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Traffic prediction during hurricane evacuation is essential for optimizing the use of transportation infrastructures. Traffic prediction can reduce evacuation time by providing information in advance on future congestion. However, evacuation traffic prediction can be challenging because evacuation traffic patterns are significantly different than are those for regular period traffic. A data-driven traffic prediction model is developed in this study by utilizing traffic detector and Facebook movement data during Hurricane Ian, a rapidly intensifying hurricane. We select 766 traffic detectors from Florida's four major interstates to collect traffic features. Additionally, we use Facebook movement data collected during Hurricane Ian's evacuation period. The deep-learning model is first trained on regular period (May to August 2022) data to understand regular traffic patterns. Then, Hurricane Ian's evacuation period data are used as test data. The model achieves 95% accuracy (RMSE=356) during regular period but underperforms with 55% accuracy (RMSE=1,084) during the evacuation period. Then, a transfer learning approach is adopted in which a pretrained model is used with additional evacuation-related features to predict evacuation period traffic. After transfer learning, the model achieves 89% accuracy (RMSE=514). Adding Facebook movement data further reduces the model's RMSE value to 393 and increases accuracy to 93%. The proposed model is capable of forecasting traffic up to 6-h in advance. Evacuation traffic management officials can use the developed traffic prediction model to anticipate future traffic congestion in advance and take proactive measures to reduce delays during evacuation. Practical Applications: Hurricane evacuation causes significant traffic congestion in transportation networks. Increased traffic demand can affect the evacuation process because it delays the movement of people to safer locations. To remedy this issue, an accurate traffic prediction model is beneficial for evacuation traffic management. The prediction model can give expected traffic volume on evacuation routes well in advance, which allows traffic management agencies to prepare for and activate strategies such as emergency shoulder utilization, adjustments to signal timing for optimal traffic flow, and others on those evacuation routes. This work aims to construct a data-driven model to predict traffic flow with a lead time of up to 6 h. The model can be used to forecast networkwide traffic in real time. Thus, practitioners can use this tool to effectively implement evacuation traffic management strategies by determining the timing, locations, and extent of those strategies based on predicted traffic volume. Another benefit of this model is that it can be trained with data from normal period and historical hurricane evacuations and then be implemented for future hurricanes. [ABSTRACT FROM AUTHOR]

The application of intelligent transportation systems (ITSs) relies heavily on accurate traffic prediction, which hinges on effectively capturing spatial-temporal features. Current methodologies often address spatial and temporal dependencies separately, which limits their ability to synchronize modeling efforts. Moreover, existing graph convolutional network (GCN) approaches primarily support transductive learning and fall short in inductive tasks. To address these challenges, this paper introduces a novel spatial-temporal synchronous GraphSAGE (STS-GraphSAGE) model for traffic prediction. By integrating spatial and temporal correlations into a unified graph structure, STS-GraphSAGE achieves synchronous learning of these dependencies. Specifically, we introduce the Spearman correlation coefficient to compensate for the spatial adjacency matrix, facilitating the construction of an inclusive spatial graph. Coupled with a causal temporal graph, this forms a spatial-temporal synchronous graph that is capable of capturing intricate dependencies across both dimensions. Furthermore, our model employs multiple STS-GraphSAGE layers equipped with attention mechanisms to inductively aggregate spatial-temporal features from neighboring nodes. Extensive experiments on real-world datasets validate the effectiveness of STS-GraphSAGE, which significantly outperforms state-of-the-art baselines in traffic prediction tasks. [ABSTRACT FROM AUTHOR]

Accurate and efficient traffic information prediction is significantly important for the management of intelligent transportation systems. The traffic status (e.g., speed or flow) on one road segment is spatially affected by both its nearby neighbors and distant locations. The impending traffic status can be temporally influenced not only by its recent status but also by the randomness of its historical status change. The current state-of-the-art methods have effectively captured the spatio-temporal dependencies of road networks. However, most existing methods overlook the impact of time delay when capturing dynamic time dependencies. In addition, aggregating roads with similar traffic patterns from a wide range of spatial associations still poses challenges. In this paper, a spatial-temporal nonlinear auto-regressive multi-channel neural network (ST-NAMN) model is proposed to reveal the sophisticated nonlinear dynamic interconnections between temporal and spatial dependencies in road traffic data. Considering the temporal periodicity and spatial pattern similarity inherently in road traffic data, a divided period latent similarity correlation matrix (DLSC) first is utilized to calculate the similarity of traffic patterns from historical observation data. Then, we introduce an output feedback to the multi-layer perceptron (MLP) through a delay unit, which enables the output-layer to feedback its result data to the input layer in real-time, and further participate in the next iterative training to boost the learning capacity. Furthermore, an Enhanced-Bayesian Regularization weight updating method (EBR) is designed to better contemplate the influence of the continuous and delayed observation points compared to existing optimizers during the learning procedure. Experimental tests have been carried out on four real-world datasets and the results demonstrated that the proposed ST-NAMN method outperforms other state-of-the-art models. [ABSTRACT FROM AUTHOR]

