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Massive amounts of real-time streaming network data are generated quickly because of the exponential growth of applications. Analyzing patterns in generated flow traffic streaming offers benefits in reducing traffic congestion, enhancing network management, and improving the quality of service management. Processing massive volumes of generated traffic poses more challenges when data traffic encryption is raised. Classifying encrypted network traffic in real-time with deep learning networks has received attention because of their excellent performance. The substantial volume of incoming packets, characterized by high speed and wide variety, puts real-time traffic classification within the domain of big data problems. Classifying traffic with high speed and accuracy is a significant challenge in the era of big data. The real-time nature of traffic intensifies deep learning networks, necessitating a considerable number of parameters, layers, and resources for optimal network training. Until now, various datasets have been employed to evaluate the effectiveness of previous methods for classifying encrypted traffic. The primary objective has been to enhance accuracy, precision, and F1-measure. Presently, encrypted traffic classification performance depends on pre-existing datasets. The learning and testing phases are done offline, and more research is needed to investigate the feasibility of these methods in real-world scenarios. This paper examines the possibility of a tradeoff between evaluating the model's effectiveness, execution time, and utilization of processing resources when processing stream-based input data for traffic classification. We aim to explore the feasibility of establishing a tradeoff between these factors and determining optimal parameter settings. This paper used the ISCX VPN-Non VPN 2016 public dataset to evaluate the proposed method. All packets from the dataset were streamed continuously through Apache Kafka to the classification framework. Numerous experiments have been designed to demonstrate the efficacy of the proposed method. The experimental results show that the proposed method outperforms the baseline methods by 11% in the F1-measure when the number of workers is two and by 25% when the number of workers is equal to 32. [ABSTRACT FROM AUTHOR]

The exponential growth of vehicles has led to increased traffic congestion and pollutants emission in cities. Intelligent transportation systems (ITS) shall be built by integrating Artificial Intelligence(AI). Planning optimal route with minimum travel time is a great challenge for the traffic management system. A hybrid model integrating long-short-term memory(LSTM) deep-learning model and Dijkstra's algorithm for recommending the fastest route is proposed in this work. The proposed hybrid approach reduces the travel time to a greater extent. Vehicle speed greatly influences eco-driving factors such as fuel consumption and exhaust emission. Thus, for each road links, the fuel consumption and emission of pollutants such as carbon monoxide(CO), nitrogen oxides(NOx), and hydrocarbon(HC) are estimated for different type of vehicles. The results showed the proposed approach decreases fuel consumption, CO emission, NOx emission, and HC emission up to 54.5%, 61.5%, 64.2%, and 81.9%, respectively, compared to the traditional shortest path. [ABSTRACT FROM AUTHOR]

The key to solving traffic congestion is the accurate traffic speed forecasting. However, this is difficult owing to the intricate spatial-temporal correlation of traffic networks. Most existing studies either ignore the correlations among distant sensors, or ignore the time-varying spatial features, resulting in the inability to extract accurate and reliable spatial-temporal features. To overcome these shortcomings, this study proposes a new deep learning framework named spatial-temporal gated graph convolutional network for long-term traffic speed forecasting. Firstly, a new spatial graph generation method is proposed, which uses the adjacency matrix to generate a global spatial graph with more comprehensive spatial features. Then, a new spatial-temporal gated recurrent unit is proposed to extract the comprehensive spatial-temporal features from traffic data by embedding a new graph convolution operation into gated recurrent unit. Finally, a new self-attention block is proposed to extract global features from the traffic data. The evaluation on two real-world traffic speed datasets demonstrates the proposed model can accurately forecast the long-term traffic speed, and outperforms the baseline models in most evaluation metrics. [ABSTRACT FROM AUTHOR]

