| 基于集成学习的城市空间停车难度预测 |
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| 引用本文：谭文安,刘新乐.基于集成学习的城市空间停车难度预测[J].上海第二工业大学(中文版),2019,(1):53-60 |
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| 基金项目:国家自然科学基金(61672022, 61272036), 上海第二工业大学研究生项目基金(EGD17YJ0034), 上海第二工业大学重
点学科(XXKZD1604) 资助 |
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| 中文摘要:停车位预测技术是解决城市停车难问题的一种可行方案。针对神经网络等预测模型难以应对诸如路边占道
停车等复杂情况, 提出了一个基于支持向量机和决策树集成的模型训练方法, 不再着重预测停车位的个数, 而是预
测某一位置的停车难度。在每轮训练过程中拟合一个支持向量机模型, 同时收集预测出错的样本, 最后在误分类样
本集合上训练决策树模型来提高整个模型的预测准确性。采用该方法训练了一个城市空间停车难度预测模型, 并利
用该模型预测了近一周时间的停车难度。实验结果显示, 该方法的预测效果优于单独使用支持向量机、决策树和全
连接神经网络模型, 可以较好地捕捉到停车难度随时间变化的基本情况。 |
| 中文关键词:停车预测 集成学习 支持向量机 决策树 |
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| Urban Space Parking Difficulties Prediction Based on Ensemble Learning |
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| Abstract:Parking space prediction technology is a feasible solution to solve the problem of urban parking difficulties. In view of the
difficulty of predictive models such as neural networks in dealing with complex situations like roadside parking, an integrated model
training method based on support vector machine and decision tree is proposed, which no longer focuses on predicting the number of
parking spaces, but on predicting the parking difficulty of a certain location. In each training cycle, a support vector machine model is
fitted, and the prediction error samples are collected. Finally, the decision tree model is trained on the set of misclassification samples
to improve the prediction accuracy of the whole model. This method is used to train a prediction model of parking difficulty in urban
space, and the model is used to predict parking difficulty in nearly a week. The experimental results show that the prediction effect of
this method is better than that of using support vector machine, decision tree and fully connected neural network model alone, and can
capture the basic situation of parking difficulty changing with time. |
| keywords:parking prediction ensemble learning support vector machine decision tree |
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