Short-term prediction of on-street parking occupancy using multivariate variable based on deep learning  

作　　者：Mengqi Lyu Yanjie Ji Chenchen Kuai Shuichao Zhang 

机构地区：[1]School of Transportation,Southeast University,Nanjing 211189,China [2]Shandong Provincial Communications Planning and Design Institute Group Co.,Ltd.,Jinan 250101,China [3]School of Civil and Transportation Engineering,Ningbo University of Technology,Ningbo 315211,China

出　　处：《Journal of Traffic and Transportation Engineering(English Edition)》2024年第1期28-40,共13页交通运输工程学报（英文版）

基　　金：supported in part by the National Key Research and Development Program of China(Project No.2018YFB1600900);the Jiangsu Province Transportation Key Project of Science(Project No.2019Z01);Zhejiang Provincial Natural Science Foundation of China(No.LTGG23E080005).

摘　　要：Short-term prediction of on-street parking occupancy is essential to the ITS system,which can guide drivers in finding vacant parking spaces.And the spatial dependencies and exogenous dependencies need to be considered simultaneously,which makes short-term prediction of on-street parking occupancy challenging.Therefore,this paper proposes a deep learning model for predicting block-level parking occupancy.First,the importance of multiple points of interest(POI)in different buffers is sorted by Boruta,used for feature selection.The results show that different types of POI data should consider different buffer radii.Then based on the real on-street parking data,long short-term memory(LSTM)that can address the time dependencies is applied to predict the parking occupancy.The results demonstrate that LSTM considering POI data after Boruta selection(LSTM(+BORUTA))outperforms other baseline methods,including LSTM,with an average testing MAPE of 11.78%.The selection process of POI data helps LSTM reduce training time and slightly improve the prediction performance,which indicates that complex correlations among the same type of POI data in different buffer zones will also affect the prediction accuracy of LSTM.When there are more restaurants on both sides of the street,the prediction performance of LSTM(+BORUTA)is significantly better than that of LSTM.

关 键 词：Smart parking Parking occupancy Short-term prediction Long short-term memory Boruta 

分 类 号：U491.7[交通运输工程—交通运输规划与管理]