基于残差时域注意力神经网络的交通模式识别算法

Traffic mode recognition algorithm based on residual temporal attention neural network

交通模式识别是用户行为识别中的一个重要分支,其目的是对用户所处的交通模式进行准确判断。针对现代智慧城市交通系统对在移动设备环境下精准感知用户交通模式的需求,提出了一种基于残差时域注意力神经网络的交通模式识别算法。首先,通过具有较强局部特征提取能力的残差网络提取传感器时序中的局部特征;然后,采用基于通道的注意力机制对不同传感器特征进行重校准,并针对不同传感器的数据异构性进行注意力重校准;最后,利用具有更广感受野的时域卷积网络(TCN)提取传感器时序中的全局特征。采用数据丰富度较高的宏达通讯(HTC)交通模式识别数据集来对已有的交通模式识别算法和所提出的残差时域注意力模型进行评估,实验结果表明,所提出的残差时域注意力模型在对现代移动嵌入式设备的计算开销友好的前提下具有高达96.07%的准确率,且对单一类别均具有高于90%的召回率与精确率,验证了该模型的准确性与鲁棒性。所提模型可以作为一种支持移动智能终端运算的交通模式识别应用于智能交通出行、智慧城市等领域。

Traffic mode recognition is an important branch of user behavior recognition,the purpose of which is to identify the user’s current traffic mode.Aiming at the demand of the modern intelligent urban transportation system to accurately perceive the user’s traffic mode in the mobile device environment,a traffic mode recognition algorithm based on the residual temporal attention neural network was proposed.Firstly,the local features in the sensor time sequence were extracted through the residual network with strong local feature extraction ability.Then,the channel-based attention mechanism was used to recalibrate the different sensor features,and the attention recalibration was performed by focusing on the data heterogeneity of different sensors.Finally,the Temporal Convolutional Network(TCN)with a wider receptive field was used to extract the global features in the sensor time sequence.The data-rich High Technology Computer(HTC)traffic mode recognition dataset was used to evaluate the existing traffic mode recognition algorithms and the residual temporal attention model.Experimental results show that the proposed residual temporal attention model has the accuracy as high as 96.07%with friendly computational overhead for mobile devices,and has the precision and recall for any single class reached or exceeded 90%,which verify the accuracy and robustness of the proposed model.The proposed model can be applied to intelligent transportation,smart city and other domains as a kind of traffic mode detection for supporting mobile intelligent terminal operation.