Deep learning models are widely used for traffic forecasting on freeways due to their ability to learn complex temporal and spatial relationships. In particular, graph neural networks, which integrate graph theory into deep learning, have become popular for modeling traffic sensor networks. However, traditional graph convolutional networks (GCNs) face limitations in capturing long-range spatial correlations, which can hinder accurate long-term predictions. To address this issue, we propose the Two-level Resolution Neural Network, which enhances interpretability by introducing two resolution blocks. The first block captures large-scale regional traffic patterns, while the second block, using a GCN, focuses on small-scale spatial correlations, informed by the regional predictions. This structure allows the model to intuitively integrate both local and distant traffic data, improving long-term forecasting. In addition to its predictive capabilities, TwoResNet offers enhanced interpretability, particularly in scenarios involving noisy or incomplete data. [ABSTRACT FROM AUTHOR]

Traffic flow prediction, crucial for traffic management, relies on spatial and temporal data to achieve high accuracy. However, standard performance metrics only measure the average prediction errors and overlook the spatiotemporal aspects. To address this gap, this study introduces three simple spatial key performance indicators (KPIs): Global Moran's I, Getis-Ord General G, and Adapted PageRank Algorithm Modified (APAM). We evaluated the traffic prediction results for synthetic clustering scenarios and four different prediction methods applied to the PeMSD8 dataset using spatial KPIs. Spatial KPIs are calculated based on traffic prediction errors and the adjacency matrix of the traffic network. Our results demonstrate that spatial KPIs can effectively differentiate between synthetic clustering scenarios. Global Moran's I measures the spatial autocorrelation, Getis-Ord General G measures the spatial clustering of high/low values, and the univariate analysis of APAM deduces the distribution of node importance by considering node centrality and node values. Getis-Ord General G showed the highest sensitivity, being capable of distinguishing between methods with similar average RMSE, whereas Global Moran's I and APAM univariate analysis revealed subtle differences between methods. Spatial KPIs serve as important complementary metrics for performance evaluation in the design of robust traffic management systems. [ABSTRACT FROM AUTHOR]

Timely exact distracted driving risk prediction is beneficial to perceive real-time traffic risk, which is an essential but challenging task in modern traffic safety management. The use and improvement of measures for road safety management will be better facilitated by grasping and analyzing the spatio-temporal patterns of driving behavior and forming predictions. In this paper, a Distracted Driving Risk Prediction (DDRP) neural network by deep learning and spatio-temporal dependence is proposed, which to accurately predict the scale of distracted driving behavior on road networks. Then, the method is employed for distracted driving risk prediction based on the provincial road network. The experiment demonstrates that our method performs relatively better than the other methods applied in this paper. In addition, the method can adapt to predict the scale of distracted driving behavior in different categories, time intervals, and grid cells. [ABSTRACT FROM AUTHOR]

The increasing complexity of urban road networks has driven the development of Intelligent Transportation Systems (ITS) to optimize vehicle flow. To address this challenge, this paper presents an algorithm and MATLAB function that generates an adjacency list of traffic signals to provide detailed information about the relationships between all signals within a network. This list is based on stable structural road and traffic lights data and offers a crucial global perspective for signal coordination, especially in managing multiple intersections. An adjacency list is more efficient than matrices in terms of space and computational cost, allowing for the identification of critical signals before applying advanced optimization techniques such as neural networks or hypergraphs. We successfully tested the proposed method on three networks of varying complexity extracted from VISSIM and VISUM, demonstrating its effectiveness even in networks with up to 8372 links and 547 traffic lights. This tool provides a solid foundation for improving urban traffic management and coordinating signals across intersections. [ABSTRACT FROM AUTHOR]

Traffic prediction is crucial to the intelligent transportation system. However, accurate traffic prediction still faces challenges. It is difficult to extract dynamic spatial–temporal correlations of traffic flow and capture the specific traffic pattern for each sub-region. In this paper, a temporal attention recurrent graph convolutional neural network (TARGCN) is proposed to address these issues. The proposed TARGCN model fuses a node-embedded graph convolutional (Emb-GCN) layer, a gated recurrent unit (GRU) layer, and a temporal attention (TA) layer into a framework to exploit both dynamic spatial correlations between traffic nodes and temporal dependencies between time slices. In the Emb-GCN layer, node embedding matrix and node parameter learning techniques are employed to extract spatial correlations between traffic nodes at a fine-grained level and learn the specific traffic pattern for each node. Following this, a series of gated recurrent units are stacked as a GRU layer to capture spatial and temporal features from the traffic flow of adjacent nodes in the past few time slices simultaneously. Furthermore, an attention layer is applied in the temporal dimension to extend the receptive field of GRU. The combination of the Emb-GCN, GRU, and the TA layer facilitates the proposed framework exploiting not only the spatial–temporal dependencies but also the degree of interconnectedness between traffic nodes, which benefits the prediction a lot. Experiments on public traffic datasets PEMSD4 and PEMSD8 demonstrate the effectiveness of the proposed method. Compared with state-of-the-art baselines, it achieves 4.62% and 5.78% on PEMS03, 3.08% and 0.37% on PEMSD4, and 5.08% and 0.28% on PEMSD8 superiority on average. Especially for long-term prediction, prediction results for the 60-min interval show the proposed method presents a more notable advantage over compared benchmarks. The implementation on Pytorch is publicly available at https://github.com/csust-sonie/TARGCN. [ABSTRACT FROM AUTHOR]

