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Vehicular Ad-Hoc Networks (VANETs) represent a crucial component of intelligent transportation systems (ITS), enabling vehicles to communicate with each other and with roadside infrastructure. Predicting traffic congestion in VANETs is essential for enhancing road safety, optimizing traffic flow, and improving overall transportation efficiency. Traditional machine learning methods have shown promise in this domain; however, they often fall short in handling the complex, high-dimensional data typical of VANETs. To address these challenges, this study employs Extreme Deep Learning Machines (EDRLM), an advanced deep learning technique, for traffic congestion prediction. The EDRLM framework leverages the strengths of deep neural networks and extreme learning machines, offering a robust and scalable solution for processing the dynamic and heterogeneous data in VANETs. By integrating feature extraction, selection, and prediction into a unified model, EDRLM can capture intricate patterns and temporal dependencies within traffic data. The proposed model is trained and validated using realworld VANET datasets, incorporating various traffic parameters such as vehicle speed, density, and inter-vehicular distances. Our experimental results demonstrate that EDRLM outperforms conventional machine learning algorithms in terms of prediction accuracy, computational efficiency, and robustness to noise and missing data. The model's ability to provide timely and precise congestion predictions can facilitate proactive traffic management strategies, including dynamic routing and adaptive traffic signal control, ultimately leading to reduced travel times and enhanced road safety. This study underscores the potential of EDRLM in transforming traffic management in VANETs, paving the way for more intelligent and adaptive ITS solutions. Future research directions include exploring hybrid models combining EDRLM with other advanced machine learning techniques and expanding the framework to accommodate emerging vehicular communication technologies such as 5G and Internet of Things (IoT) devices. [ABSTRACT FROM AUTHOR]

The quality of life and the development of urban areas are impacted by traffic-related issues. The delayed response of priority and emergency vehicles, such as police cars and ambulances, jeopardizes public safety and well-being. Further, repeated episodes of congestion affect driver's temperament by wasting time and causing frustration. Prevailing forecasting techniques are inadequate to address the complexities of urban infrastructure that include autonomous vehicles, connected infrastructure, and integrated public transport. In this article, a new model has been proposed using heuristic methods for real-time traffic management and control applications. The adaptive weighted features are utilized in the atrous convolution-based hybrid attention network for efficient traffic congestion prediction. The features are optimally selected by Mean Square Error of Grass Fibrous Root Optimization (MSE-GFRO) and combined with the optimal weights and thus, are offered the adaptive weighted features. The prediction model combines deep Temporal Convolutional Network (DTCN) and gated recurrent unit (GRU) based on an attention mechanism to predict traffic congestion on the basis of adaptive weighted features. Experimental analysis is performed over distinct optimization models and classifiers to demonstrate the efficiency of the implemented model. [ABSTRACT FROM AUTHOR]

Large and expanding cities suffer from a traffic congestion problem that harms the environment, travelers, and the economy. This paper aims to predict short term traffic congestion on a road section of expressway in Delhi city. For this purpose, we first propose a traffic congestion index based on traffic speed and flow. Clustering techniques and the Greenshield's model were used for the derivation of the congestion index. Using this congestion index, congested time intervals of each day and each location of a weekday were identified. This study also introduces a feature series long short-term memory neural network (FSLSTMNN), which links a long short-term memory (LSTM) layer to each feature. It is trained using the many heterogeneous traffic features data collected in Delhi city for the next five minutes of traffic flow and speed prediction. FSLSTMNN achieved the good capability to learn feature series data. We also trained several traditional and deep-learning models using the same traffic data. The FSLSTMNN reduces mean absolute error 12.90% and 17.13%, respectively, in speed and traffic flow prediction compared to the second good-performance long short-term memory neural network (LSTMNN). Finally, traffic congestion is predicted classwise (light, medium, and congested) using the developed congestion index and traffic speed and flow predicted by the FSLSTMNN. Predicted results are consistent with the measured field data. Study results confirm that the developed congestion index and FSLSTMNN can be used successfully to predict traffic congestion. [ABSTRACT FROM AUTHOR]

