Trajectory Data Mining and Trip Travel Time Prediction on Specific Roads
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866908369528291328 |
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| author | Amin, Muhammad Awais Chughtai, Jawad-Ur-Rehman Ahmad, Waqar Bangyal, Waqas Haider Haq, Irfan Ul |
| author_facet | Amin, Muhammad Awais Chughtai, Jawad-Ur-Rehman Ahmad, Waqar Bangyal, Waqas Haider Haq, Irfan Ul |
| contents | Predicting a trip's travel time is essential for route planning and navigation applications. The majority of research is based on international data that does not apply to Pakistan's road conditions. We designed a complete pipeline for mining trajectories from sensors data. On this data, we employed state-of-the-art approaches, including a shallow artificial neural network, a deep multi-layered perceptron, and a long-short-term memory, to explore the issue of travel time prediction on frequent routes. The experimental results demonstrate an average prediction error ranging from 30 seconds to 1.2 minutes on trips lasting 10 minutes to 60 minutes on six most frequent routes in regions of Islamabad, Pakistan. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_07030 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Trajectory Data Mining and Trip Travel Time Prediction on Specific Roads Amin, Muhammad Awais Chughtai, Jawad-Ur-Rehman Ahmad, Waqar Bangyal, Waqas Haider Haq, Irfan Ul Artificial Intelligence Predicting a trip's travel time is essential for route planning and navigation applications. The majority of research is based on international data that does not apply to Pakistan's road conditions. We designed a complete pipeline for mining trajectories from sensors data. On this data, we employed state-of-the-art approaches, including a shallow artificial neural network, a deep multi-layered perceptron, and a long-short-term memory, to explore the issue of travel time prediction on frequent routes. The experimental results demonstrate an average prediction error ranging from 30 seconds to 1.2 minutes on trips lasting 10 minutes to 60 minutes on six most frequent routes in regions of Islamabad, Pakistan. |
| title | Trajectory Data Mining and Trip Travel Time Prediction on Specific Roads |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2407.07030 |