Current research often formalizes traffic prediction tasks as spatio-temporal graph modeling problems. Despite some progress, this approach still has the following limitations. First, space can be divided into intrinsic and latent spaces. Static graphs in intrinsic space lack flexibility when facing changing prediction tasks, while dynamic relationships in latent space are influenced by multiple factors. A deep understanding of specific traffic patterns in different spaces is crucial for accurately modeling spatial dependencies. Second, most studies focus on correlations in sequential time periods, neglecting both reverse and global temporal correlations. This oversight leads to incomplete temporal representations in models. In this work, we propose a Space-Specific Graph Convolutional Recurrent Transformer Network (SSGCRTN) to address these limitations simultaneously. For the spatial aspect, we propose a space-specific graph convolution operation to identify patterns unique to each space. For the temporal aspect, we introduce a spatio-temporal interaction module that integrates spatial and temporal domain knowledge of nodes at multiple granularities. This module learns and utilizes parallel spatio-temporal relationships between different time points from both forward and backward perspectives, revealing latent patterns in spatio-temporal associations. Additionally, we use a transformer-based global temporal fusion module to capture global spatio-temporal correlations. We conduct experiments on four real-world traffic flow datasets (PeMS03/04/07/08) and two traffic speed datasets (PeMSD7(M)/(L)), achieving better performance than existing technologies. Notably, on the PeMS08 dataset, our model improves the MAE by 6.41% compared to DGCRN. The code of SSGCRTN is available at https://github.com/OvOYu/SSGCRTN. [ABSTRACT FROM AUTHOR]

Recently, the increasing rapid number of cars on the roads and the great demand for traffic prediction, because of the increase in traffic congestion, has posed a great challenge to governments' economic development and social stability of most countries in the world. While many challenges are facing governments to solve traffic congestion and reduce car accidents and pollution, the objective of this study is to apply a dynamic approach for the Jordanian community to lessen traffic congestion on the roads in Amman by utilising social media, artificial intelligence efficiency methods, and decision support to lessen traffic issues in the capital city of Jordan. It also aims to communicate directly with social media users to cut down on the time and effort needed to make traffic predictions, which will help to relieve the congestion of areas and roads in Amman. To obtain the research aims, we handled a TRF2021JOR dataset related to traffic records in Amman city, collected from Amman Municipality for several years. Then, we applied the Netnography model, which was built using the data science concepts, to create an efficient model in Amman city for traffic prediction and time series based on selective features of previous congestion in city roads. The experiment results showed the accuracy preference for the proposed method in Amman city. Furthermore, the experiment tested the accuracy of results based on the machine learning methods using the KNIME Analytics Platform tool, one of the Artificial Intelligence (AI) methods used; the experiment results showed that the classifier SVM gives a low accuracy reached (89.9%). However, the accuracy using the decision tree classifier reached (90.59%), while the classifier random forest gives a high accuracy reached (93.16%). [ABSTRACT FROM AUTHOR]

Traffic flow prediction plays a crucial role in intelligent transportation systems (ITS), offering applications across diverse domains. However, current deep learning models face significant challenges. Real-world traffic conditions, especially during peak hours, exhibit complex spatio-temporal dynamics and intricate nonlinear relationships. Existing studies often overlook variations in traffic flow across different time periods, locations, and scenarios, resulting in prediction models lacking robustness and accuracy across diverse contexts. Furthermore, simplistic models struggle to accurately forecast traffic flow during peak periods, as they typically focus on isolated features such as traffic speed, flow rate, or occupancy rate, neglecting crucial interdependencies with other relevant factors. This paper introduces a novel approach, the peak hour embedding-based multi-feature spatio-temporal coupled traffic flow prediction model (PE-MFSTC), to address these challenges. The PE-MFSTC model incorporates peak time embedding within a multirelational synchronization graph attention network structure. The peak time-based embedding involves mapping daily, weekly, and morning/evening peak periods into low-dimensional time representations, facilitating the extraction of nonlinear spatio-temporal features. The network framework employs a multirelational synchronized graph attention network, integrating multiple traffic features and spatio-temporal sequences for learning. Additionally, a spatio-temporal dynamic fusion module (STDFM) is introduced to model correlations and dynamically adjust node weights, enhancing the model's sensitivity. Experimental evaluations on four real-world public datasets consistently demonstrate the superior performance of the PE-MFSTC model over seven state-of-the-art deep learning models. These results highlight the efficacy of the proposed model in addressing the complexities of traffic flow prediction across various scenarios. [ABSTRACT FROM AUTHOR]

Traffic prediction is of vital importance in intelligent transportation systems. It enables efficient route planning, congestion avoidance, and reduction of travel time, etc. However, accurate road traffic prediction is challenging due to the complex spatio-temporal dependencies within the traffic network. Establishing and learning spatial dependencies are pivotal for accurate traffic prediction. Unfortunately, many existing methods for capturing spatial dependencies consider only single relationships, disregarding potential temporal and spatial correlations within the traffic network. Moreover, the end-to-end training methods often lack control over the training direction during graph learning. Additionally, existing traffic forecasting methods often fail to integrate multiple traffic data sources effectively, which affects prediction accuracy adversely. In order to capture the spatiotemporal dependencies of the traffic network accurately, a novel traffic prediction framework, Adaptive Spatio-Temporal Graph Neural Network based on Multi-graph Fusion (DTS-AdapSTNet), is proposed. Firstly, in order to better extract the hidden spatial dependencies, a method for fusing multiple factors is designed, which includes the distance relationship, transfer relationship and same-road segment relationship of traffic data. Secondly, an adaptive learning method is proposed, which can control the learning direction of parameters better by the adaptive matrix generation module and traffic prediction module. Thirdly, an improved loss function is designed for training processes and a multi-matrix fusion module is designed to perform weighted fusion of the learned matrices, updating the spatial adjacency matrix continuously, which fuses as much traffic information as possible for more accurate traffic prediction. Finally, experimental results using two large real-world datasets demonstrate that the DTS-AdapSTNet model outperforms other baseline models in terms of mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) when forecasting traffic speed one hour ahead. On average, it achieves reductions of 12.4%, 9.8% and 16.1%, respectively. Moreover, the ablation study validates the effectiveness of the individual modules of DTS-AdapSTNet. [ABSTRACT FROM AUTHOR]