Traffic congestion prediction has become an indispensable component of an intelligent transport system. However, one limitation of the existing methods is that they treat the effects of spatio-temporal correlations on traffic prediction as invariable during modeling spatio-temporal features, which results in inadequate modeling. In this paper, we propose an attention-based spatio-temporal 3D residual neural network, named AST3DRNet, to directly forecast the congestion levels of road networks in a city. AST3DRNet combines a 3D residual network and a self-attention mechanism together to efficiently model the spatial and temporal information of traffic congestion data. Specifically, by stacking 3D residual units and 3D convolution, we proposed a 3D convolution module that can simultaneously capture various spatio-temporal correlations. Furthermore, a novel spatio-temporal attention module is proposed to explicitly model the different contributions of spatio-temporal correlations in both spatial and temporal dimensions through the self-attention mechanism. Extensive experiments are conducted on a real-world traffic congestion dataset in Kunming, and the results demonstrate that AST3DRNet outperforms the baselines in short-term (5/10/15 min) traffic congestion predictions with an average accuracy improvement of 59.05%, 64.69%, and 48.22%, respectively. [ABSTRACT FROM AUTHOR]

The continuous growth of urban populations has led to the persistent problem of traffic congestion, which imposes adverse effects on quality of life, such as commute times, road safety, and the local air quality. Advancements in Internet of Things (IoT) sensor technology have contributed to a plethora of new data streams regarding traffic conditions. Therefore, the recognition and prediction of traffic congestion patterns utilizing such data have become crucial. To that end, the integration of Machine Learning (ML) algorithms can further enhance Intelligent Transportation Systems (ITS), contributing to the smart management of transportation systems and effectively tackling traffic congestion in cities. This study seeks to assess a wide range of models as potential solutions for an ML-based multi-step forecasting approach intended to improve traffic congestion prediction, particularly in areas with limited historical data. Various interpretable predictive algorithms, suitable for handling the complexity and spatiotemporal characteristics of urban traffic flow, were tested and eventually shortlisted based on their predictive performance. The forecasting approach selects the optimal model in each step to maximize the accuracy. The findings demonstrate that, in a 24 h step prediction, variating Ensemble Tree-Based (ETB) regressors like the Light Gradient Boosting Machine (LGBM) exhibit superior performances compared to traditional Deep Learning (DL) methods. Our work provides a valuable contribution to short-term traffic congestion predictions and can enable more efficient scheduling of daily urban transportation.

Traffic congestion is a major challenge in modern transportation systems, leading to increased travel time, greater fuel consumption, and higher levels of harmful emissions. Accurate traffic congestion forecasting is crucial for effective traffic control, such as managing traffic lights. To generate reliable predictions, it is crucial to precisely detect abnormal traffic patterns in the data, particularly in densely populated urban areas. This study presents a novel approach that integrates anomaly detection and ensemble learning for traffic congestion forecasting. As an initial step in the learning process, we evaluate different anomaly detection techniques to identify unusual traffic patterns in different locations over time. After predicting the anomaly, the data pattern is cleaned accordingly, and a set of baseline learner models is trained as a secondary learning process. The top-performing models are chosen and undergo an ensemble process to combine their results, evaluating both stacking and voting ensemble methods as a third learning process. We evaluate the efficacy of the proposed strategy by employing a real-world traffic dataset and diverse evaluation metrics. The dataset undergoes several preprocessing techniques, including the windowing process with various settings, to convert the time series data into frequency patterns and produce a more generalized model. The results show that the multilevel learning approach improves prediction accuracy compared to baseline models, highlighting its effectiveness. This study highlights the utilization of anomaly detection and ensemble learning to enhance the precision of traffic congestion prediction, thereby promoting further exploration of this approach in intelligent transportation systems. [ABSTRACT FROM AUTHOR]

Abstract With the rapid development of economy, the sharp increase in the number of urban cars and the backwardness of urban road construction lead to serious traffic congestion of urban roads. Many scholars have tried their best to solve this problem by predicting traffic congestion. Some traditional models such as linear models and nonlinear models have been proved to have a good prediction effect. However, with the increasing complexity of urban traffic network, these models can no longer meet the higher demand of congestion prediction without considering more complex comprehensive factors, such as the spatio-temporal correlation information between roads. In this paper, we propose a traffic congestion index and devise a new traffic congestion prediction model spatio-temporal transformer (STTF) based on transformer, a deep learning model. The model comprehensively considers the traffic speed of road segments, road network structure, the spatio-temporal correlation between road sections and so on. We embed temporal and spatial information into the model through the embedding layer for learning, and use the spatio-temporal attention module to mine the hidden spatio-temporal information within the data to improve the accuracy of traffic congestion prediction. Experimental results based on real-world datasets demonstrate that the proposed model significantly outperforms state-of-the-art approaches.

