{"schema_version":"1.0","canonical_url":"https://patentable.app/patents/US-11270579","patent":{"patent_number":"US-11270579","title":"Transportation network speed foreeasting method using deep capsule networks with nested LSTM models","assignee":null,"inventors":[],"filing_date":"2019-04-16T00:00:00.000Z","publication_date":"2022-03-08T00:00:00.000Z","cpc_codes":["G06N","G08G","G06N","G06N","G06N","G06N","G08G","G08G","G08G","G06N"],"num_claims":6,"abstract":"This application is a transportation network speed forecasting method using deep capsule networks with nested LSTM models. The method includes the following steps: (1) This method divides the transport network into road links, calculates average speeds of each road link, maps the average speeds into a grid system, and generate traffic images representing traffic state at time intervals; (2) the method uses a CapsNet to capture the spatial relationship between road links. The learn patterns are represented in vectors; (3) The vectors of CapsNet are feed into a NLSTM model to learn temporal relationships between road links; (4) The model is trained using and training dataset, and predicts future traffic states using testing dataset. This application uses a new and advanced CapsNet neural structure, while can more efficiently deal with complex traffic networks than CNN models."},"analysis":{"summary":null,"layman_explanation":null,"technical_analysis":null,"business_analysis":null,"faqs":null,"topics":[],"tech_cluster":null},"seo":{"title":"Transportation network speed foreeasting method using deep capsule networks with nested LSTM models","description":"This application is a transportation network speed forecasting method using deep capsule networks with nested LSTM models. The method includes the following steps: (1) This method divides the transpor","keywords":[]},"attribution":{"source":"Patentable","source_url":"https://patentable.app","canonical_url":"https://patentable.app/patents/US-11270579","license":"CC-BY-4.0-like","license_terms":"AI-generated analysis on this page (summary, layman_explanation, technical_analysis, business_analysis, faqs) may be reused with attribution and a visible link back to the canonical URL above. Patent abstracts, claims, and bibliographic data are USPTO public domain.","required_link":"https://patentable.app/patents/US-11270579","citation_suggestion":"Patentable. \"Transportation network speed foreeasting method using deep capsule networks with nested LSTM models\" (US-11270579). https://patentable.app/patents/US-11270579","copyright_holder":"Nomic Interactive Technology LLC"},"links":{"html":"https://patentable.app/patents/US-11270579","json":"https://patentable.app/api/llm-context/US-11270579","site":"https://patentable.app","llms_txt":"https://patentable.app/llms.txt"},"generated_at":"2026-05-30T07:35:04.058Z"}