Open AccessPredicting vacant parking space availability: an SVR method with fruit fly optimisation
Author(s) -
Fan Junkai,
Hu Qian,
Tang Zhenzhou
Publication year - 2018
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2018.5031
Subject(s) - support vector machine , backpropagation , artificial neural network , parking space , population , machine learning , stability (learning theory) , computer science , predictive modelling , artificial intelligence , data mining , engineering , transport engineering , demography , sociology
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.