A machine learning approach to predicting and mitigating traffic congestion
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2024.
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| التنسيق: | أطروحة |
| اللغة: | English |
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Brac University
2024
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://hdl.handle.net/10361/23938 |
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10361-23938 |
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10361-239382024-10-01T08:56:33Z A machine learning approach to predicting and mitigating traffic congestion Faisal, Abu Fatah Mohammed Zahid, Chowdhury Zaber Bin Hasan, Walid Ibne Talukder, Shuvo Esfar-E-Alam, A.M. Department of Computer Science and Engineering, Brac University Traffic congestion Urban mobility Machine learning Traffic prediction Traffic management Traffic engineering--Data processing. Traffic congestion--Prediction. Traffic congestion--Prevention. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2024. Cataloged from PDF version of thesis. Includes bibliographical references (pages 36-38). Traffic congestion has notable effects on urban mobility, impacting thousands of people on a daily basis which hampers economic productivity and environmental sustainability. This research represents an extensive approach to address the multifaceted affairs of traffic jams through data analysis, machine learning modeling and prediction analysis. This research emphasizes four key dimensions. Such as traffic patterns, data preprocessing, model implementation and result analysis. This research starts by diving deep into the complex dynamics of the urban traffic jam, recognizing the crucial challenges such as outdated infrastructure, suboptimal traffic signal synchronization, and the unstable navigation system exemplified by Google Maps. Through diligent data exploration, data preprocessing, temporal features which we fetched from the dataset which enable a deeper understanding of traffic congestion patterns and temporal dependencies. By developing a robust machine learning model, leveraging the Random Forest Regressor, we have predicted the number of vehicles across four junctions. The Model class framework summarizes deferent preprocessing steps, model training, evaluation metric calculation and prediction abilities. The prediction capabilities of the model extend to forecasting future traffic volumes for the coming four months which empowers the stakeholders with proactive decision-making insights. Among the key takeaways that we can have from the research are the model’s versatility, adaptability to deferent traffic prediction scenarios, and its ability to capture temporal patterns and predict future outcomes. To conclude, the research presents a holistic framework for better comprehension, forecasting and optimization of the traffic patterns with effects which extend to the urban planning, infrastructure management and traffic management strategies. Abu Fatah Mohammed Faisal Chowdhury Zaber Bin Zahid Walid Ibne Hasan Shuvo Talukder B.Sc. in Computer Science and Engineering 2024-08-29T04:14:34Z 2024-08-29T04:14:34Z ©2024 2024-03 Thesis ID 20301240 ID 20301256 ID 20301103 ID 23141068 http://hdl.handle.net/10361/23938 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. 43 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Traffic congestion Urban mobility Machine learning Traffic prediction Traffic management Traffic engineering--Data processing. Traffic congestion--Prediction. Traffic congestion--Prevention. |
| spellingShingle |
Traffic congestion Urban mobility Machine learning Traffic prediction Traffic management Traffic engineering--Data processing. Traffic congestion--Prediction. Traffic congestion--Prevention. Faisal, Abu Fatah Mohammed Zahid, Chowdhury Zaber Bin Hasan, Walid Ibne Talukder, Shuvo A machine learning approach to predicting and mitigating traffic congestion |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2024. |
| author2 |
Esfar-E-Alam, A.M. |
| author_facet |
Esfar-E-Alam, A.M. Faisal, Abu Fatah Mohammed Zahid, Chowdhury Zaber Bin Hasan, Walid Ibne Talukder, Shuvo |
| format |
Thesis |
| author |
Faisal, Abu Fatah Mohammed Zahid, Chowdhury Zaber Bin Hasan, Walid Ibne Talukder, Shuvo |
| author_sort |
Faisal, Abu Fatah Mohammed |
| title |
A machine learning approach to predicting and mitigating traffic congestion |
| title_short |
A machine learning approach to predicting and mitigating traffic congestion |
| title_full |
A machine learning approach to predicting and mitigating traffic congestion |
| title_fullStr |
A machine learning approach to predicting and mitigating traffic congestion |
| title_full_unstemmed |
A machine learning approach to predicting and mitigating traffic congestion |
| title_sort |
machine learning approach to predicting and mitigating traffic congestion |
| publisher |
Brac University |
| publishDate |
2024 |
| url |
http://hdl.handle.net/10361/23938 |
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