Traffic Analysis Using Artificial Neural Network Article Swipe
B Mounica
,
B. Nithya
,
N Rakshitha
,
M Sirisha
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.32628/cseit217494
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.32628/cseit217494
The vehicle traffic on the road is increasing progressively and managing such traffic on the roads are not stable by conventional method. To remove this traffic issue, we develop a project using machine learning in which we train the testing model as well as trained model of extracted traffic features. Extracted information from image sequences of testing model can give us real information to create the database which is the captured images like accident, foggy places, collision of the vehicles, traffic signal, no traffic jam, treefall etc. Choose any traffic image from the testing model, process and analyze the traffic image and the traffic image which was taken from the testing model is compared with the trained model of traffic images to determine the cause of the traffic. Image processing will be done to determine the cause of the traffic. This project is utilizing image processing methods designed to analyze and determine the cause of the traffic with the accuracy of the traffic caused. Thus, by using this project we can avoid the traffic and the time being wasted.
Related Topics
Concepts
Computer science
Artificial neural network
Image processing
Traffic accident
Process (computing)
Artificial intelligence
Image (mathematics)
Floating car data
Collision
Deep learning
Traffic generation model
Computer vision
Real-time computing
Data mining
Traffic congestion
Transport engineering
Engineering
Computer security
Operating system
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.32628/cseit217494
- https://doi.org/10.32628/cseit217494
- OA Status
- diamond
- References
- 3
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3187620099
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3187620099Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.32628/cseit217494Digital Object Identifier
- Title
-
Traffic Analysis Using Artificial Neural NetworkWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-07-20Full publication date if available
- Authors
-
B Mounica, B. Nithya, N Rakshitha, M SirishaList of authors in order
- Landing page
-
https://doi.org/10.32628/cseit217494Publisher landing page
- PDF URL
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https://doi.org/10.32628/cseit217494Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.32628/cseit217494Direct OA link when available
- Concepts
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Computer science, Artificial neural network, Image processing, Traffic accident, Process (computing), Artificial intelligence, Image (mathematics), Floating car data, Collision, Deep learning, Traffic generation model, Computer vision, Real-time computing, Data mining, Traffic congestion, Transport engineering, Engineering, Computer security, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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3Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.issue, | 26 |
| abstract_inverted_index.model, | 94 |
| abstract_inverted_index.remove | 23 |
| abstract_inverted_index.stable | 18 |
| abstract_inverted_index.analyze | 97, 149 |
| abstract_inverted_index.caused. | 163 |
| abstract_inverted_index.develop | 28 |
| abstract_inverted_index.machine | 32 |
| abstract_inverted_index.method. | 21 |
| abstract_inverted_index.methods | 146 |
| abstract_inverted_index.places, | 75 |
| abstract_inverted_index.process | 95 |
| abstract_inverted_index.project | 30, 141, 168 |
| abstract_inverted_index.signal, | 81 |
| abstract_inverted_index.testing | 39, 56, 93, 110 |
| abstract_inverted_index.traffic | 2, 12, 25, 48, 80, 83, 89, 99, 103, 119, 156, 162, 173 |
| abstract_inverted_index.trained | 44, 116 |
| abstract_inverted_index.vehicle | 1 |
| abstract_inverted_index.wasted. | 178 |
| abstract_inverted_index.accuracy | 159 |
| abstract_inverted_index.captured | 70 |
| abstract_inverted_index.compared | 113 |
| abstract_inverted_index.database | 66 |
| abstract_inverted_index.designed | 147 |
| abstract_inverted_index.learning | 33 |
| abstract_inverted_index.managing | 10 |
| abstract_inverted_index.traffic. | 127, 139 |
| abstract_inverted_index.treefall | 85 |
| abstract_inverted_index.Extracted | 50 |
| abstract_inverted_index.accident, | 73 |
| abstract_inverted_index.collision | 76 |
| abstract_inverted_index.determine | 122, 134, 151 |
| abstract_inverted_index.extracted | 47 |
| abstract_inverted_index.features. | 49 |
| abstract_inverted_index.sequences | 54 |
| abstract_inverted_index.utilizing | 143 |
| abstract_inverted_index.vehicles, | 79 |
| abstract_inverted_index.increasing | 7 |
| abstract_inverted_index.processing | 129, 145 |
| abstract_inverted_index.information | 51, 62 |
| abstract_inverted_index.conventional | 20 |
| abstract_inverted_index.progressively | 8 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.5299999713897705 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.0879386 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |