A predictability analysis of network traffic

The intelligent transportation system seeks to reduce traffic and improve the driving experience. They give us a lot of data that we can use to improve services for both the public and transportation officials by feeding it into machine learning systems. Most importantly, Traffic environment refers to everything that might have an impact on how much traffic is moving down the road, including traffic signals, accidents, protests, and even road repairs that might result in a backup. A motorist or rider can make an informed choice if they have previous knowledge that is very close to approximate all the above and many more real-world circumstances that can affect traffic. Additionally, it aids in the development of driverless vehicles. Traffic data have been growing dramatically in recent decades, and we are moving toward big data concepts for transportation. The current approaches for predicting traffic flow use some traffic prediction models, however they are still inadequate to handle practical situations. We thus aimed to focus on the traffic flow forecast problem using the traffic data and prediction models. The proposed model called DRGNN, a dilated recurrent graph neural network framework aims to effectively analyze and predict the traffic pattern by considering the spatial (space) and temporal (time) aspects of the real-time traffic data considering social relationships between internet of vehicles which indeed produced accurate and valuable insights that could help in deploying the model in any suitable real-time traffic monitoring and prediction system.

As is well acknowledged, deep learning has been fairly widely used in both academic and industrial fields in recent periods. Researchers favour its strong portability and good performance. Although deep learning has shown satisfactory performance in traffic flow prediction, its specific development process in such a field is not very clear, which is also not conducive for researchers to choose and adjust the model. This paper mainly retraces some significant models applied in traffic prediction before and after the advent of neural networks and digs out the reasons for their appearance. Through review and mining, we have sorted out the development history of traffic flow prediction models, providing us with some inspiration for subsequent research.

As the application of deep learning in intelligent transportation systems becomes increasingly widespread, the accuracy and reliability of traffic flow prediction have become crucial. However, existing deep learning methods are often complex in model design and lack intuitiveness, making it challenging to provide responsible explanations for traffic predictions. This paper references a responsible and reliable traffic flow prediction model (R2T-LLM) based on large language models (LLMs). This model captures complex spatiotemporal patterns and external factors by converting multimodal traffic data into natural language descriptions. By leveraging LLMs’ advanced understanding capabilities, R2T-LLM provides more transparent and interpretable predictions. It bridges the gap between technical performance and real-world application needs. While maintaining accuracy comparable to deep learning baselines, R2T-LLM offers intuitive and reliable prediction explanations. This paper also explores the spatiotemporal and input dependencies of conditional future traffic predictions and compares the model with other different approaches and types. Furthermore, it evaluates the potential of R2T-LLM in addressing challenges in large-scale urban traffic systems, highlighting its developmental and application prospects.

Traffic prediction is crucial for transportation management and user convenience. With the rapid development of deep learning techniques, numerous models have emerged for traffic prediction. Recurrent Neural Networks (RNNs) are extensively utilized as representative predictive models in this domain. This paper comprehensively reviews RNN applications in traffic prediction, focusing on their significance and challenges. The review begins by discussing the evolution of traffic prediction methods and summarizing state-of-the-art techniques. It then delves into the unique characteristics of traffic data, outlines common forms of input representations in traffic prediction, and generalizes an abstract description of traffic prediction problems. Then, the paper systematically categorizes models based on RNN structures designed for traffic prediction. Moreover, it provides a comprehensive overview of seven sub-categories of applications of deep learning models based on RNN in traffic prediction. Finally, the review compares RNNs with other state-of-the-art methods and highlights the challenges RNNs face in traffic prediction. This review is expected to offer significant reference value for comprehensively understanding the various applications of RNNs and common state-of-the-art models in traffic prediction. By discussing the strengths and weaknesses of these models and proposing strategies to address the challenges faced by RNNs, it aims to provide scholars with insights for designing better traffic prediction models.

This paper proposes a unified spatio-temporal model on the basis of STARIMA (Space-Time AutoRegressive Integrated Moving Average) for short-term road traffic prediction. The contributions of this paper are as follows. First, we develop a physically intuitive approach to traffic prediction that captures the time-varying spatio-temporal correlation between traffic at different measurement points. The spatio-temporal correlation is affected by the road network topology, time-varying speed, and time-varying trip distribution. Distinctly different from previous black-box approaches to road traffic modeling and prediction, parameters of the proposed approach have physically intuitive meanings which make them readily amenable to suit changing road and traffic conditions. Second, unlike some existing techniques which capture the variation of spatio-temporal correlation by a complete redesign and calibration of the model, the proposed approach uses a unified model which incorporates the physical factors potentially affecting the variation of spatio-temporal correlation into a series of parameters. These parameters are relatively easy to control and adjust when road and traffic conditions change, thereby greatly reducing the computational complexity. Experiments using two set of real traffic traces demonstrate that the proposed approach has superior accuracy compared with the widely used ARIMA (AutoRegressive Integrated Moving Average) and is only marginally inferior to that obtained by constructing multiple STARIMA models for different time of the day, however with a much reduced computational and implementation complexity.

In recent years, traffic flow prediction has attracted increasing interest from both academia and industry, and existing data-driven learning models for traffic flow prediction have achieved excellent success. However, this requires a large number of datasets for efficient model training, while it is difficult to acquire all the data from one agent, and thus data collaboration among different agents becomes an attracting trend. Moreover, with the increase in the number of agents, how to perform accurate multi-agent traffic forecasting while protecting privacy is an important issue. To address this challenge, we introduce a privacy-preserving federated learning framework. In this paper, we propose a novel Dynamic Spatio-Temporal traffic flow prediction model based on graph convolutional network (DST-GCN), which incorporates both dynamic spatial and temporal dependence of intersection traffic. In addition, we provide an improved federated learning framework with opportunistic client selection (FLoS). In the proposed FLoS protocol, we employ a FedAVG algorithm for secure parameter aggregation and design an optimal client selection algorithm to reduce the communication overhead during the transfer of model updates. Experiments based on real-world datasets demonstrate that our proposed DST-GCN traffic prediction model outperforms state-of-the-art baseline models. And our proposed FLoS can achieve superior results while reducing communication consumption. © 2022 IEEE.

For providing drivers with robust traffic information and Optimizing the energy management of Hybrid Electric Vehicles (HEVs), it is important to predict traffic information accurately with past traffic information. As acquisition of the traffic information have been easier by the development of Intelligent Transportation System (ITS), active study on traffic prediction is currently underway. Multi-Layer Perceptron (MLP) model have been widely utilized for predicting traffic information since it is appropriate to represent the non-linear characteristics inherent in traffic prediction. However, the MLP model doesn't reflect local dependencies of traffic data and is prone to noise in traffic data. Convolutional Neural Networks (CNN) based model, on the other hand, can capture the local dependencies of traffic data and is less prone to disturbance in data. In this paper, we use temporal data and speed data collected on main roads in Seoul, South Korea to construct traffic prediction models. The speed data which are collected by every 5 minutes are provided by Ministry of Land, Infrastructure and Transport in South Korea. We construct the CNN based model and two MLP models which predict traffic speed and compare performance of the prediction models in this paper. The comparison results show that the CNN based model's prediction performance is higher than the prediction performance of the other two MLP models.

Intelligent transportation systems can't function well without reliable data on current traffic conditions. As the volume of traffic data has increased in the last several years, we've entered the era of big data in transportation. Current methods for estimating traffic flow have been let down many real-world applications, which depend mostly on shallow traffic prediction models. Therefore, we'll review the difficulty of predicting traffic flow using detailed architectural models and a vast quantity of actual traffic data. Methods for traffic flow forecasting that consider both spatial and temporal correlations are given in this article. A layered auto encoder model is used to teach traffic flow characteristics since it builds up greedy layer by layer. Deep architecture models for traffic flow prediction have never before used auto encoders as building blocks. Researchers have also shown that the recommended method for estimating traffic flow is more accurate.

