[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85742-en":3,"doc-seo-85742-105":29,"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":13,"seo_description":14,"update_tm":27,"read_time":28},85742,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Distributed Traffic State Estimation in Connected Vehicle and Roadside Infrastructure Networks","A distributed traffic state estimation framework fuses roadside infrastructure sensors and connected vehicles as cooperative sensing nodes via V2X. Local estimates are exchanged and integrated using a distributed Kalman filter tailored to a second-order macroscopic traffic flow model. A consensus fusion step aggregates heterogeneous network information, while projection steps enforce physically consistent traffic states. Evaluation on HighD, NGSIM, and microscopic SUMO simulations reconstructs highway traffic states and detects nonlinear shockwave dynamics under sparse infrastructure sensing and intermittent connectivity. Statistical results quantify impacts of CV penetration, V2X range, and RSU deployment, showing strong gains over infrastructure-only or CV-only methods at low penetration.","Distributed Traffic State Estimation in Connected Vehicle and Roadside Infrastructure Networks  \nVincent de Heij, M. Umar B. Niazi, Saeed Ahmed, Karl H. Johansson  \narXiv :2607 .09874v1 [ ee ss . SY] 10 Jul 2026  \nAbstract—This paper proposes a distributed traffic state estimation framework that combines infrastructure sensors and connected vehicles as cooperative sensing nodes. Using Vehicle-toEverything (V2X) communication, nearby nodes exchange local estimates and update them through a distributed Kalman filter designed for a second-order macroscopic traffic flow model. A consensus step fuses heterogeneous information across the network, while projection steps enforce physically consistent traffic states. We evaluate the method on HighD and NGSIM data, and on microscopic SUMO simulations that capture transient congestion. The results show accurate reconstruction of highway traffic states and detection of nonlinear shockwave dynamics, even with sparse infrastructure sensing and intermittent vehicular connectivity. A statistical analysis further shows how CV penetration rate, V2X communication range, and infrastructure deployment affect estimation accuracy. In particular, with 10% CV penetration, V2X ranges of 300-400 m, and sparse infrastructure deployment, the combined infrastructure-vehicle configuration consistently outperforms approaches that rely only on infrastructure or only on connected vehicles.  \nIndex Terms—Distributed traffic state estimation; connected vehicles; roadside units; V2X communication; distributed Kalman filter; second-order traffic flow model; ARZ model.  \nI. INTRODUCTION  \nModern intelligent transportation systems, especially those using cooperative driving strategies for connected and automated vehicles [1], require accurate real-time macroscopic traffic states such as density, velocity, and flow. Because deploying fixed roadside sensor units (RSUs) to satisfy observability requirements is expensive, traffic state estimation (TSE) reconstructs these states by fusing data from existing sensors. RSUs, such as induction loops, radars, and cameras, provide accurate but sparse and stationary measurements. Connected vehicles (CVs), equipped with onboard sensors such as GPS, cameras, and LiDAR, provide local microscopic observations. Combining both sources enables multi-scale monitoring of traffic conditions.  \nUntil recently, TSE has relied mainly on fixed infrastructure, with mobile data supplied only intermittently by probe  \nThis work was supported by NXTGEN HighTech growthfund: Autonomous Factory, the Swedish Research Council’s Distinguished Professor Grant, the Knut and Alice Wallenberg Foundation’s Wallenberg Scholar Grant, Digital Futures’ Summer Research Internship Programme, and the Holland High Tech (TKI HTSM) strategic program PPS-I Flex HighTech under the project number 24PPS173-CABS.  \nVincent de Heij and Saeed Ahmed are with the Engineering and Technology Institute Groningen, Faculty of Science and Engineering, University of Groningen, 9747 AG Groningen, The Netherlands (emails: [v.de.heij@rug.nl](v.de.heij@rug.nl), [s.ahmed@rug.nl](s.ahmed@rug.nl)).  \nM. Umar B. Niazi and Karl H. Johansson are with the Department of Decision and Control Systems and with Digital Futures, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden (emails: [mubniazi@kth.se](mubniazi@kth.se), [kallej@kth.se](kallej@kth.se)).  \nvehicles that report aggregated trajectories rather than realtime measurements [2] . Most existing methods also use a centralized fusion architecture, where a transportation management center acts as a global estimator. This approach does not scale well and ignores key communication constraints in V2X networks. V2X protocols such as DSRC and C-V2X support only short-range direct communication, typically over a few hundred meters [3]–[6] . As CV penetration increases [7], the resulting data volume can exceed available bandwidth, causing latency and high computational cost [8] . 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A consensus step fuses heterogeneous information, and projection steps enforce physically consistent traffic states.\"},{\"question\":\"Which datasets and simulation settings are used to evaluate the approach?\",\"answer\":\"The framework is evaluated on HighD and NGSIM datasets, and on microscopic SUMO simulations that capture transient congestion and shockwave dynamics.\"}]",1784205961,40,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"distributed-traffic-state-estimation-in-connected-vehicle-and-roadside-infrastructure-networks","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/distributed-traffic-state-estimation-in-connected-vehicle-and-roadside-infrastructure-networks/85742/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",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 data sources does the proposed distributed traffic state estimation framework use?","Question",{"text":75,"@type":76},"It combines fixed infrastructure sensor measurements from roadside units with local observations from connected vehicles. These nodes cooperate by exchanging information over V2X communication.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are the exchanged information and traffic dynamics fused in the framework?",{"text":80,"@type":76},"The method uses a distributed Kalman filter for a second-order macroscopic traffic flow model. A consensus step fuses heterogeneous information, and projection steps enforce physically consistent traffic states.",{"name":82,"@type":73,"acceptedAnswer":83},"Which datasets and simulation settings are used to evaluate the approach?",{"text":84,"@type":76},"The framework is evaluated on HighD and NGSIM datasets, and on microscopic SUMO simulations that capture transient congestion and shockwave dynamics.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":45,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":45,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":45,"category_name":117,"show_sort_weight":28,"slug":118},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":45,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":45,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]