Accurate forecasting of traffic patterns plays a crucial role in the effective management and planning of urban transportation infrastructure. In particular, predicting the availability of electric vehicle (EV) charging stations is essential for alleviating range anxiety among drivers and facilitating the adoption of electric vehicles. This study proposes a novel deep learning-based predictor model to approximate the demand for charging electric vehicles over the long term. The methodology integrates the Berkeley wavelet transform (BWT) to decompose input time series data while preserving its inherent characteristics. The proposed hybrid prediction model combines an enhanced gate recurrent unit with an optimized convolution kernel within a fusion graph convolutional network (GCN). The Red Kite Optimization Algorithm (RKOA) is employed to select the convolution kernel of the GCN effectively. Additionally, the construction of the graph leverages both adjacency and adaptive graphs to accurately represent the correlations among nodes in the EV network. The model extracts multi-level spatial correlations through stacked fusion graph convolutional elements and captures multi-scale temporal correlations via an improved gated recurrent unit. Furthermore, the incorporation of residual connection units allows for the fusion of extracted spatiotemporal features with direct data, enhancing predictive performance. The proposed neural predictor is evaluated using EV charging data from Georgia Tech in Atlanta, USA. The experimental results demonstrate the effectiveness of the prediction metrics generated by the proposed model compared to existing methods reported in the literature, showcasing its capability to accurately forecast EV charging demand.Article highlights: In this research work, a novel deep learning (DL)-based predictor model is attempted to be developed for charging electric vehicles. To suggests a hybrid prediction model that is built on an upgraded gate recurrent unit and an optimised convolution kernel of a fusion graph convolutional network (GCN). Red Kite Optimisation Algorithm (RKOA) selects the convolution kernel of the GCN optimally. The outcomes demonstrate the effectiveness of the prediction metrics calculated using the suggested neural predictor for the examined dataset when compared to earlier methods from published studies. [ABSTRACT FROM AUTHOR]

Abstract Traffic prediction plays an increasingly important role in intelligent transportation systems and smart cities. Both travelers and urban managers rely on accurate traffic information to make decisions about route selection and traffic management. Due to various factors, both human and natural, traffic data often contains missing values. Addressing the impact of missing data on traffic flow prediction has become a widely discussed topic in the academic community and holds significant practical importance. Existing spatiotemporal graph models typically rely on complete data, and the presence of missing values can significantly degrade prediction performance and disrupt the construction of dynamic graph structures. To address this challenge, this paper proposes a neural network architecture designed specifically for missing data scenarios—graph convolutional recurrent ordinary differential equation network (GCRNODE). GCRNODE combines recurrent networks based on ordinary differential equation (ODE) with spatiotemporal memory graph convolutional networks, enabling accurate traffic prediction and effective modeling of dynamic graph structures even in the presence of missing data. GCRNODE uses ODE to model the evolution of traffic flow and updates the hidden states of the ODE through observed data. Additionally, GCRNODE employs a data-independent spatiotemporal memory graph convolutional network to capture the dynamic spatial dependencies in missing data scenarios. The experimental results on three real-world traffic datasets demonstrate that GCRNODE outperforms baseline models in prediction performance under various missing data rates and scenarios. This indicates that the proposed method has stronger adaptability and robustness in handling missing data and modeling spatiotemporal dependencies.

Abstract Accurate forecasting of traffic patterns plays a crucial role in the effective management and planning of urban transportation infrastructure. In particular, predicting the availability of electric vehicle (EV) charging stations is essential for alleviating range anxiety among drivers and facilitating the adoption of electric vehicles. This study proposes a novel deep learning-based predictor model to approximate the demand for charging electric vehicles over the long term. The methodology integrates the Berkeley wavelet transform (BWT) to decompose input time series data while preserving its inherent characteristics. The proposed hybrid prediction model combines an enhanced gate recurrent unit with an optimized convolution kernel within a fusion graph convolutional network (GCN). The Red Kite Optimization Algorithm (RKOA) is employed to select the convolution kernel of the GCN effectively. Additionally, the construction of the graph leverages both adjacency and adaptive graphs to accurately represent the correlations among nodes in the EV network. The model extracts multi-level spatial correlations through stacked fusion graph convolutional elements and captures multi-scale temporal correlations via an improved gated recurrent unit. Furthermore, the incorporation of residual connection units allows for the fusion of extracted spatiotemporal features with direct data, enhancing predictive performance. The proposed neural predictor is evaluated using EV charging data from Georgia Tech in Atlanta, USA. The experimental results demonstrate the effectiveness of the prediction metrics generated by the proposed model compared to existing methods reported in the literature, showcasing its capability to accurately forecast EV charging demand.

