"# Background\n# Description\n## Real-time wait-time prediction in digital maps\n## Data sources and model refinement\n## User interface and guidance","[{\"question\":\"Why is predicting traffic signal wait time difficult with current digital map or navigation apps?\",\"answer\":\"Existing apps can estimate overall travel time and show congestion, but they cannot precisely predict signal-level wait times. Variations at intersections can disrupt travel plans and cause delays.\"},{\"question\":\"What inputs do the machine learning models use to predict wait times?\",\"answer\":\"The models rely on historical data, time-of-day traffic patterns, and real-time information such as current traffic conditions. Weather data and other available signals can also be used.\"},{\"question\":\"How do users receive the predicted wait time information?\",\"answer\":\"The predicted wait time can be displayed inside a digital map navigation app as part of a traffic signal or stop sign interface element, potentially including a timer and guidance to help users anticipate delays.\"}]","Traffic Signal Wait Time Estimation Using Machine Learning | PDF",1785814674,23,{"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},"traffic-signal-wait-time-estimation-using-machine-learning","",{"@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/traffic-signal-wait-time-estimation-using-machine-learning/123106/",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","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is predicting traffic signal wait time difficult with current digital map or navigation apps?","Question",{"text":75,"@type":76},"Existing apps can estimate overall travel time and show congestion, but they cannot precisely predict signal-level wait times. Variations at intersections can disrupt travel plans and cause delays.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What inputs do the machine learning models use to predict wait times?",{"text":80,"@type":76},"The models rely on historical data, time-of-day traffic patterns, and real-time information such as current traffic conditions. Weather data and other available signals can also be used.",{"name":82,"@type":73,"acceptedAnswer":83},"How do users receive the predicted wait time information?",{"text":84,"@type":76},"The predicted wait time can be displayed inside a digital map navigation app as part of a traffic signal or stop sign interface element, potentially including a timer and guidance to help users anticipate delays.","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},[93,97,101,105,110,115,120,123,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]