Intelligent transportation system (ITS) is an advance leading edge technology that aims to deliver innovative services to different modes of transport and traffic management. Traffic flow prediction (TFP) is one of the key macroscopic parameters of traffic that supports traffic management in ITS. Growth of the real-time data in transportation from various modern equipments, technology, and other resources has led to generate big data, posing a huge concern to deal with. Recently, deep learning (DL) techniques have demonstrated the capability to extract comprehensive features efficiently, using multiple hidden layers, from such huge raw, unstructured, and nonlinear data. Nonlinearity in traffic data is the major cause of inaccuracy in TFP. In this article, we propose a flow strength indicator-based Chronological Dolphin Echolocation-Fuzzy, a bioinspired optimization method with fuzzy logic for incremental learning of deep belief network. Technical indicators provide flow strength features as an input to the model. Hidden layers of DL architecture consequently learn more features and propagate it as an input to next layer for supervised learning. The degree of membership to the features is identified by the membership functions, followed by weight optimization using Dolphin Echolocation algorithm to fit the model for the nonlinear data. Experiments performed on two different data sets, namely Traffic-major roads and performance measurement system-San Francisco (PEMS-SF), show good results for the proposed deep architecture. The analysis of the proposed method using log mean square error and log root mean square deviation acquires a minimum value of 2.4141 and 0.61 for the Traffic-major roads database taken for the time step duration of 1 year and a minimum value of 1.6691 and 0.5208 for PEMS-SF data set for the time step interval of 5 minutes, respectively. These positive results demonstrate key importance of our traffic flow model for the transportation system.

中文翻译：

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使用流量强度指标进行智能交通系统交通流量预测的模糊启发式深信度网络。

智能交通系统 (ITS) 是一项先进的前沿技术，旨在为不同的交通方式和交通管理提供创新服务。交通流预测（TFP）是支持 ITS 中交通管理的交通的关键宏观参数之一。来自各种现代设备、技术和其他资源的交通运输实时数据的增长导致了大数据的产生，这引起了人们极大的关注。最近，深度学习 (DL) 技术已经展示了使用多个隐藏层从如此庞大的原始、非结构化和非线性数据中有效提取综合特征的能力。交通数据的非线性是 TFP 不准确的主要原因。在本文中，我们提出了一种基于流动强度指标的 Chronological Dolphin Echolocation-Fuzzy，一种具有模糊逻辑的仿生优化方法，用于深度信念网络的增量学习。技术指标提供流动强度特征作为模型的输入。因此，DL 架构的隐藏层学习更多特征并将其作为输入传播到下一层进行监督学习。特征的隶属度由隶属函数确定，然后使用 Dolphin Echolocation 算法进行权重优化以拟合非线性数据的模型。在两个不同的数据集上进行的实验，即交通主要道路和性能测量系统 - 旧金山 (PEMS-SF)，显示了所提出的深度架构的良好结果。使用对数均方误差和对数均方根偏差对所提出方法的分析获得最小值 2.4141 和 0。对于 1 年时间步长的主要交通道路数据库，其最小值为 61，PEMS-SF 数据集的最小值分别为 1.6691 和 0.5208，时间步长间隔为 5 分钟。这些积极的结果证明了我们的交通流模型对交通系统的关键重要性。