• High resolution traffic speed temporospatial distribution and propagation dynamics prediction • Deep learning framework integrating traffic flow dynamics and topological dependency • Dynamic programming capturing traffic flow interdependency The information of network-wide future traffic speed distribution and its propagation is beneficial to develop proactive traffic congestion management strategies. However, predicting network-wide traffic speed propagation is non-trivial. This study develops a t raffic flow d ependency and d ynamics based deep learning aided approach (TD2-DL), which predict network-wide high resolution traffic speed propagation by explicitly integrating temporal-spatial flow dependency, traffic flow dynamics with deep learning method techniques. Specifically, we first develop a graph theory-based method to identify the local temporal-spatial traffic dependency of each road among neighboring roads adaptive to the prediction horizon and traffic delay. Then, traffic speed propagation on every road is mathematically described by v -CTM based on traffic initial and boundary conditions. Next, the long short-term memory (LSTM) model is employed to predict boundary conditions factoring the traffic temporal-spatial dependency and historical data predicted by v -CTM. In this way, we well couple the physical models (traffic dependency and v -CTM) with the deep learning approach, and further make them coevolution under this framework. Last, an EKF is used to assimilate predicted traffic speed predicted by v -CTM coupled with the LSTMs and the field traffic data; an FNN is introduced to impute missing and corrupted data for improving the traffic speed prediction accuracy. The numerical experiments indicated that the TD2-DL predicted the network-wide traffic speed propagation in 30 minutes with accuracy varying from 85%-98%. It outperformed the tested models recently developed in literature. The ablation experimental results confirmed the significance of factoring traffic dependency and integrating data imputation and assimilation techniques for improving the prediction accuracy. [ABSTRACT FROM AUTHOR]

Understanding the causal impact of meteorological variations on traffic conditions (e.g., traffic flow and speed) is crucial for effective traffic prediction and management, as well as the mitigation of adverse weather effects on traffic. However, many existing studies focused on establishing associations between meteorological situations and traffic, rather than delving into causal relationships, especially with deep learning techniques. Consequently, the ability to identify specific meteorological conditions that significantly contribute to traffic congestion or delays is still limited. To address this issue, this study proposes the M eteorological- T raffic C ausal I nference V ariational A uto- E ncoder Model (MT-CIVAE) to estimate the causal impact of fine-grained meteorological variations (e.g., rain and temperature) on traffic. Specifically, MT-CIVAE is based on the Variational Auto-Encoder and consists of an encoder to recover the distribution of latent confounders and a decoder to estimate the conditional probabilities of treatments. Transformer encoder layers are incorporated to analyze the spatial and temporal correlations of historical traffic data to further enhance the inference capability. To evaluate the effectiveness of the proposed approach for causal inference, real-world traffic flow and speed datasets collected from California, along with corresponding fine-grained meteorological datasets, are employed. The counterfactual analysis is conducted using artificially generated meteorological conditions as treatments, which allows for the simulation of hypothetical meteorological scenarios and the evaluation of their potential impact on traffic conditions. This study develops deep learning methods for assessing the causal impact of meteorological variations on traffic dynamics, offering explanations and insights that can assist transportation institutions in guiding post-meteorology traffic management strategies. • Develop causality-aware deep methods between meteorological variations and traffic dynamics. • The designed MT-CIVAE effectively captures complex spatiotemporal correlations. • The designed MT-CIVAE effectively mitigates influence of undetermined confounders. • Experiments have been conducted on real-world traffic flow and speed data from California. [ABSTRACT FROM AUTHOR]

The accuracy of short-term traffic flow prediction is one of the important issues in the construction of smart cities, and it is an effective way to solve the problem of traffic congestion. Most previous studies could not effectively mine the potential relationship between the temporal and spatial dimensions of traffic data flow. Due to the large variability in the traffic flow data of road conditions, we analyzed it with "dynamic", using a dynamic-aware graph neural network model for the hidden relationships between space-time in the deep learning segment. In this paper, we propose a dynamic perceptual graph neural network model for the temporal and spatial hidden relationships of deep learning segments. This model mixes temporal features and spatial features with graphs and expresses them. The temporal features and spatial features are connected to each other to learn potential relationships, so as to more accurately predict the traffic speed in the future time period, we performed experiments on real data sets and compared with some baseline models. The experiments show that the method proposed in this paper has certain advantages. [ABSTRACT FROM AUTHOR]

