Research on prediction of traffic flow based on GEBF-OSFNN
Efficient transport and communication systems lay the groundwork for Singapore’s urban development. However, growing population, economic and commercial progress, and high number of vehicle ownership licenses have resulted in overcrowding and congestions. Hence, it is imperative to use intelligent s...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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2019
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| Online Access: | http://hdl.handle.net/10356/78251 |
| _version_ | 1826112909735886848 |
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| author | Badjate, Harsh Vijaykumar |
| author2 | Justin Dauwels |
| author_facet | Justin Dauwels Badjate, Harsh Vijaykumar |
| author_sort | Badjate, Harsh Vijaykumar |
| collection | NTU |
| description | Efficient transport and communication systems lay the groundwork for Singapore’s urban development. However, growing population, economic and commercial progress, and high number of vehicle ownership licenses have resulted in overcrowding and congestions. Hence, it is imperative to use intelligent systems to analyse, predict and control traffic, saving resources. Intelligent transport system (ITS) was invented that monitors and collects traffic data using surveillance devices and processes that data to help curb congestion and avoid accidents. As the backbone of ITS, traffic guidance systems rely heavily on accurate prediction of traffic flow. Hence, traffic flow prediction has been an important research subject. In this project, chaos theory, and Generalised Ellipsoidal Basis Function Based Online Self-Constructing Fuzzy Neural Network (GEBF-OSFNN) is adopted to predict short-term traffic flow. The proposed technique will facilitate traffic analysis and prediction capabilities as well as provide a comprehensive platform for traffic management solutions. |
| first_indexed | 2024-10-01T03:14:50Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/78251 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:14:50Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | ntu-10356/782512023-07-07T15:54:11Z Research on prediction of traffic flow based on GEBF-OSFNN Badjate, Harsh Vijaykumar Justin Dauwels Er Meng Joo School of Electrical and Electronic Engineering Centre for Transportation Studies DRNTU::Engineering::Electrical and electronic engineering Efficient transport and communication systems lay the groundwork for Singapore’s urban development. However, growing population, economic and commercial progress, and high number of vehicle ownership licenses have resulted in overcrowding and congestions. Hence, it is imperative to use intelligent systems to analyse, predict and control traffic, saving resources. Intelligent transport system (ITS) was invented that monitors and collects traffic data using surveillance devices and processes that data to help curb congestion and avoid accidents. As the backbone of ITS, traffic guidance systems rely heavily on accurate prediction of traffic flow. Hence, traffic flow prediction has been an important research subject. In this project, chaos theory, and Generalised Ellipsoidal Basis Function Based Online Self-Constructing Fuzzy Neural Network (GEBF-OSFNN) is adopted to predict short-term traffic flow. The proposed technique will facilitate traffic analysis and prediction capabilities as well as provide a comprehensive platform for traffic management solutions. Bachelor of Engineering (Electrical and Electronic Engineering) 2019-06-14T02:40:14Z 2019-06-14T02:40:14Z 2019 Final Year Project (FYP) http://hdl.handle.net/10356/78251 en Nanyang Technological University 68 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Badjate, Harsh Vijaykumar Research on prediction of traffic flow based on GEBF-OSFNN |
| title | Research on prediction of traffic flow based on GEBF-OSFNN |
| title_full | Research on prediction of traffic flow based on GEBF-OSFNN |
| title_fullStr | Research on prediction of traffic flow based on GEBF-OSFNN |
| title_full_unstemmed | Research on prediction of traffic flow based on GEBF-OSFNN |
| title_short | Research on prediction of traffic flow based on GEBF-OSFNN |
| title_sort | research on prediction of traffic flow based on gebf osfnn |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | http://hdl.handle.net/10356/78251 |
| work_keys_str_mv | AT badjateharshvijaykumar researchonpredictionoftrafficflowbasedongebfosfnn |