【摘要】 为准确预测城市不同区域的共享单车需求量,解决区域间供需不平衡问题,在对上海市共享单车数据进行时空特征可视化分析的基础上,研究共享单车的出行分布规律.针对时间出行分布的非严格周期性,提出了一种引入注意力机制的长短时记忆网络预测模型AM-LSTM.利用Spearman相关性分析法分析特征影响因素,提取模型特征值.分别构建不同输入序列的预测模型,与传统时序预测模型进行对比分析.结果表明,采用30 min时间间隔的输入序列具有较高的预测精度,AM-LSTM模型能够较好地预测共享单车的出行需求量,预测精度优于单一的LSTM模型.最后对预测曲线进行相关度分析,验证了AM-LSTM模型的预测性能,可以为城市共享单车的调度及分配提供有效信息.更多还原

【Abstract】 In order to accurately predict the demand for bike-sharing in different regions of a city and solve the problem of imbalance between supply and demand,the travel distribution law of bike-sharing in Shanghai were studied based on the visualization analysis of spatio-temporal characteristics. In view of the non-strict periodicity of time travel distribution,Attention Mechanism was introduced into the Long-short Term Memory(LSTM)network to build a demand forecasting model named AM-LSTM. Spearman correlation analysis method was used to analyze character-istic influencing factors and extract model characteristic values. The prediction models of different input sequences were constructed and compared with the traditional time series prediction models. The results showed that the input sequence with a time interval of 30 min had a higher prediction accuracy. AM-LSTM model can better predict the travel demand of bike-sharing,and the prediction accuracy was higher than that of the single LSTM model. Finally,the correlation analysis of the prediction curve was conducted to verify the prediction performance of AM-LSTM model,which can provide effective information for the scheduling and distribution of urban bike-sharing.更多还原