...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Transactions on Intelligent Transportation Systems >Taxi-Based Mobility Demand Formulation and Prediction Using Conditional Generative Adversarial Network-Driven Learning Approaches
【24h】

Taxi-Based Mobility Demand Formulation and Prediction Using Conditional Generative Adversarial Network-Driven Learning Approaches

机译：使用条件生成对抗网络驱动学习方法的基于出租车的移动需求制定和预测

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In this paper, a deep learning (DL) framework was proposed to predict the taxi-passenger demand while the spatial, the temporal, and external dependencies were considered simultaneously. The proposed DL framework combined a modified density-based spatial clustering algorithm with noise (DBSCAN) and a conditional generative adversarial network (CGAN) model. More specifically, the modified DBSCAN model was applied to produce a number of sub-networks considering the spatial correlation of taxi pick-up events in the road network. And the CGAN model, fed with the historical taxi passenger demand and other conditional information, was capable to predict the taxi-passenger demands. The proposed CGAN model was made up with two long short-term memory (LSTM) neural networks, which are termed as the generative network G and the discriminative network D, respectively. Adversarial training process was conducted to the two LSTMs. In the numerical experiment, different model layouts were compared. It was found that different network layouts provided reasonable accuracy. With limited training data, more LSTM layers in the generator network resulted in not only higher accuracy, but also more difficulties in training. Comparisons were also conducted between the proposed prediction model and four typical approaches, including the moving average method, the autoregressive integrated moving method, the neural network model, and the LSTM neural network model. The comparison results showed that the proposed model outperformed all the other methods. And the repeated experiment indicated that the proposed CGAN model provided significant better predictions than the LSTM model did. Future research was recommended to include more datasets for testing the model and more information for improving predictive performance.
机译：在本文中，提出了一种深度学习（DL）框架来预测出租车乘客的需求，同时同时考虑了空间，时间和外部依赖性。所提出的DL框架将改进的基于噪声的基于密度的空间聚类算法（DBSCAN）与条件生成对抗网络（CGAN）模型相结合。更具体地说，考虑到道路网络中出租车接送事件的空间相关性，将修改后的DBSCAN模型应用于产生多个子网。 CGAN模型结合了出租车的历史乘客需求和其他条件信息，能够预测出租车乘客的需求。所提出的CGAN模型由两个长短期记忆（LSTM）神经网络组成，分别称为生成网络G和判别网络D。对两个LSTM进行了对抗训练。在数值实验中，比较了不同的模型布局。发现不同的网络布局可提供合理的准确性。如果训练数据有限，则生成器网络中更多的LSTM层不仅会导致更高的准确性，而且会导致训练上的更多困难。还比较了所提出的预测模型和四种典型方法，包括移动平均法，自回归综合移动法，神经网络模型和LSTM神经网络模型。比较结果表明，所提模型优于其他所有方法。重复实验表明，所提出的CGAN模型比LSTM模型提供了更好的预测。建议未来的研究包括更多的数据集以测试模型，以及更多的信息以提高预测性能。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号