Traffic prediction plays an increasingly important role in intelligent transportation systems and smart cities. Both travelers and urban managers rely on accurate traffic information to make decisions about route selection and traffic management. Due to various factors, both human and natural, traffic data often contains missing values. Addressing the impact of missing data on traffic flow prediction has become a widely discussed topic in the academic community and holds significant practical importance. Existing spatiotemporal graph models typically rely on complete data, and the presence of missing values can significantly degrade prediction performance and disrupt the construction of dynamic graph structures. To address this challenge, this paper proposes a neural network architecture designed specifically for missing data scenarios—graph convolutional recurrent ordinary differential equation network (GCRNODE). GCRNODE combines recurrent networks based on ordinary differential equation (ODE) with spatiotemporal memory graph convolutional networks, enabling accurate traffic prediction and effective modeling of dynamic graph structures even in the presence of missing data. GCRNODE uses ODE to model the evolution of traffic flow and updates the hidden states of the ODE through observed data. Additionally, GCRNODE employs a data-independent spatiotemporal memory graph convolutional network to capture the dynamic spatial dependencies in missing data scenarios. The experimental results on three real-world traffic datasets demonstrate that GCRNODE outperforms baseline models in prediction performance under various missing data rates and scenarios. This indicates that the proposed method has stronger adaptability and robustness in handling missing data and modeling spatiotemporal dependencies. [ABSTRACT FROM AUTHOR]

Traffic flow prediction plays a crucial role in intelligent transportation systems as it enables effective control and management of urban traffic. However, existing methods that based on Graph Convolutional Networks (GCNs) primarily utilize local neighborhood information for message passing, resulting in limited perception of global structures. Additionally, it is also a challenge to extract spatial-temporal similarity features due to the constraints of graph structures. To address these issues, we propose a novel traffic flow prediction model based on Dynamic Spatial-Temporal Similarity Pyramid Network (DSTSPYN). Our model employs a spatial-temporal pyramid architecture, which dynamically adjusts the weights of central, edge, and global spatial-temporal features using an enhanced attention mechanism. Furthermore, it captures dynamic temporal dependencies at different scales through pyramid gated convolution. Meanwhile, the spatial similarity features of different time steps can be extracted through the spatial-temporal global similarity (STGS) module. We evaluate our model on four public transportation datasets and demonstrate that the DSTSPYN model outperforms several baseline methods in terms of prediction accuracy. It effectively captures the dynamic spatial-temporal correlations of the road network and edge node features, making it well-suited for long-term traffic flow prediction. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is essential in several Internet application areas and remains a key technology for communication technologies. Shorter delays in transmission between Roadside Units (RSUs) and vehicles, road safety, and smooth traffic flow are the major difficulties of Intelligent Transportation System (ITS). Machine Learning (ML) was an advanced technique to find hidden insights into ITSs. This article introduces an Improved Arithmetic Optimization with Deep Learning Driven Traffic Congestion Control (IAOADL-TCC) for ITS in Smart Cities. The presented IAOADL-TCC model enables traffic data collection and route traffic on existing routes for avoiding traffic congestion in smart cities. The IAOADL-TCC algorithm exploits a hybrid convolution neural network attention long short-term memory (HCNN-ALSTM) method for traffic congestion control. In addition, an IAOA-based hyperparameter tuning strategy is derived to optimally modify the parameters of the HCNN-ALSTM model. The presented IAOADL-TCC model effectively enhances the flow of traffic and reduces congestion. The experimental validation was performed using the road traffic dataset from the Kaggle repository. The proposed model obtained an average accuracy of 98.03 % with an error rate of 1.97 %. The experimental analysis stated the superior performance of the IAOADL-TCC approach over other DL methods.

Traffic prediction is crucial for urban planning and transportation management, and deep learning techniques have emerged as effective tools for this task. While previous works have made advancements, they often overlook comprehensive analyses of spatio-temporal distributions and the integration of multimodal representations. Our research addresses these limitations by proposing a large-scale spatio-temporal multimodal fusion framework that enables accurate predictions based on location queries and seamlessly integrates various data sources. Specifically, we utilize Convolutional Neural Networks (CNNs) for spatial information processing and a combination of Recurrent Neural Networks (RNNs) for final spatio-temporal traffic prediction. This framework not only effectively reveals its ability to integrate various modal data in the spatio-temporal hyperspace, but has also been successfully implemented in a real-world large-scale map, showcasing its practical importance in tackling urban traffic challenges. The findings presented in this work contribute to the advancement of traffic prediction methods, offering valuable insights for further research and application in addressing real-world transportation challenges.

Cloud computing provides on-demand computing services over the Internet, allowing for quicker innovation, more flexible resources, and economies of scale while reducing the need for physical data centers and servers. With this benefit, most organizations are adopting this technology, and some organizations are also operating fully on cloud computing. This causes traffic to increase, and most of these organizations are struggling with resource allocation, resulting in complaints from users regarding inactive system performance, timeouts in applications, and higher bandwidth use during peak hours. In this regard, this study investigates data analytics techniques and tools for the allocation of resources in cloud computing. The study indexed journal articles from the Scopus Database and Web of Science (WOS) between 2010 and 2024. This article brings new insights into the analysis of data analytics techniques in Africa and collaborations with other developing countries. The findings present tools and approaches that may be used to allocate cloud computing resources and give recommendations.

