http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-111613054-A
Outgoing Links
| Predicate | Object |
|---|---|
| assignee | http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_d6a6f422b091ba12ea61d4adbf1b0e8e |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-214 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-2411 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G08G1-0104 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-04 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-62 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G08G1-01 |
| filingDate | 2020-05-07-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor | http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_ffb411ba11fd4da093a348f0ce86ce66 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_80bd96db1086cb38c1945f98923e91fd |
| publicationDate | 2020-09-01-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-111613054-A |
| titleOfInvention | A Multi-step Traffic Speed Prediction Method Synergistically Considering Spatio-temporal Correlations and Contribution Differences |
| abstract | The invention discloses a multi-step traffic speed prediction method that synergistically considers temporal-spatial correlation and contribution differences. The method uses an encoding-decoding network architecture based on a cyclic neural network to fully express the time-series characteristics of traffic speeds. In the coding part, the first-stage attention mechanism is introduced into the input vector composed of the speed values of the relevant road segments, so that it can adaptively learn the weight contribution of different relevant road segments at different times; in the decoding part, the second-stage attention mechanism is introduced , to adaptively learn the weight contribution of different historical moments to the current prediction moment. At the same time, considering the influence of external factors, the output of the decoder and the features of external factors are input into the fully connected neural network to obtain the final output. This method can describe the spatial-temporal correlation characteristics of traffic data in a finer-grained and differentiated manner, and can perform multi-step traffic speed prediction, which points out a new direction for the research of traffic speed prediction methods. |
| isCitedBy | http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-113391622-B http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-113391622-A http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-112652165-A http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-113077053-B http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-113077053-A http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-114387782-A |
| priorityDate | 2020-05-07-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
Total number of triples: 26.