Traffic congestion is one of the most important issues in large cities, and the overall travel speed is an important factor that reflects the traffic status on road networks. This study proposes a hybrid deep convolutional neural network (CNN) method that uses gradient descent optimization algorithms and pooling operations for predicting the short-term traffic congestion index in urban networks based on probe vehicles. First, the input data are collected by the probe vehicles to calculate the traffic congestion index (output label). Then, a CNN that uses gradient descent optimization algorithms and pooling operations is applied to enhance its performance. Finally, the proposed model is chosen on the basis of the R-squared (R2) and root mean square error (RMSE) values. In the best-case scenario, the proposed model achieved an R2 value of 98.7%. In addition, the experiments showed that the proposed model significantly outperforms other algorithms, namely the ordinary least squares (OLS), k-nearest neighbors (KNN), random forest (RF), recurrent neural network (RNN), artificial neural network (ANN), and convolutional long short-term memory (ConvLSTM), in predicting traffic congestion index. Furthermore, using the proposed method, the time-series changes in the traffic congestion status can be reliably visualized for the entire urban network.

Congestion in large and growing cities is a significant issue that harms the economy, travelers, and the ecosystem. Forecasting the degree of congestion on a road network in advance can help to avoid it and boost the network's capacity and performance. Despite its importance, however, traffic congestion prediction is not a popular topic among researchers and traffic engineers. It's because there aren't enough effective computational traffic forecast techniques or high-quality citywide traffic data. In the existing works, there are several Artificial Intelligence (AI) models are developed for the prediction of traffic congestion in the transport networks. Yet, it facing the problems and challenges relevant to the following parameters: ineffective data handling, prediction error, high time for training and testing operations. Therefore, the proposed work aims to implement a novel AI model for accurately forecasting the traffic congestion in the transportation networks. Here, the Cascaded Transition Recurrent Feature Network (CTRFN) technique is used to extract the best and useful features related to congestion from the traffic dataset. After that, the Paramount Transfer Learning Network (PTLN) model is deployed to predict the traffic congestion based on the extracted features. During this process, the Coherent Lizard Search Optimization (CLSO) algorithm is used to compute the weight factor for constructing the covariance matrix, which supports to obtain the reduced prediction error outputs. Moreover, the congestion prediction performance of the proposed model is validated and compared by using different measures. [ABSTRACT FROM AUTHOR]

Traffic congestion is an important socio-economic problem that swelled in the last few decades. It affects the social mobility of people, length of trips, quality of life, and the economy of countries. As a major problem in most countries, it has been tackled by governments, universities, and advanced research using intelligent transportation systems (ITS) to solve the problem or at least ease its adverse effects. Hidden Markov Models (HMM) represent one of the methods that are suitable for congestion prediction. In this paper, a new model, based on Hidden Markov Model and Contrast, is proposed to define the traffic states during peak hours in two dimensional space (2D). The proposed model uses mean speed and contrast to capture the variability in traffic patterns. Empirical evaluation shows that the proposed approach has improved prediction error in comparison to HMM related work and neuro-fuzzy approaches.

The Internet of Things (IoT) is heading to its mature development and continuously evolving itself as an essential component of the fifth-generation (5G) internet. IoT infrastructure requires networks for which 5G network is developed. Indeed global interest and the advancement in 5G networks has transformed the vision of smart cities into reality in terms of smart homes, smart consumer items, driverless cars, and similar daily appliances. Congestion in traffic affected the areas around the world and causing different issues like fuel wastage, anxiety, and delayed conveyances problems. To locate the exact location of congestion is one of the significant problem in the intelligent transportation system. It perceives the variation from the norm of traffic with the assistance of various sensors utilized in the progression of traffic. So far the development of intelligent transportation system given the capacity to researchers to investigate new procedures for finding the congested zones. This paper proposes the novel model for the prediction of traffic congestion in internet of vehicle using information fusion with artificial neural network and support vector machine. The proposed model obtained 98.4% accuracy, which is greater than previously mentioned approaches in the literature. [ABSTRACT FROM AUTHOR]

