In this study, a novel prediction model for the number of vacant parking spaces after a specific period of time is proposed based on support vector regression (SVR) with fruit fly optimisation algorithm (FOA). In the proposed model, the SVR parameters are initialised as the fruit fly population, and FOA is utilised to search the optimal parameters for SVR. Sufficient experiments within various scenarios, i.e. predicting the vacant parking space availability in parking lots with various capacities after various periods of time, have been conducted to verify the effectiveness of the proposed FOA-SVR prediction model. Three other commonly used prediction models, i.e. backpropagation neural network (NN), extreme learning machine and wavelet NN, are used as the comparison models. The experimental results show that the proposed FOA-SVR method has higher accuracy and stability in all the prediction scenarios.
Predicting vacant parking space availability: an SVR method with fruit fly optimisation
IET Intelligent Transport Systems ; 12 , 10 ; 1414-1420
2018-10-12
7 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
backpropagation , FOA-SVR method , SVR parameters , parking lots , prediction scenarios , FOA-SVR prediction model , learning (artificial intelligence) , support vector machines , fruit fly population , vacant parking space availability , neural nets , vacant parking spaces , commonly used prediction models , support vector regression , optimal parameters , optimisation , extreme learning machine , fruit fly optimisation , novel prediction model , regression analysis
Metadata by IET is licensed under CC BY 3.0
Predicting vacant parking space availability: an SVR method with fruit fly optimisation
Wiley | 2018
|Vacant parking space detection apparatus and vacant parking space detection method
Europäisches Patentamt | 2020
|VACANT PARKING SPACE DETECTION APPARATUS AND VACANT PARKING SPACE DETECTION METHOD
Europäisches Patentamt | 2020
|VACANT PARKING SPACE FINDING DEVICE AND VACANT PARKING SPACE FINDING METHOD
Europäisches Patentamt | 2020
|