基于LightGBM-SVR-LSTM的停车区车位预测

Prediction of Empty Parking Space in Parking Area Based on LightGBM-SVR-LSTM

停车难和交通拥堵现象愈演愈烈,提前告知驾驶员未来一段时间空车位数量,可以减少其寻找有效车位的时间,进而够缓解拥堵情况。基于此,提出了一种基于LightGBM-SVR-LSTM的停车区剩余车位预测模型。首先,通过数据预处理,尽可能保留原始数据特征的基础上,修复部分噪声数据;其次,将修复的数据放入轻量级梯度提升机(light gradient boosting machine, LightGBM),提取叶子节点的值作为新的特征,并将其放入支持向量回归模型(support vector regression, SVR)进行预测;然后,利用长短时记忆神经网络(long short-term memory neural network, LSTM)进行误差修复。最后,选取某停车区数据,利用均方根误差(root mean square error, RMSE)、平均绝对误差(mean absolute error, MAE)、平均百分比误差(mean absolute percentage error, MAPE)进行预测效果验证。结果表明:在正常条件和节假日期间,所提出的组合模型精度均有提升,具有一定的鲁棒性。

Parking difficulties and congestion are becoming more and more serious.Informing drivers in advance of the number of empty parking spaces in the future can reduce their time to find effective parking spaces,so as to alleviate the congestion.Based on this phenomenon,an empty parking space prediction model based on LightGBM-SVR-LSTM was proposed.Firstly,through data processing,some noise data were repaired on the basis of preserving the characteristics of the original data as much as possible.Secondly,the repaired data were put into the light gradient boosting machine(LightGBM),the value of leaf node was extracted as new features,and was put into the support vector regression(SVR)for prediction.Then,the long short-term memory neural network(LSTM)was used to repair the error.Finally,the data of a parking area were selected to verify the prediction effect by using root mean square error(RMSE),mean absolute error(MAE)and mean absolute percentage error(MAPE).The results show that the accuracy of the proposed combined model is improved and has robustness under normal conditions and holidays.