• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Conference on Artificial Intelligence and Security >Short-Term Demand Forecasting of Shared Bicycles Based on Long Short-Term Memory Neural Network Model
【24h】

Short-Term Demand Forecasting of Shared Bicycles Based on Long Short-Term Memory Neural Network Model

机译：基于长短期记忆神经网络模型的共享自行车的短期需求预测

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Shared bicycles have strong liquidity and high randomness. In order to more accurately predict the short term demand for shared bicycles, the long short-term memory (LSTM) neural network model was used as the tool to predict, on the basis of crawling the weather characteristics data of bicycles shared by Citi Bike in New York City, and analyzing the influence of time factor and meteorological factors on the demand for bicycles. On the purpose of verify our method, the traditional RNN and back propagation (BP) neural network were compared with LSTM neural network. The experimental results show that the main factors affecting the demand for shared bicycles including temperature, holidays, seasons and morning and evening peak time periods. Compared with traditional BP neural network and cyclic neural network RNN algorithm, LSTM has high robustness and strong generalization ability. The prediction result curve is consistent with the real result curve, the prediction accuracy is the highest with 0.860 and the root mean square error is the smallest with 0.090. It can be seen that the LSTM model can be used to predict the short-term demand for shared bicycles.
机译：共享的自行车具有很强的流动性和较高的随机性。为了更准确地预测共享自行车的短期需求，在抓取Citi Bike共享的自行车的天气特征数据的基础上，使用长短期记忆（LSTM）神经网络模型作为预测的工具。纽约市，并分析时间因素和气象因素对自行车需求的影响。为了验证我们的方法，将传统的RNN和反向传播（BP）神经网络与LSTM神经网络进行了比较。实验结果表明，影响共享自行车需求的主要因素包括温度，假期，季节以及早晚高峰时段。与传统的BP神经网络和循环神经网络RNN算法相比，LSTM具有较高的鲁棒性和较强的泛化能力。预测结果曲线与实际结果曲线一致，预测精度最高，为0.860，均方根误差最小，为0.090。可以看出，LSTM模型可用于预测共享自行车的短期需求。

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