Accurate traffic flow prediction is crucial for improving transportation efficiency. To improve the accuracy of traffic flow prediction, we developed a traffic flow prediction framework—namely, traffic flow multicomponent network—that appropriately processes the noise, volatility, and nonlinearity in traffic flow data. This framework comprises three components: a factor selection component, traffic flow decomposition component, and traffic flow prediction component. The factor selection component considers the dynamic effects of weather‐related, environmental, and spatiotemporal factors on traffic flow; it then extracts and analyzes factors exhibiting strong correlations with traffic flow. The traffic flow decomposition component optimizes the parameters of variational mode decomposition on the basis of the envelope entropy by using the sparrow search algorithm; it then transforms traffic flow into multiple intrinsic mode functions to enable accurate traffic flow prediction. Finally, the traffic flow prediction component constructs dynamic feature matrices by using a bidirectional gated recurrent unit model to identify relationships within the data. Moreover, it uses an attention mechanism to assign different weights to different features on the basis of the importance of these features to traffic flow prediction, thereby enabling the efficient processing of a large volume of data. The performance of the proposed framework was examined in experiments conducted on large volumes of traffic flow data with different time granularities. The results indicated that the proposed framework achieved high prediction accuracy and stability for various time granularities, data samples, dataset sizes, and noise conditions. Moreover, it generally outperformed existing traffic flow prediction models under all experimental conditions.

As the fundamental part of other Intelligent Transportation Systems (ITS) applications, short-term traffic volume prediction plays an important role in various intelligent transportation tasks, such as traffic management, traffic signal control and route planning. Although Neural-network-based traffic prediction methods can produce good results, most of the models can’t be explained in an intuitive way. In this paper, we not only proposed a model that increase the short-term prediction accuracy of the traffic volume, but also improved the interpretability of the model by analyzing the internal attention score learnt by the model. we propose a spatiotemporal attention mechanism-based multistep traffic volume prediction model (SAMM). Inside the model, an LSTM-based Encoder-Decoder network with a hybrid attention mechanism is introduced, which consists of spatial attention and temporal attention. In the first level, the local and global spatial attention mechanisms considering the micro traffic evolution and macro pattern similarity, respectively, are applied to capture and amplify the features from the highly correlated entrance stations. In the second level, a temporal attention mechanism is employed to amplify the features from the time steps captured as contributing more to the future exit volume. Considering the time-dependent characteristics and the continuity of the recent evolutionary traffic volume trend, the timestamp features and historical exit volume series of target stations are included as the external inputs. An experiment is conducted using data from the highway toll collection system of Guangdong Province, China. By extracting and analyzing the weights of the spatial and temporal attention layers, the contributions of the intermediate parameters are revealed and explained with knowledge acquired by historical statistics. The results show that the proposed model outperforms the state-of-the-art model by 29.51% in terms of MSE, 13.93% in terms of MAE, and 5.69% in terms of MAPE. The effectiveness of the Encoder-Decoder framework and the attention mechanism are also verified.

With the rapid development of intelligent transportation systems, vehicle detection and traffic flow statistics have become increasingly critical in traffic management and the construction of smart cities. However, complex weather conditions lead to challenges in vehicle detection algorithms, increasing false negatives and false positives. Additionally, the rapid growth of vehicle ownership makes traditional algorithms inadequate for meeting practical application needs. Therefore, this paper proposes a collaborative optimization-based method combining YOLOv11 for vehicle detection and traffic flow analysis under complex weather conditions. Vehicle information is accurately extracted through object detection and tracking methods, and further analyzed using Logistic Regression to assess traffic congestion. The main contributions of this study are: (1) Integration of the CBAM attention mechanism into YOLOv11, enhancing the vehicle feature extraction ability through collaborative channel and spatial attention, thereby improving interference resistance under complex weather conditions. (2) Adoption of the Shape-IoU loss function, with dynamic weight allocation to optimize the accuracy of detecting smaller vehicles and the precision of bounding box regression in images where vehicles are located at a greater distance. (3) Construction of a traffic congestion prediction model based on the detection results, integrated with Logistic Regression. The proposed method's effectiveness is validated on the public dataset UA-DETRAC. Through the synergistic effect of the CBAM attention mechanism and Shape-IoU loss function, the improved model's mAP50-95 shows a 0.3 percentage point increase over the YOLOv11 baseline, providing efficient and reliable technical support for intelligent transportation systems. This study enhances the prediction performance of traffic states under complex weather conditions by effectively calculating traffic flow parameters such as vehicle count and density, thus offering foundational data support for traffic management decision-making. The research conclusions are of practical significance for urban traffic improvement.

