A novel approach to forecast traffic congestion using CMTF and machine learning
This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.
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| Ձևաչափ: | Թեզիս |
| Լեզու: | English |
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BRAC University
2018
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| Առցանց հասանելիություն: | http://hdl.handle.net/10361/10121 |
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10361-10121 |
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10361-101212022-01-26T10:20:05Z A novel approach to forecast traffic congestion using CMTF and machine learning Chowdhury, Md. Mohiuddin Hasan, Mahmudul Safait, Saimoom Uddin, Jia Chaki, Dipankar Department of Computer Science and Engineering, BRAC University Traffic congestion Traffic data Forecast Machine learning CMTF This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. Cataloged from PDF version of thesis. Includes bibliographical references (pages 30-31). Traffic congestion severely affects many cities around the world causing various problems like fuel wastage, increased stress levels, delayed deliveries and monetary losses. Therefore, it is urgent to make an accurate prediction of traffic jams to minimize these losses. But forecasting is a real challenge to obtain promising results for vibrant and ambiguous traffic flows in urban networks. This paper proposes a new traffic congestion model using pre-calculated density from node information table based on previous traffic data. In this model, we predicted traffic congestion of an intersection according to its adjacent road's node information table, where node information table contains the traffic density of all incoming lanes of an intersection (node). Besides, for this model, we consider all intersections of a city as individual nodes, and we prepare node information table for each node. Our work can be divided into two parts: (1) we perform time series analysis on previous data of a node and its adjacent nodes, and (2) then apply those calculated values to this model and make the prediction based on it. The forecasted value will always be between 0 and 1. Where 0 means no traffic congestion, close to 0 means low traffic congestion and 1 means heavy traffic or close to 1 means congested traffic lane accordingly. Md. Mohiuddin Chowdhury Mahmudul Hasan Saimoom Safait B. Computer Science and Engineering 2018-05-10T10:07:34Z 2018-05-10T10:07:34Z 2018 2018-04 Thesis ID 13101198 ID 13101165 ID 13101197 http://hdl.handle.net/10361/10121 en BRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 31 pages application/pdf BRAC University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Traffic congestion Traffic data Forecast Machine learning CMTF |
| spellingShingle |
Traffic congestion Traffic data Forecast Machine learning CMTF Chowdhury, Md. Mohiuddin Hasan, Mahmudul Safait, Saimoom A novel approach to forecast traffic congestion using CMTF and machine learning |
| description |
This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. |
| author2 |
Uddin, Jia |
| author_facet |
Uddin, Jia Chowdhury, Md. Mohiuddin Hasan, Mahmudul Safait, Saimoom |
| format |
Thesis |
| author |
Chowdhury, Md. Mohiuddin Hasan, Mahmudul Safait, Saimoom |
| author_sort |
Chowdhury, Md. Mohiuddin |
| title |
A novel approach to forecast traffic congestion using CMTF and machine learning |
| title_short |
A novel approach to forecast traffic congestion using CMTF and machine learning |
| title_full |
A novel approach to forecast traffic congestion using CMTF and machine learning |
| title_fullStr |
A novel approach to forecast traffic congestion using CMTF and machine learning |
| title_full_unstemmed |
A novel approach to forecast traffic congestion using CMTF and machine learning |
| title_sort |
novel approach to forecast traffic congestion using cmtf and machine learning |
| publisher |
BRAC University |
| publishDate |
2018 |
| url |
http://hdl.handle.net/10361/10121 |
| work_keys_str_mv |
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| _version_ |
1814309186023981056 |