AI-driven local parking availability prediction
This report addresses the challenge of finding available parking lots in Singapore. The data from the Urban Redevelopment Authority (URA) indicates that car park ratios (parking spaces per 1,000 residents) in the CBD are significantly lower compared to suburban areas. This data underscores the inher...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/175519 |
| _version_ | 1811689518802141184 |
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| author | Ma Yiheng |
| author2 | Kong Wai-Kin, Adams |
| author_facet | Kong Wai-Kin, Adams Ma Yiheng |
| author_sort | Ma Yiheng |
| collection | NTU |
| description | This report addresses the challenge of finding available parking lots in Singapore. The data from the Urban Redevelopment Authority (URA) indicates that car park ratios (parking spaces per 1,000 residents) in the CBD are significantly lower compared to suburban areas. This data underscores the inherent limitations of parking infrastructure in the CBD and its contribution to lower availability rates. [10] The parking lot occupancy prediction offers a solution by leveraging existing online parking information. These services can empower drivers to make informed decisions by providing them with key details during their search for the optimal parking spot. [11]
This project aims to develop service that forecasting parking availability of car parks in Singapore. This service can potentially alleviate the struggles associated with finding a suitable parking space. A Singapore map with parking spots is shown to assist drivers figure out which parking place is still with available lots currently and in 30 minutes near future. As a system based on the machine learning model, comparing with one based on live real-time data, it allows users to know in advance the likelihood of finding a parking space, thus better planning their trips and reducing anxiety associated with uncertainty.
In Singapore, where parking occupancy rates can vary significantly between many factors, such as the day and night, peak hours and off-peak hours. [12] The necessity for such predictions in Singapore arises from its high vehicle density and limited parking infrastructure, making real-time and predictive information crucial for efficient urban mobility. A trained model can function independently of live updates, which can be particularly advantageous in areas with poor connectivity or limited sensor coverage. [13]
Moreover, the improved prediction accuracy can further benefit drivers by reducing fuel consumption and emissions as they spend less time searching for parking, contributing to a more sustainable urban environment. |
| first_indexed | 2024-10-01T05:49:23Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/175519 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:49:23Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1755192024-04-26T15:45:30Z AI-driven local parking availability prediction Ma Yiheng Kong Wai-Kin, Adams School of Computer Science and Engineering AdamsKong@ntu.edu.sg Computer and Information Science Artificial intelligence This report addresses the challenge of finding available parking lots in Singapore. The data from the Urban Redevelopment Authority (URA) indicates that car park ratios (parking spaces per 1,000 residents) in the CBD are significantly lower compared to suburban areas. This data underscores the inherent limitations of parking infrastructure in the CBD and its contribution to lower availability rates. [10] The parking lot occupancy prediction offers a solution by leveraging existing online parking information. These services can empower drivers to make informed decisions by providing them with key details during their search for the optimal parking spot. [11] This project aims to develop service that forecasting parking availability of car parks in Singapore. This service can potentially alleviate the struggles associated with finding a suitable parking space. A Singapore map with parking spots is shown to assist drivers figure out which parking place is still with available lots currently and in 30 minutes near future. As a system based on the machine learning model, comparing with one based on live real-time data, it allows users to know in advance the likelihood of finding a parking space, thus better planning their trips and reducing anxiety associated with uncertainty. In Singapore, where parking occupancy rates can vary significantly between many factors, such as the day and night, peak hours and off-peak hours. [12] The necessity for such predictions in Singapore arises from its high vehicle density and limited parking infrastructure, making real-time and predictive information crucial for efficient urban mobility. A trained model can function independently of live updates, which can be particularly advantageous in areas with poor connectivity or limited sensor coverage. [13] Moreover, the improved prediction accuracy can further benefit drivers by reducing fuel consumption and emissions as they spend less time searching for parking, contributing to a more sustainable urban environment. Bachelor's degree 2024-04-26T01:58:37Z 2024-04-26T01:58:37Z 2024 Final Year Project (FYP) Ma Yiheng (2024). AI-driven local parking availability prediction. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/175519 https://hdl.handle.net/10356/175519 en application/pdf Nanyang Technological University |
| spellingShingle | Computer and Information Science Artificial intelligence Ma Yiheng AI-driven local parking availability prediction |
| title | AI-driven local parking availability prediction |
| title_full | AI-driven local parking availability prediction |
| title_fullStr | AI-driven local parking availability prediction |
| title_full_unstemmed | AI-driven local parking availability prediction |
| title_short | AI-driven local parking availability prediction |
| title_sort | ai driven local parking availability prediction |
| topic | Computer and Information Science Artificial intelligence |
| url | https://hdl.handle.net/10356/175519 |
| work_keys_str_mv | AT mayiheng aidrivenlocalparkingavailabilityprediction |