Abstract Software-defined networking (SDN) revolutionizes network administration by centralizing control and decoupling the data plane from the control plane. Despite its advantages, the escalating volume of network traffic induces congestion at nodes, adversely affecting routing quality and overall performance. Addressing congestion has become imperative due to its emergence as a fundamental challenge in network management. Previous strategies often faced drawbacks in handling congestion, with issues arising from the inability to efficiently manage heavy packet surges in specific network regions. In response, this research introduces a novel approach integrating a multiplicative gated recurrent neural network with a congestion-aware hunter prey optimization (HPO) algorithm for effective traffic management in SDN. The framework leverages machine learning and deep learning techniques, acknowledged for their proficiency in processing traffic data. Comparative simulations showcase the congestion-aware HPO algorithm's superiority, achieving a normalized throughput 3.4–7.6% higher than genetic algorithm (GA) and particle swarm optimization (PSO) alternatives. Notably, the proposed framework significantly reduces data transmission delays by 58–65% compared to the GA and PSO algorithms. This research not only contributes a state-of-the-art solution but also addresses drawbacks observed in existing methodologies, thereby advancing the field of traffic engineering and congestion management in SDN. The proposed framework demonstrates notable enhancements in both throughput and latency, providing a more robust foundation for future SDN implementations.

Abstract Traffic prediction is crucial to the intelligent transportation system. However, accurate traffic prediction still faces challenges. It is difficult to extract dynamic spatial–temporal correlations of traffic flow and capture the specific traffic pattern for each sub-region. In this paper, a temporal attention recurrent graph convolutional neural network (TARGCN) is proposed to address these issues. The proposed TARGCN model fuses a node-embedded graph convolutional (Emb-GCN) layer, a gated recurrent unit (GRU) layer, and a temporal attention (TA) layer into a framework to exploit both dynamic spatial correlations between traffic nodes and temporal dependencies between time slices. In the Emb-GCN layer, node embedding matrix and node parameter learning techniques are employed to extract spatial correlations between traffic nodes at a fine-grained level and learn the specific traffic pattern for each node. Following this, a series of gated recurrent units are stacked as a GRU layer to capture spatial and temporal features from the traffic flow of adjacent nodes in the past few time slices simultaneously. Furthermore, an attention layer is applied in the temporal dimension to extend the receptive field of GRU. The combination of the Emb-GCN, GRU, and the TA layer facilitates the proposed framework exploiting not only the spatial–temporal dependencies but also the degree of interconnectedness between traffic nodes, which benefits the prediction a lot. Experiments on public traffic datasets PEMSD4 and PEMSD8 demonstrate the effectiveness of the proposed method. Compared with state-of-the-art baselines, it achieves 4.62% and 5.78% on PEMS03, 3.08% and 0.37% on PEMSD4, and 5.08% and 0.28% on PEMSD8 superiority on average. Especially for long-term prediction, prediction results for the 60-min interval show the proposed method presents a more notable advantage over compared benchmarks. The implementation on Pytorch is publicly available at https://github.com/csust-sonie/TARGCN .

The Internet of Things (IoT) is essential in several Internet application areas and remains a key technology for communication technologies. Shorter delays in transmission between Roadside Units (RSUs) and vehicles, road safety, and smooth traffic flow are the major difficulties of Intelligent Transportation System (ITS). Machine Learning (ML) was an advanced technique to find hidden insights into ITSs. This article introduces an Improved Arithmetic Optimization with Deep Learning Driven Traffic Congestion Control (IAOADL-TCC) for ITS in Smart Cities. The presented IAOADL-TCC model enables traffic data collection and route traffic on existing routes for avoiding traffic congestion in smart cities. The IAOADL-TCC algorithm exploits a hybrid convolution neural network attention long short-term memory (HCNN-ALSTM) method for traffic congestion control. In addition, an IAOA-based hyperparameter tuning strategy is derived to optimally modify the parameters of the HCNN-ALSTM model. The presented IAOADL-TCC model effectively enhances the flow of traffic and reduces congestion. The experimental validation was performed using the road traffic dataset from the Kaggle repository. The proposed model obtained an average accuracy of 98.03 % with an error rate of 1.97 %. The experimental analysis stated the superior performance of the IAOADL-TCC approach over other DL methods. [ABSTRACT FROM AUTHOR]

Origin-destination demand prediction is a critical task in the field of intelligent transportation systems. However, accurately modeling the complex spatial-temporal dependencies presents significant challenges, which arises from various factors, including spatial, temporal, and external influences such as geographical features, weather conditions, and traffic incidents. Moreover, capturing multi-scale dependencies of local and global spatial dependencies, as well as short and long-term temporal dependencies, further complicates the task. To address these challenges, a novel framework called the Spatial-Temporal Memory Enhanced Multi-Level Attention Network (ST-MEN) is proposed. The framework consists of several key components. Firstly, an external attention mechanism is incorporated to efficiently process external factors into the prediction process. Secondly, a dynamic spatial feature extraction module is designed that effectively captures the spatial dependencies among nodes. By incorporating two skip-connections, this module preserves the original node information while aggregating information from other nodes. Finally, a temporal feature extraction module is proposed that captures both continuous and discrete temporal dependencies using a hierarchical memory network. In addition, multi-scale features cascade fusion is incorporated to enhance the performance of the proposed model. To evaluate the effectiveness of the proposed model, extensively experiments are conducted on two real-world datasets. The experimental results demonstrate that the ST-MEN model achieves excellent prediction accuracy, where the maximum improvement can reach to 19.1%. [ABSTRACT FROM AUTHOR]