Traffic speed forecasting is indispensable in Intelligent Traffic Systems (ITS). The missing traffic speed due to sensors' failure may hamper the accurate prediction of traffic speed. Different kinds of imputation techniques were employed to replace the missing values considering the spatial and temporal information. However, previous techniques did not consider the dynamic contribution of the neighboring nodes and failed to capture the complex properties of traffic speed data precisely. The architecture of the previous imputation models was infeasible in real-time applications in terms of scalability. Therefore, a novel machine learning-based imputation technique is proposed to generate the missing traffic speed using the spatial–temporal information and dynamic contribution of neighboring nodes. A new dataset considering the characteristics of the traffic speed data is generated from the existing dataset by exploiting the non-missing traffic speed of neighboring nodes. The newly formed dataset helps in the estimation of the dynamic contribution of the neighboring nodes using the linear regression algorithm. The non-missing speeds and estimated dynamic contribution of neighboring nodes assist in calculating the missing speeds of a node. Furthermore, a novel deep learning-based prediction model is introduced to forecast traffic speed accurately. The prediction model contains graph structure learning, a deep graph neural network with skip connections, GRU, multi-head soft attention, and a fully connected neural network. Graph structure learning is proposed based on the distribution of the congestion to represent the graph in an efficient way. The deep graph neural network with skip connections and GRU help in capturing topological and temporal information, respectively. The multi-head soft attention is designed to focus on every relevant temporal information at each time step for capturing global traffic information. Finally, the extracted spatial–temporal features are fed into the fully connected neural network to predict the traffic speed. The proposed imputation and prediction models are evaluated on two real-time datasets, METR-LA and PeMSD7 datasets, under different missing rates of different missing patterns. The proposed framework generates possible spatial–temporal information for efficient traffic congestion management and evicts erroneous issues in real-time. • We propose a learning framework to forecast traffic speed with missing values effectively. • A lightweight dynamic attentive imputation model estimates the missing values considering spatial–temporal information. • The estimated missing values with non-missing data points are engaged in a novel graph structure learning. • Graph neural network and multi-head soft attention-based GRU are employed to predict the traffic speed with imputed missing values. • The proposed learning framework has better performance in real-time traffic analysis. [ABSTRACT FROM AUTHOR]

Traffic prediction plays a crucial role in an intelligent transportation system (ITS) for enabling advanced transportation management and services. In this paper, we address the problem of multi-step traffic speed prediction, including both short- and long-term predictions. We assert that it is important to consider not just the fixed spatial dependency of the road network (i.e., the connections between road segments) but also the dynamic spatial dependency of traffic within the static topology that intertwines with the temporal evolution of traffic condition across the entire network. We propose a novel deep learning model, named Self-Attention Graph Convolutional Network with Spatial, Sub-spatial and Temporal blocks (SAGCN-SST) model, that specifically capture such complex dynamic spatial–temporal processes. In SAGCN-SST, we integrate self-attention mechanism into graph convolutional networks in a novel framework design while using a sequence-to-sequence model in an encoder–decoder architecture for extracting long-temporal dependency of traffic speed. Two real-world datasets with frequent traffic congestion and accidents from large-scale road networks (i.e., Seattle and Los Angeles) are used to train and test our model. Our experiment results indicate that the proposed deep learning model consistently achieves the most accurate predictions (higher than 98% accuracy on both datasets for the short- and long-term predictions) when compared against well-known existing models in recent literature. The results also indicate that SAGCN-SST is robust against emergent traffic situations. • Novel traffic deep learning prediction model on large-scale road networks. • Graph convolution network and attention mechanism for spatial feature analysis. • Sequence-to-sequence architecture for temporal feature analysis. • Result validated using real-world traffic datasets. [ABSTRACT FROM AUTHOR]

In this paper, a deep learning-based traffic state discrimination method is proposed to detect traffic congestion at urban intersections. The detection algorithm includes two parts, global speed detection and a traffic state discrimination algorithm. Firstly, the region of interest (ROI) is selected as the road intersection from the input image of the You Only Look Once (YOLO) v3 object detection algorithm for vehicle target detection. The Lucas-Kanade (LK) optical flow method is employed to calculate the vehicle speed. Then, the corresponding intersection state can be obtained based on the vehicle speed and the discrimination algorithm. The detection of the vehicle takes the position information obtained by YOLOv3 as the input of the LK optical flow algorithm and forms an optical flow vector to complete the vehicle speed detection. Experimental results show that the detection algorithm can detect the vehicle speed and traffic state discrimination method can judge the traffic state accurately, which has a strong anti-interference ability and meets the practical application requirements. [ABSTRACT FROM AUTHOR]