The crowd flows prediction plays an important role in urban planning management and urban public safety. Accuracy is a challenge for predicting the flow of crowds in a region. On the one hand, crowd flow is influenced by many factors such as holidays and weather. On the other hand, sample data about crowd flows are generally high-dimensional, which not only has a negative impact on the prediction accuracy but also increases computational complexity. In this paper, an efficiency-enhanced model is constructed for predicting citywide crowd flows based on multi-source data using deep learning techniques. Specifically, a data reconstruction mechanism is built with Bernoulli restricted Boltzmann machine (BRBM), for the purpose of reducing the dimension of sample data. A collaborative prediction mechanism is introduced to improve the prediction accuracy of crowd flows, in which a spatio-temporal data oriented prediction model is constructed based on bottleneck residual network that can reduce the effectively computational complexity of model training, and an auxiliary prediction to further optimize the prediction accuracy based on the fully-connected network. The proposed method is evaluated by using two open datasets. The experimental results show that our method can significantly improve the prediction accuracy and reduce the training time of the prediction model, compared with other methods.

人群流量预测在城市规划管理和城市公共安全中发挥着重要作用。准确度对于预测区域中的人群流量是一项挑战。一方面，人流受到假期和天气等许多因素的影响。另一方面，关于人群流的样本数据通常是高维的，这不仅对预测准确性有负面影响，而且还会增加计算复杂度。在本文中，使用深度学习技术，基于多源数据，构建了一个用于预测城市范围内人群流量的效率增强模型。具体地，为了减小样本数据的尺寸，利用伯努利限制的玻尔兹曼机（BRBM）建立了数据重建机制。引入了一种协同预测机制来提高人群流的预测精度，其中基于瓶颈残差网络构建时空数据导向的预测模型，可以有效降低模型训练的计算复杂度，而辅助预测则可以进一步优化基于全连接网络的预测准确性。通过使用两个开放数据集对提出的方法进行了评估。实验结果表明，与其他方法相比，该方法可以显着提高预测精度，减少预测模型的训练时间。辅助预测，以基于全连接网络进一步优化预测精度。通过使用两个开放数据集对提出的方法进行了评估。实验结果表明，与其他方法相比，该方法可以显着提高预测精度，减少预测模型的训练时间。辅助预测，以基于全连接网络进一步优化预测精度。通过使用两个开放数据集对提出的方法进行了评估。实验结果表明，与其他方法相比，该方法可以显着提高预测精度，减少预测模型的训练时间。