Public transport delay prediction using deep learning
A system of trains, buses, subways, ferries, and other vehicles that are accessible to the public is referred to as public transportation, also known as public transit or mass transit. These networks are usually run by private or public organisations are made to efficiently transport large numbers o...
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| Format: | Article |
| Language: | English |
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Semarak Ilmu Publishing
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/119055/ http://psasir.upm.edu.my/id/eprint/119055/1/119055.pdf |
| _version_ | 1848867860606091264 |
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| author | Mohd Rum, Siti Nurulain Meor Yusoff, Meor Muhammad Nazmi Mahdi, Amalia |
| author_facet | Mohd Rum, Siti Nurulain Meor Yusoff, Meor Muhammad Nazmi Mahdi, Amalia |
| author_sort | Mohd Rum, Siti Nurulain |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | A system of trains, buses, subways, ferries, and other vehicles that are accessible to the public is referred to as public transportation, also known as public transit or mass transit. These networks are usually run by private or public organisations are made to efficiently transport large numbers of people in urban and suburban areas. It usually operates on set schedules and routes and has a listed fare for each journey. Long wait periods and lengthy travel times resulting from delays are two problems that frequently plague public transportation services. There are several things that can cause a delay in public transport, such as an excess of passengers, heavy traffic, accidents, and other unforeseen circumstances. The availability of a more accurate delay prediction for public transportation might increase users’ confidence and their willingness to pay more for transit services. Over the past two decades, a number of studies on prediction algorithms for transportation data have been proposed. Most of the work is on machine learning model development, focusing on delay prediction and taking into account several factors such as weather conditions and infrastructure issues. This paper proposes a deep learning model to predict public transportation delays using data from public transportation and the weather. The results obtained from this research work are compared with several other existing works. Our experiment has demonstrated that the deep neural network (DNN) is the best model to predict transit delay compared to several other machine learning and deep learning models. |
| first_indexed | 2025-11-15T14:43:13Z |
| format | Article |
| id | upm-119055 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:43:13Z |
| publishDate | 2024 |
| publisher | Semarak Ilmu Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1190552025-08-05T04:41:33Z http://psasir.upm.edu.my/id/eprint/119055/ Public transport delay prediction using deep learning Mohd Rum, Siti Nurulain Meor Yusoff, Meor Muhammad Nazmi Mahdi, Amalia A system of trains, buses, subways, ferries, and other vehicles that are accessible to the public is referred to as public transportation, also known as public transit or mass transit. These networks are usually run by private or public organisations are made to efficiently transport large numbers of people in urban and suburban areas. It usually operates on set schedules and routes and has a listed fare for each journey. Long wait periods and lengthy travel times resulting from delays are two problems that frequently plague public transportation services. There are several things that can cause a delay in public transport, such as an excess of passengers, heavy traffic, accidents, and other unforeseen circumstances. The availability of a more accurate delay prediction for public transportation might increase users’ confidence and their willingness to pay more for transit services. Over the past two decades, a number of studies on prediction algorithms for transportation data have been proposed. Most of the work is on machine learning model development, focusing on delay prediction and taking into account several factors such as weather conditions and infrastructure issues. This paper proposes a deep learning model to predict public transportation delays using data from public transportation and the weather. The results obtained from this research work are compared with several other existing works. Our experiment has demonstrated that the deep neural network (DNN) is the best model to predict transit delay compared to several other machine learning and deep learning models. Semarak Ilmu Publishing 2024-10-07 Article PeerReviewed text en cc_by_nc_4 http://psasir.upm.edu.my/id/eprint/119055/1/119055.pdf Mohd Rum, Siti Nurulain and Meor Yusoff, Meor Muhammad Nazmi and Mahdi, Amalia (2024) Public transport delay prediction using deep learning. Journal of Advanced Research in Applied Sciences and Engineering Technology, 59 (1). pp. 1-10. ISSN 2462-1943 https://semarakilmu.com.my/journals/index.php/applied_sciences_eng_tech/article/view/9943 10.37934/araset.59.2.168177 |
| spellingShingle | Mohd Rum, Siti Nurulain Meor Yusoff, Meor Muhammad Nazmi Mahdi, Amalia Public transport delay prediction using deep learning |
| title | Public transport delay prediction using deep learning |
| title_full | Public transport delay prediction using deep learning |
| title_fullStr | Public transport delay prediction using deep learning |
| title_full_unstemmed | Public transport delay prediction using deep learning |
| title_short | Public transport delay prediction using deep learning |
| title_sort | public transport delay prediction using deep learning |
| url | http://psasir.upm.edu.my/id/eprint/119055/ http://psasir.upm.edu.my/id/eprint/119055/ http://psasir.upm.edu.my/id/eprint/119055/ http://psasir.upm.edu.my/id/eprint/119055/1/119055.pdf |