Accurate traffic prediction is crucial for optimizing taxi demand, managing traffic flow, and planning public transportation routes. Traditional models often fail to capture complex spatial–temporal dependencies. To tackle this, we introduce the Spatio-Temporal Embedding Fusion Transformer (STEFT). This deep learning model leverages attention mechanisms and feature fusion to effectively model dynamic dependencies in traffic data. STEFT includes an Embedding Fusion Network that integrates spatial, temporal, and flow embeddings, preserving original flow information. The Flow Block uses an enhanced Transformer encoder to capture periodic dependencies within neighboring regions, while the Prediction Block forecasts inflow and outflow dynamics using a fully connected network. Experiments on NYC (New York City) Taxi and NYC Bike datasets show STEFT's superior performance over baseline methods in RMSE and MAPE metrics, highlighting the effectiveness of the concatenation-based feature fusion approach. Ablation studies confirm the contribution of each component, underscoring STEFT's potential for real-world traffic prediction and other spatial–temporal challenges. [ABSTRACT FROM AUTHOR]

Traffic prediction is essential for transportation planning, resource allocation, congestion management and enhancing travel experiences. This study optimizes data preprocessing techniques to improve machine learning-based traffic prediction models. Data preprocessing is critical in preparing the data for machine learning models. This study proposes an approach that optimizes data preprocessing techniques, focusing on flow-based analysis and optimization, to enhance traffic prediction models. The proposed approach explores fixed and variable orders of data preprocessing using a genetic algorithm across five diverse datasets. Evaluation metrics such as root mean squared error (RMSE), mean absolute error (MAE) and R-squared error assess model performance. The results indicate that the genetic algorithm’s variable order achieves the best performance for the ArcGIS Hub and Frementon Bridge Cycle datasets, fixed order one preprocessing for the Traffic Prediction dataset and variable order using the genetic algorithm for the PeMS08 dataset. Fixed order 2 preprocessing yields the best performance for the XI AN Traffic dataset. These findings highlight the importance of selecting the appropriate data preprocessing flow order for each dataset, improving traffic prediction accuracy and reliability. The proposed approach advances traffic prediction methodologies, enabling more precise and reliable traffic forecasts for transportation planning and management applications. [ABSTRACT FROM AUTHOR]

In the light of the worsening of, and the adverse effects produced by, global warming, a study of Shanghai's transport carbon emissions can provide an advanced model that can be replicated throughout other cities, thus assisting in the management and reduction of carbon emissions. Considering the volatility and nonlinearity of the carbon emission data series of the transport industry, a prediction model combining complementary ensemble empirical modal decomposition (CEEMD), the improved whale optimization algorithm (IWOA), and the Kernel Extreme Learning Machine (KELM) is proposed for a more accurate prediction of the forecasting of carbon emissions from Shanghai's transport sector. First, nine indicators were screened as the influencing factors of Shanghai's transport carbon emissions through the STIRPAT model, and the corresponding carbon emissions were calculated with data related to Shanghai's transport carbon emissions from 1995 to 2019; Secondly, CEEMD was used to decompose the original data into multiple smooth series and one residual term, and KELM was applied to build a prediction model for each decomposition result, and IWOA was used to optimize the model parameters. The experimental results also demonstrate that CEEMD can effectively reduce model errors. Comparative experiments show that the IWOA algorithm can significantly enhance the stability of machine learning models. The outcomes of various experiments indicate that the CEEMD-IWOA-KELM model produces optimal results with the highest accuracy. Additionally, this model exhibits high stability, as it provides a wider range of methods for predicting carbon emissions and contributing to carbon reduction targets. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Network traffic classification has become more important with the rapid growth of the Internet and online applications. The rapid development of the Internet has enabled explosive growth of various network traffic. The challenge lies in how to classify and identify different categories of network traffic among these huge network traffic. The classification with the massive data network traffic suffers from noise and imbalanced data. Traditional classification algorithms are becoming less effective in handling these issues of the large number of traffic generated by these technologies. This paper proposes an advanced clustering model to enhance network traffic classification and improve the quality of services based on Advanced Density-Based Spatial Clustering of Applications with Noise (A-DBSCAN) with similarity and probability distance. A-DBSCAN with adaptive parameters are applied to identify clusters. The similarity distance is utilized to distinguish between clusters to identify the quality of clusters, where the value of similarity between (-1,1). Moreover, the cluster with a value similarity of more than 0 is identified as a highquality cluster. The probability distance is used to re-evolve the instances of negative clusters to suitable positive clusters. This stage results in consolidated optimal clusters to overcome the problem of imbalances data in the dynamic network efficiently. Additionally, the standard classifiers, such as the Random Forest (RF), K Nearest Neighbours (KNN), Decision Trees (DT), and Naïve Bayes (NB) classifier are utilized to classify data network traffic. Finally, the ISCX VPN-nonVPN dataset remarks as a benchmark to evaluate the proposed solution. The experiment results show that the performance evaluation achieves higher accuracy 81.9% compared to the standard classifiers and related works. [ABSTRACT FROM AUTHOR]