With the increasing complexity of the network, the current network traffic has strong nonlinearity and burstiness. Therefore, the traditional traffic prediction model is no longer applicable. The neural network model, especially the LSTM, can well fit the nonlinearity of time-series data and preserve the information memory of the past. However, as for the periodicity of long-term span network traffic data, the neural network model does not perform well. Based on this, this paper proposes LTS-TP (Long-Term Span Traffic Prediction model), a network traffic prediction model, to solve the problem. First, the model decomposes the collected network traffic data using the improved STL decomposition algorithm to preserve the seasonal component. Then, the trend component and the remainder component are input into the Seq2Seq model based on the LSTM added with the improved attention mechanism for prediction. Finally, the predicted value of the output is added to the seasonal component, and the final network traffic prediction value is obtained. In the simulation part, this paper uses the MAWI public data set to test the proposed network traffic prediction model and compared performance with other models. The results show that the network traffic prediction model proposed in this paper has a good predictive effect on long-term span network traffic data.

Highway congestion has become a normal social problem that needs to be managed more accurately and deeply. In this paper, a traffic flow congestion prediction model is established based on LSTM, and the traffic parameters obtained from four observation points of China's long and deep high-speed video are used for training, so as to provide real-time early warning before the continuous congestion of the road section between the third and fourth points. The results show that the model constructed in this paper can reach 89.25% accuracy after predicting 400 times steps in 50 times Windows. After further evaluation of the constructed traffic flow congestion prediction model, it is found that the mean square error (MSE), mean absolute error (MAE) and R2 is 0.0897, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.2334,0.6028$</tex>, respectively, revealing that the model can well predict the future traffic congestion.

Due to the randomness and weak symmetry of traffic accidents occurring in the expressway maintenance operation area, it is difficult to use the number of traffic accidents to evaluate the safety of maintenance operation areas. In this paper, the traffic characteristics and traffic conflicts of the maintenance operation area with the lane closed on the outside of the two-way four-lane expressway are studied. By using the statistical method, the distribution of vehicle speed and time headway in different areas of the maintenance operation area are analyzed, and the queuing characteristics of vehicles in the upstream transition zone of the expressway are determined. Based on improved time to collision (TTC) model, the traffic conflict severity of expressway maintenance operation area is divided. The negative binomial distribution is used to establish a traffic conflict prediction model for the enclosed maintenance area of the outer lane of the expressway, and the validity of the traffic conflict prediction model is verified based on the average absolute error percentage (MAPE). The research results show that: when the 0 &lt; TTC &lt; 1.3 s, the traffic conflict is serious conflict; when 1.3 s &lt; TTC, the traffic conflict is non-serious conflict. Furthermore, the traffic conflict prediction model has high accuracy, the MAPE of the warning area and the upstream transition area are 10.8% and 5.0%, respectively.

Traffic flow has the characteristics of complex spatial dependence and temporal dependence. Deep learning as a traffic flow prediction method can make full use of the temporal and spatial characteristics of traffic flow. In this paper, the road network is abstracted into a graph structure, the size of the graph structure is dynamically changed, and the graph convolutional neural network (GCN) and the long short term memory network (LSTM) are used to capture the temporal and spatial characteristics of traffic flow to solve the traffic flow prediction problem. Based on the data of vehicle speed in California bay area, the experiment is divided into three prediction scales. The effectiveness of the traffic flow prediction model is verified by experimental comparison.