基于Transformer的街道停车位数据补全和预测

doi:10.11896/jsjkx.221200171

Abstract

Abstract: With the continuous growth of the number of cars in cities,the difficulty of parking on the street has become a hot issue.The key to solve the street parking problem is to accurately predict the future parking space information of the street.CrowdSensing is a low-cost and cost-effective way of sensing parking space by installing sonar on vehicles.However,the parking space data sensed in this way has high sparsity in time,and the traditional model cannot be directly used for prediction.To solve this problem,a transformer-based parking space sequence completion and prediction network is proposed.This network generates the memory of the missing parking space sequence through the encoder,and then the decoder completes the missing part of the parking space sequence in the way of auto-regression,and predicts the future parking space information.Experimental results show that the proposed method is better than a series of traditional machine learning and deep learning methods in the completion and prediction of two highly missing street parking space data sets.

Key words: Street parking space, Data completion, Time series prediction, Machine learning, Deep learning

CLC Number:

TP391

LIN Binwei, YU Zhiyong, HUANG Fangwan, GUO Xianwei. Data Completion and Prediction of Street Parking Spaces Based on Transformer[J].Computer Science, 2024, 51(4): 165-173.

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Data Completion and Prediction of Street Parking Spaces Based on Transformer

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