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

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

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  • NSTL
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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 per-formance of LSTM(+BORUTA)is significantly better than that of LSTM.

Smart parkingParking occupancyShort-term predictionLong short-term memoryBoruta

Mengqi Lyu、Yanjie Ji、Chenchen Kuai、Shuichao Zhang

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School of Transportation,Southeast University,Nanjing 211189,China

Shandong Provincial Communications Planning and Design Institute Group Co.,Ltd.,Jinan 250101,China

School of Civil and Transportation Engineering,Ningbo University of Technology,Ningbo 315211,China

National Key Research and Development Program of ChinaJiangsu Province Transportation Key Project of ScienceZhejiang Provincial Natural Science Foundation of China

Project 2018YFB1600900Project 2019Z01LTGG23E080005

2024

10.1016/j.jtte.2022.05.004

交通运输工程学报(英文版)

交通运输工程学报(英文版)

CSTPCD
ISSN：2095-7564
年,卷(期)：2024.11(1)
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