TY - JOUR
T1 - Notice of Removal
T2 - Free Parking Space Prediction and Reliability Analysis Based on Big Data Analysis
AU - Zhang, Chao
AU - Wang, Xianwei
AU - Ma, Zhongjing
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Parking guidance
KW - big data analysis
KW - free parking spaces (FPSs)
KW - prediction model
UR - https://www.scopus.com/pages/publications/85084114297
U2 - 10.1109/ACCESS.2020.2986056
DO - 10.1109/ACCESS.2020.2986056
M3 - Article
AN - SCOPUS:85084114297
SN - 2169-3536
VL - 8
SP - 66609
EP - 66614
JO - IEEE Access
JF - IEEE Access
M1 - 9057694
ER -