基于Transformer的街道停车位数据补全和预测

Data Completion and Prediction of Street Parking Spaces Based on Transformer

随着城市汽车数量的持续增长,街道停车难已经成为一个热点问题。解决街道停车问题的关键在于准确预测街道未来的停车位信息。移动群智感知方式(CrowdSensing)通过在车辆上安装声呐以感知路边的停车位情况,是一种低成本、高效益的感知停车位的方式,然而这种方式感知的停车位数据在时间上存在高稀疏性问题,传统模型无法直接用于预测。针对此问题,提出了一种基于Transformer的停车位序列补全和预测网络,此网络通过编码器生成缺失停车位序列的记忆,进而解码器以自回归的方式补全停车位序列中缺失的部分,同时预测出未来的停车位信息。实验结果表明,所提方法在两个高缺失的街道停车位数据集上的补全和预测效果都优于传统的机器学习和深度学习方法。

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.