Accurate traffic prediction is crucial for urban planning, especially in rapidly growing cities. Traditional models often struggle to account for sudden traffic pattern changes, such as those caused by the COVID-19 pandemic. Neural networks offer a powerful solution, capturing complex, non-linear relationships in traffic data for more precise prediction. This study aims to create a neural network model for predicting vehicle numbers at main intersections in the city. The model is created using real data from the sensors placed across the city of Zilina, Slovakia. By integrating pandemic-related variables, the model assesses the COVID-19 impact on traffic flow. The model was developed using neural networks, following the data-mining methodology CRISP-DM. Before the modelling, the data underwent thorough preparation, emphasising correcting sensor errors caused by communication failures. The model demonstrated high prediction accuracy, with correlations between predicted and actual values ranging from 0.70 to 0.95 for individual sensors and vehicle types. The results highlighted a significant pandemic impact on urban mobility. The model's adaptability allows for easy retraining for different conditions or cities, making it a robust, adaptable tool for future urban planning and traffic management. It offers valuable insights into pandemic-induced traffic changes and can enhance post-pandemic urban mobility analysis. [ABSTRACT FROM AUTHOR]

Intelligent transportation systems based on sensor signals are crucial in addressing contemporary transportation issues, accomplishing dynamic traffic management, and facilitating route planning. However, the highly dynamic and intricate nature of traffic sensor signals presents difficulties for traffic prediction, with current models for traffic prediction inadequate in meeting the requirements of both long-term and short-term prediction tasks. In this paper, we propose a novel deep-learning framework called dynamic spatial-temporal correlation learning network (DSTLNet) that jointly leverages dynamical spatial and temporal features of traffic sensor signals to further improve the accuracy of long- and short-term traffic modeling and route planning. Specifically, we leverage the temporal convolutional network to capture long-term correlations. In addition, a spatial graph convolutional network is developed to dynamically model spatial features, and long- and short-term fusion layers are used to fuse the extracted long- and short-term temporal features, respectively. Experimental results on real-world datasets show that DSTLNet is competitive with the state-of-the-art, especially for long-term traffic prediction. [ABSTRACT FROM AUTHOR]

Software-defined networking (SDN) revolutionizes network administration by centralizing control and decoupling the data plane from the control plane. Despite its advantages, the escalating volume of network traffic induces congestion at nodes, adversely affecting routing quality and overall performance. Addressing congestion has become imperative due to its emergence as a fundamental challenge in network management. Previous strategies often faced drawbacks in handling congestion, with issues arising from the inability to efficiently manage heavy packet surges in specific network regions. In response, this research introduces a novel approach integrating a multiplicative gated recurrent neural network with a congestion-aware hunter prey optimization (HPO) algorithm for effective traffic management in SDN. The framework leverages machine learning and deep learning techniques, acknowledged for their proficiency in processing traffic data. Comparative simulations showcase the congestion-aware HPO algorithm's superiority, achieving a normalized throughput 3.4–7.6% higher than genetic algorithm (GA) and particle swarm optimization (PSO) alternatives. Notably, the proposed framework significantly reduces data transmission delays by 58–65% compared to the GA and PSO algorithms. This research not only contributes a state-of-the-art solution but also addresses drawbacks observed in existing methodologies, thereby advancing the field of traffic engineering and congestion management in SDN. The proposed framework demonstrates notable enhancements in both throughput and latency, providing a more robust foundation for future SDN implementations. [ABSTRACT FROM AUTHOR]

Traffic prediction is of vital importance in intelligent transportation systems. It enables efficient route planning, congestion avoidance, and reduction of travel time, etc. However, accurate road traffic prediction is challenging due to the complex spatio-temporal dependencies within the traffic network. Establishing and learning spatial dependencies are pivotal for accurate traffic prediction. Unfortunately, many existing methods for capturing spatial dependencies consider only single relationships, disregarding potential temporal and spatial correlations within the traffic network. Moreover, the end-to-end training methods often lack control over the training direction during graph learning. Additionally, existing traffic forecasting methods often fail to integrate multiple traffic data sources effectively, which affects prediction accuracy adversely. In order to capture the spatiotemporal dependencies of the traffic network accurately, a novel traffic prediction framework, Adaptive Spatio-Temporal Graph Neural Network based on Multi-graph Fusion (DTS-AdapSTNet), is proposed. Firstly, in order to better extract the hidden spatial dependencies, a method for fusing multiple factors is designed, which includes the distance relationship, transfer relationship and same-road segment relationship of traffic data. Secondly, an adaptive learning method is proposed, which can control the learning direction of parameters better by the adaptive matrix generation module and traffic prediction module. Thirdly, an improved loss function is designed for training processes and a multi-matrix fusion module is designed to perform weighted fusion of the learned matrices, updating the spatial adjacency matrix continuously, which fuses as much traffic information as possible for more accurate traffic prediction. Finally, experimental results using two large real-world datasets demonstrate that the DTS-AdapSTNet model outperforms other baseline models in terms of mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) when forecasting traffic speed one hour ahead. On average, it achieves reductions of 12.4%, 9.8% and 16.1%, respectively. Moreover, the ablation study validates the effectiveness of the individual modules of DTS-AdapSTNet.