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Traditional discovery remains expert-driven, as candidate regularities must be extracted from heterogeneous observations and validated through interventions. Although AI discovery systems excel in controlled labs, applying them to complex transportation is difficult. TrafficSci is introduced as an agentic, auditable workflow for evidence scoping, critic–judge hypothesis induction, and observational–interventional validation.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/autonomous-discovery-of-traffic-laws-with-ai-traffic-scientists-abstract/327161/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/autonomous-discovery-of-traffic-laws-with-ai-traffic-scientists-abstract/327161.png","ImageObject",300,407,{"name":92,"@type":93},"Ivy","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-11","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",16,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What challenge does the paper focus on in discovering traffic laws?","Question",{"text":113,"@type":114},"It targets the difficulty of moving from expert-driven identification and validation of traffic-law regularities to autonomous AI-driven discovery in complex, non-stationary urban domains.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How does TrafficSci operationalize traffic-law discovery?",{"text":118,"@type":114},"TrafficSci formulates discovery as an iterative, auditable workflow that integrates evidence scoping, critic–judge hypothesis induction, and observational–interventional validation.",{"name":120,"@type":111,"acceptedAnswer":121},"What results does TrafficSci report from the case studies?",{"text":122,"@type":114},"Across four case studies, it rediscovers three established traffic laws and identifies an unreported intrinsic temporal memory scale consistent across eight cities and two trajectory datasets.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},327161,1790485940,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":56,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},549758252649,"https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819","arXiv :2607 .0 1639v 1 [ cs .AI] 2 Jul 2026  \nAutonomous discovery of traffic laws with AI traffic scientists  \nXingyuan Dai 1†, Yue Liu 1,2†, Xiaoyan Gong 1†, Qinghai Miao2†, Junyou Shang 1,2 , Yutong Wang 1 , Chao Guo 1 , Yonglin Tian 1 , Yizhang Chai 1,2 , Chao Xiang3 ,  \nYisheng Lv 1,4*, Fei-Yue Wang4,5*  \n1 State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.  \n2 School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China.  \n3 China Telecom Research Institute, Beijing, 102209, China.  \n4 Macau Institute of Systems Engineering, Macau University of Science and Technology, Macao 999078, China.  \n5 State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.  \n*Corresponding author(s). E-mail(s): [yisheng.lv@ia.ac.cn](yisheng.lv@ia.ac.cn) ; [feiyue.wang@ia.ac.cn](feiyue.wang@ia.ac.cn) ;†These authors contributed equally to this work.  \nAbstract  \nUniversal traffic laws describe recurrent patterns in congestion, mobility and driving behavior across cities, providing a scientific basis for transportation planning, management and control. Their discovery, however, remains expert-driven, requiring candidate regularities to be identified from heterogeneous observational evidence or validated through intervention experiments. Although autonomous artificial intelligence (AI) systems have advanced scientific discovery in controlled laboratory settings, extending them to complex transportation domains remains a challenge. Here we present TrafficSci, an agentic AI system that formulates traffic-law discovery as an iterative, auditable workflow integrating evidencescoping, critic–judge hypothesis induction, and observational–interventional validation. Across four case studies spanning population, network, control and trajectory scales, TrafficSci autonomously rediscovers three established traffic laws and identifies an unreported intrinsic temporal memory scale in urban driving behavior, statistically consistent across eight cities and two trajectory datasets. TrafficSci provides a route for extending AI-driven scientific discovery from controlled domains to complex urban systems.  \nUrban transportation networks are among the most complex systems that cities should actively manage. Decisions on congestion mitigation, signal deployment, route guidance and long-term infrastructure investment all rest, ultimately, on quantitative understanding of how traffic behaves: how congestion costs distribute across a network, how populations allocate trips over space and time, how individual drivers respond to preceding conditions, and how the benefits of a control technology scale with its deployment level. Such understanding is encoded in traffic laws, concise and testable regularities that recur across operating conditions and, crucially, across cities [1–5] . These laws provide interpretable priors for prediction, principled boundaries for control design, and transferable foundations for urban planning [6–9] . However, the set of established traffic laws remains limited relative to the complexity of urban transportation systems. Data-driven prediction and control models have advanced at a remarkable pace [10], but the interpretable scientific regularities on which robust traffic management ultimately depends have not kept step, leaving a widening gap between the field’s capacity to observe and optimize increasingly complex traffic systems and its capacity to explain why particular interventions work and where their limits lie.  \nThe difficulty of closing this gap is both practical and structural. A conventional traffic-law study begins with manual literature synthesis, proceeds through iterative variable and metric design, and demands substantial implementation effort, including data selection, cleaning and bespoke analysis code, bef","cbCaigkkwNoaPsII","https://ap.wps.com/l/cbCaigkkwNoaPsII","pdf",3773152,"English","# Abstract\n## Traffic-law significance and discovery challenge\n## TrafficSci system and workflow\n## Case studies and findings","[{\"question\":\"What challenge does the paper focus on in discovering traffic laws?\",\"answer\":\"It targets the difficulty of moving from expert-driven identification and validation of traffic-law regularities to autonomous AI-driven discovery in complex, non-stationary urban domains.\"},{\"question\":\"How does TrafficSci operationalize traffic-law discovery?\",\"answer\":\"TrafficSci formulates discovery as an iterative, auditable workflow that integrates evidence scoping, critic–judge hypothesis induction, and observational–interventional validation.\"},{\"question\":\"What results does TrafficSci report from the case studies?\",\"answer\":\"Across four case studies, it rediscovers three established traffic laws and identifies an unreported intrinsic temporal memory scale consistent across eight cities and two trajectory datasets.\"}]","Autonomous discovery of traffic laws with AI traffic scientists - Abstract | PDF",1789976569,48]