[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126604-en":3,"doc-seo-126604-105":31,"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126604,687207020761,"Patrick","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","A MACHINE LEARNING METHOD FOR PREDICTING TRAFFIC SIGNAL TIMING FROM PROBE VEHICLE DATA","Traffic signals are crucial for managing traffic flow and ensuring safety at intersections, while accurate phase and timing information can enable optimal vehicle routing, eco-driving, and realistic simulation of signalized road networks. This paper introduces a machine learning approach that estimates traffic signal timing from probe vehicle data. An Extreme Gradient Boosting model predicts signal cycle lengths, and a neural network estimates red times per phase, with green times derived from cycle and red-time predictions. Results report low cycle-length error and strong red-time accuracy on average.","arXiv :2308 .02370v1 [ cs .LG] 4 Aug 2023  \nA MACHINE LEARNING METHOD FOR PREDICTING TRAFFIC SIGNAL TIMING FROM PROBE VEHICLE DATA  \nJuliette Ugirumurera Joseph Severino Erik A. Bensen  \nNational Renewable Energy Laboratory National Renewable Energy Laboratory Carnegie Mellon University  \nQichao Wang  \nNational Renewable Energy Laboratory  \nJane Macfarlane  \nUC Berkeley  \nABSTRACT  \nTraffic signals play an important role in transportation by enabling traffic flow management, and ensuring safety at intersections. In addition, knowing the traffic signal phase and timing data can allow optimal vehicle routing for time and energy efficiency, eco-driving, and the accurate simulation of signalized road networks. In this paper, we present a machine learning (ML) method for estimating traffic signal timing information from vehicle probe data. To the authors best knowledge, very few works have presented ML techniques for determining traffic signal timing parameters from vehicle probe data. In this work, we develop an Extreme Gradient Boosting (XGBoost) model to estimate signal cycle lengths and a neural network model to determine the corresponding red times per phase from probe data. The green times are then be derived from the cycle length and red times. Our results show an error of less than 0.56 sec for cycle length, and red times predictions within 7.2 sec error on average.  \nKeywords Traffic Signal Timing, Machine Learning, Probe Vehicle Data  \n1 INTRODUCTION  \nIn traffic networks, traffic signals play a key role in determining and managing vehicular traffic flow. They control the flow of traffic, ensure safety by regulating the flow of competing movements through intersections and can reduce traffic congestion when optimized [1, 2] . However, they can also induce stop-and-go traffic, which increases vehicles’delay and fuel consumption. In addition, not knowing the traffic signals’ timings and switching patterns makes it very challenging to accurately determine the most time-efficient or energy-efficient routes, to inform driving decisions to maximize arrival on green or minimize engine idling at intersections [3], and to correctly simulate traffic for signal-controlled regions.  \nUsually, traffic lights are managed by different local agencies. For example, in the US, hundreds of agencies are in charge of the more than 320,000 traffic lights installed [4] . This makes direct access to traffic signal timing and phase data a very challenging task. In this paper, we present a machine learning method that uses probe vehicle data to estimate the timings for pre-timed traffic signals. We demonstrate this algorithm on probe data generated from a well-calibrated microscopic simulation using the SUMO simulator[5] and the NEMA Type controller in SUMO [6] . We focus on fixed-time traffic signals since they represent the majority of traffic lights in the US [7] .  \n2 RELATED WORKS  \nProbe vehicle data has enabled many important tasks in transportation research, including estimating link-level hourly traffic volume [8], queue length estimation [9], and traffic signal control [10] . The availability of and opportunities to exploit probe vehicle data will only continue to grow due to the predicted increase in connected and automated vehicle (CAV) adoption and the increased availability of high-speed communication infrastructure. Probe vehicle data has also been used broadly for estimating traffic signal timings. In [3], the authors uses statistical patterns in probe data  \nPredicting Traffic Signal Timing from Probe Vehicle Data  \nfrom public buses in San Fransisco, California, USA, to estimate the cycle times, the duration of red times and the greens start times for fixed-timed traffic lights. Yu and Lu estimated cycle length for fixed time intersections using low frequency taxi trajectory data[11] . They formulate the cycle length estimation problem as a general approximate greatest common divisor (AGCD) problem and solved it with a most frequ","cbCaihIFO2gYx8tz","https://ap.wps.com/l/cbCaihIFO2gYx8tz","pdf",2029047,2,1,10,"English","en",105,"# Abstract\n# Introduction\n# Related Works","[{\"question\":\"How does the proposed method estimate traffic signal timing from probe vehicle data?\",\"answer\":\"It uses an XGBoost model to estimate signal cycle lengths and a neural network to predict red times per phase from probe data. Green times are then derived from cycle length and red times.\"},{\"question\":\"Why is traffic signal timing information important for transportation tasks?\",\"answer\":\"Knowing phase and timing enables more efficient vehicle routing, supports eco-driving and reduces unnecessary idling, and improves the accuracy of traffic simulations for signal-controlled road regions.\"},{\"question\":\"What performance is reported for the cycle length and red-time predictions?\",\"answer\":\"The reported cycle-length error is below 0.56 seconds, and red-time predictions have an average error within 7.2 seconds.\"}]","A MACHINE LEARNING METHOD FOR PREDICTING TRAFFIC SIGNAL TIMING FROM PROBE VEHICLE DATA | PDF",1785933676,25,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"a-machine-learning-method-for-predicting-traffic-signal-timing-from-probe-vehicle-data","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/a-machine-learning-method-for-predicting-traffic-signal-timing-from-probe-vehicle-data/126604/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-24","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},"How does the proposed method estimate traffic signal timing from probe vehicle data?","Question",{"text":76,"@type":77},"It uses an XGBoost model to estimate signal cycle lengths and a neural network to predict red times per phase from probe data. Green times are then derived from cycle length and red times.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Why is traffic signal timing information important for transportation tasks?",{"text":81,"@type":77},"Knowing phase and timing enables more efficient vehicle routing, supports eco-driving and reduces unnecessary idling, and improves the accuracy of traffic simulations for signal-controlled road regions.",{"name":83,"@type":74,"acceptedAnswer":84},"What performance is reported for the cycle length and red-time predictions?",{"text":85,"@type":77},"The reported cycle-length error is below 0.56 seconds, and red-time predictions have an average error within 7.2 seconds.","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":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":22,"slug":134},"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]