Parking lot availability prediction and patterns
In Singapore, some car parks’ availability information is available to Land Transport Authority of Singapore (LTA). Digital notice boards are used on the road to display the current availability of some popular car parks. Some iOS apps also provide such function. However there are high chances that...
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sg-ntu-dr.10356-628832023-03-03T20:40:08Z Parking lot availability prediction and patterns Ning, Haoyan Ho Shen-Shyang School of Computer Engineering DRNTU::Engineering::Computer science and engineering::Computing methodologies::Pattern recognition In Singapore, some car parks’ availability information is available to Land Transport Authority of Singapore (LTA). Digital notice boards are used on the road to display the current availability of some popular car parks. Some iOS apps also provide such function. However there are high chances that the current availability is greater than zero and car park is full when the driver arrives especially during peak hours. In order to resolve the existing issues, a car park availability prediction model has to be developed which predicts the availability of the car park based on driver’s arrival time. Multiple linear regression is used as the prediction model. The report analyzes the features used for prediction and discusses the performance of the predictions. Another objective of the project is to develop an iOS app (iSPARK) to enhance the driving experience of the drivers. The iOS platform is selected because of the increasing downloads of iOS apps from Apple store. The app allows car parks search by current location or specified destination. It is also used to display the predicted availability for the resulting car parks. The app is an interface where it can be used by other related projects to perform data collection or to display the results. Bachelor of Engineering (Computer Science) 2015-04-30T06:52:03Z 2015-04-30T06:52:03Z 2015 2015 Final Year Project (FYP) http://hdl.handle.net/10356/62883 en Nanyang Technological University 45 p. application/pdf |
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DRNTU::Engineering::Computer science and engineering::Computing methodologies::Pattern recognition Ning, Haoyan Parking lot availability prediction and patterns |
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In Singapore, some car parks’ availability information is available to Land Transport Authority of Singapore (LTA). Digital notice boards are used on the road to display the current availability of some popular car parks. Some iOS apps also provide such function. However there are high chances that the current availability is greater than zero and car park is full when the driver arrives especially during peak hours. In order to resolve the existing issues, a car park availability prediction model has to be developed which predicts the availability of the car park based on driver’s arrival time. Multiple linear regression is used as the prediction model. The report analyzes the features used for prediction and discusses the performance of the predictions. Another objective of the project is to develop an iOS app (iSPARK) to enhance the driving experience of the drivers. The iOS platform is selected because of the increasing downloads of iOS apps from Apple store. The app allows car parks search by current location or specified destination. It is also used to display the predicted availability for the resulting car parks. The app is an interface where it can be used by other related projects to perform data collection or to display the results. |
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Ho Shen-Shyang |
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Ho Shen-Shyang Ning, Haoyan |
format |
Final Year Project |
author |
Ning, Haoyan |
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Ning, Haoyan |
title |
Parking lot availability prediction and patterns |
title_short |
Parking lot availability prediction and patterns |
title_full |
Parking lot availability prediction and patterns |
title_fullStr |
Parking lot availability prediction and patterns |
title_full_unstemmed |
Parking lot availability prediction and patterns |
title_sort |
parking lot availability prediction and patterns |
publishDate |
2015 |
url |
http://hdl.handle.net/10356/62883 |
_version_ |
1759855169453948928 |