Cyberspace Search Intentions as Leading Indicators for Proactive Traffic Hotspot Detection

[2608.14595] Cyberspace Search Intentions as Leading Indicators for Proactive Traffic Hotspot Detection Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Networking and Internet Architecture arXiv:2608.14595 (cs) [Submitted on 26 Jun 2026] Title: Cyberspace Search Intentions as Leading Indicators for Proactive Traffic Hotspot Detection Authors: Hangli Ge , Dizhi Huang , Takeshi Kawasaki , Noboru Koshizuka View a PDF of the paper titled Cyberspace Search Intentions as Leading Indicators for Proactive Traffic Hotspot Detection, by Hangli Ge and 3 other authors View PDF HTML (experimental) Abstract: This study proposes a cyber-physical data-driven framework for proactive detection of highway traffic hotspots and hot regions. The proposed framework bridges users' online search records in cyberspace as early indicator. To handle large-scale and irregular search records, we propose an Origin Destination Time (ODT) tensor model to represent the spatio-temporal structure of route search data and accelerate computation. Using destination-wise inflow sequences derived from these records, we develop a systematic method to automatically identify anomalous surges that indicate emerging traffic hotspots and further the regions. To validate the framework, we conduct experiments using a one-year real-world dataset covering 2,728 interchange (IC) nodes within a highway network. Furthermore, we integrate and compare search data with actual traffic volumes for evaluation. The results reveal a strong correlation between search intensity and traffic flow, demonstrating that online search behavior serves as a reliable proxy for anticipating traffic dynamics. These findings suggest that route search records in cyberspace can be effectively utilized for proactive traffic monitoring and highlight the potential for early prediction of congestion patterns. Comments: Accepted by IEEE SMC 2026 Subjects: Networking and Internet Architecture (cs.NI) Cite as: arXiv:2608.14595 [cs.NI] (or arXiv:2608.14595v1 [cs.NI] for this version) https://doi.org/10.48550/arXiv.2608.14595 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Hangli Ge [ view email ] [v1] Fri, 26 Jun 2026 08:01:04 UTC (3,065 KB) Full-text links: Access Paper: View a PDF of the paper titled Cyberspace Search Intentions as Leading Indicators for Proactive Traffic Hotspot Detection, by Hangli Ge and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.NI < prev | next > new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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