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首页> 外文期刊>Journal of advanced transportation >Highway Travel Time Prediction of Segments Based on ANPR Data considering Traffic Diversion
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Highway Travel Time Prediction of Segments Based on ANPR Data considering Traffic Diversion

机译：基于考虑交通转移的ANPR数据的段的公路旅行时间预测

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Travel time is one of the most critical parameters in proactive traffic management and the deployment of advanced traveler information systems. This paper proposes a hybrid model named LSTM-CNN for predicting the travel time of highways by integrating the long short-term memory (LSTM) and the convolutional neural networks (CNNs) with the attention mechanism and the residual network. The highway is divided into multiple segments by considering the traffic diversion and the relative location of automatic number plate recognition (ANPR). There are four steps in this hybrid approach. First, the average travel time of each segment in each interval is calculated from ANPR and fed into LSTM in the form of a multidimensional array. Second, the attention mechanism is adopted to combine the hidden layer of LSTM with dynamic temporal weights. Third, the residual network is introduced to increase the network depth and overcome the vanishing gradient problem, which consists of three pairs of one-dimensional convolutional layers (Conv1D) and batch normalization (BatchNorm) with the rectified linear unit (ReLU) as the activation function. Finally, a series of Conv1D layers is connected to extract features further and reduce dimensionality. The proposed LSTM-CNN approach is tested on the three-month ANPR data of a real-world 39.25?km highway with four pairs of ANPR detectors of the uplink and downlink, Zhejiang, China. The experimental results indicate that LSTM-CNN learns spatial, temporal, and depth information better than the state-of-the-art traffic forecasting models, so LSTM-CNN can predict more accurate travel time. Moreover, LSTM-CNN outperforms the state-of-the-art methods in nonrecurrent prediction, multistep-ahead prediction, and long-term prediction. LSTM-CNN is a promising model with scalability and portability for highway traffic prediction and can be further extended to improve the performance of the advanced traffic management system (ATMS) and advanced traffic information system (ATIS).
机译：旅行时间是主动流量管理中最关键的参数之一和高级旅行者信息系统的部署。本文提出了一种名为LSTM-CNN的混合模型，用于通过将长短期存储器（LSTM）和卷积神经网络（CNNS）与注意机制和剩余网络集成来预测高速公路的行驶时间。通过考虑交通转移和自动数字板识别（ANPR）的相对位置，高速公路分为多个段。这种混合方法有四个步骤。首先，从ANPR计算每个间隔中的每个段的平均旅行时间，并以多维阵列的形式进入LSTM。其次，采用注意机制将LSTM隐藏层与动态时间重量结合起来。第三，介绍了剩余网络以增加网络深度并克服消失的梯度问题，该问题由三对一维卷积层（CONV1D）和批量归一化（Batchnorm）与整流的线性单元（Relu）为激活而组成功能。最后，一系列CONC1D层连接以进一步提取特征并减少维度。拟议的LSTM-CNN方法是在真实世界39.25的三个月ANPR数据上进行测试，用四对ANPR探测器的浙江，浙江，中国。实验结果表明，LSTM-CNN比最先进的业务预测模型更好地学习空间，时间和深度信息，因此LSTM-CNN可以预测更准确的行驶时间。此外，LSTM-CNN在非逆流预测，多次预测预测和长期预测中优于最先进的方法。 LSTM-CNN是一个有前途的模型，具有公路交通预测的可扩展性和可移植性，可以进一步扩展，以提高高级交通管理系统（ATM）和高级交通信息系统（ATIS）的性能。

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