Notice of Violation of IEEE Publication Principles: Free Parking Space Prediction and Reliability Analysis Based on Big Data Analysis
The number of free parking spaces (FPSs) is highly dynamic and stochastic, calling for a parking guidance model that enables the driver to find the right parking lot. This paper explores the distribution of FPSs number in parking lots, predicts the number of FPSs, and proposes a parking guidance mod...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
|
| Series: | IEEE Access |
| Online Access: | https://ieeexplore.ieee.org/document/9057694/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | The number of free parking spaces (FPSs) is highly dynamic and stochastic, calling for a parking guidance model that enables the driver to find the right parking lot. This paper explores the distribution of FPSs number in parking lots, predicts the number of FPSs, and proposes a parking guidance model with a solving algorithm. Firstly, the number of FPSs from several parking lots was subjected to big data analysis, revealing that the hourly number of FPSs obey similar trends on different weekdays. On this basis, the data on the FPSs number of the parking lots were classified by hourly, weekly and holiday features. Whereas the FPSs number obeys the normal distribution, a parking guidance model was established with the most reliable path to forecast the number of FPSs. Then, the solving algorithm was proposed based on the reliability boundary. Finally, the effectiveness of the model and algorithm was verified through simulation. Compared with the actual data, the prediction accuracy of the model is more than 95%. The research results shed new light on the development of parking guidance systems. |
|---|---|
| ISSN: | 2169-3536 |