Real-time monitoring of traffic conditions using soft computing methods
For the past few years, as the Intelligent Transportation System (ITS) developing rapidly, intelligent transportation control and management has become a popular topic. In many countries, many people rely on the public transport system for commuting. Commuters concern more about the reliability and...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2019
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| Online Access: | http://hdl.handle.net/10356/77429 |
| _version_ | 1826118949819908096 |
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| author | Wu, Shuang |
| author2 | Er Meng Joo |
| author_facet | Er Meng Joo Wu, Shuang |
| author_sort | Wu, Shuang |
| collection | NTU |
| description | For the past few years, as the Intelligent Transportation System (ITS) developing rapidly, intelligent transportation control and management has become a popular topic. In many countries, many people rely on the public transport system for commuting. Commuters concern more about the reliability and punctuality of the public transport system. Therefore, the precise prediction of real-time traffic conditions has become the key of the transport management system. As is well-known, road traffic system is human-related, time-varying and complex massive system. It has high uncertainty, due to natural factors (season and weather) and artificial reasons (traffic accident and drivers’ mentality). These factors bring more challenges to the prediction of traffic flow, especially for short-term forecast. This thesis works on the short-term prediction which is different from macroscopic aspect. The approach is with the help of machine learning utilizing dynamic neural network. |
| first_indexed | 2024-10-01T04:51:56Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/77429 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:51:56Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | ntu-10356/774292023-07-07T17:34:46Z Real-time monitoring of traffic conditions using soft computing methods Wu, Shuang Er Meng Joo Justin Dauwels School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering For the past few years, as the Intelligent Transportation System (ITS) developing rapidly, intelligent transportation control and management has become a popular topic. In many countries, many people rely on the public transport system for commuting. Commuters concern more about the reliability and punctuality of the public transport system. Therefore, the precise prediction of real-time traffic conditions has become the key of the transport management system. As is well-known, road traffic system is human-related, time-varying and complex massive system. It has high uncertainty, due to natural factors (season and weather) and artificial reasons (traffic accident and drivers’ mentality). These factors bring more challenges to the prediction of traffic flow, especially for short-term forecast. This thesis works on the short-term prediction which is different from macroscopic aspect. The approach is with the help of machine learning utilizing dynamic neural network. Bachelor of Engineering (Electrical and Electronic Engineering) 2019-05-29T02:08:18Z 2019-05-29T02:08:18Z 2019 Final Year Project (FYP) http://hdl.handle.net/10356/77429 en Nanyang Technological University 48 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Wu, Shuang Real-time monitoring of traffic conditions using soft computing methods |
| title | Real-time monitoring of traffic conditions using soft computing methods |
| title_full | Real-time monitoring of traffic conditions using soft computing methods |
| title_fullStr | Real-time monitoring of traffic conditions using soft computing methods |
| title_full_unstemmed | Real-time monitoring of traffic conditions using soft computing methods |
| title_short | Real-time monitoring of traffic conditions using soft computing methods |
| title_sort | real time monitoring of traffic conditions using soft computing methods |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | http://hdl.handle.net/10356/77429 |
| work_keys_str_mv | AT wushuang realtimemonitoringoftrafficconditionsusingsoftcomputingmethods |