A framework for traffic congestion prediction and visualization based on machine learning and Fuzzy Comprehensive Evaluation named MF-TCPV is proposed in this paper. The framework uses DataX and DataV to implement the integration of multi-source heterogeneous traffic data and the visualization of congestion prediction results. A deep prediction model named LSTM-SPRVM based on deep learning algorithms, machine learning algorithms, and Spark parallelization technology for the prediction of traffic congestion features in the future is proposed. In MF-TCPV, traffic congestion is divided into six levels based on Fuzzy Comprehensive Evaluation and traffic congestion features such as average speed, road occupancy rate, and traffic flow density. MF-TCPV is validated based on the real data of Whitemud Drive in Canada. The experimental results demonstrate that MF-TCPV is capable of predicting the traffic congestion accurately and displaying prediction results visually. LSTM-SPRVM is better than other existing deep learning models in terms of prediction accuracy, and MF-TCPV can intuitively visualize the prediction results of traffic congestion.

Traffic flow exhibits different magnitudes of temporal patterns, such as short-term (daily and weekly) and long-term (monthly and yearly). Existing research into road traffic flow prediction has focused on short-term patterns; little research has been done to determine the effect of different long-term patterns on road traffic flow prediction. Providing more temporal contextual information through the use of different temporal data segments could improve prediction results. In this paper, we have investigated different magnitudes of temporal patterns, such as short-term and long-term, through the use of different temporal data segments to understand how contextual temporal data can improve prediction. Furthermore, to learn temporal patterns dynamically, we have proposed a novel online dynamic temporal context neural network framework. The framework uses different temporal data segments as input features, and during online learning, the updating scheme dynamically determines how useful a temporal data segment (short and long-term temporal patterns) is for prediction, and weights it accordingly for use in the regression model. Therefore, the framework can include short-term and relevant long-term patterns in the regression model leading to improved prediction results. We have conducted a thorough experimental evaluation with a real dataset containing daily, weekly, monthly and yearly data segments. The experiment results show that both short and long-term temporal patterns improved prediction accuracy. In addition, the proposed online dynamical framework improved predication results by 10.8% when compared with a deep gated recurrent unit model.

Traffic congestion is an important socio-economic problem that swelled in the last few decades. It affects the social mobility of people, length of trips, quality of life, and the economy of countries. As a major problem in most countries, it has been tackled by governments, universities, and advanced research using intelligent transportation systems (ITS) to solve the problem or at least ease its adverse effects. Hidden Markov Models (HMM) represent one of the methods that are suitable for congestion prediction. In this paper, a new model, based on Hidden Markov Model and Contrast, is proposed to define the traffic states during peak hours in two dimensional space (2D). The proposed model uses mean speed and contrast to capture the variability in traffic patterns. Empirical evaluation shows that the proposed approach has improved prediction error in comparison to HMM related work and neuro-fuzzy approaches. [ABSTRACT FROM AUTHOR]

Until recently, urban cities have faced an increasing demand for an efficient system able to help drivers to discover the congested roads and avoid the long queues. In this paper, an Intelligent Traffic Congestion Prediction System (ITCPS) was developed to predict traffic congestion states in roads. The system embeds a Neural Network architecture able to handle the variation of traffic changes. It takes into account various traffic patterns in urban regions as well as highways during workdays and free-days. The developed system provides drivers with the fastest path and the estimated travel time to reach their destination. The performance of the developed system was tested using a big and real-world Global Positioning System (GPS) database gathered from vehicles circulating in Sfax city urban areas, Tunisia as well as the highways linking Sfax and other Tunisian cities. The results of congestion and travel time prediction provided by our system show promise when compared to other non-parametric techniques. Moreover, our model performs well even in cross-regions whose data were not used during training phase. [ABSTRACT FROM AUTHOR]

Since decades, road traffic congestions have been recognized as an escalating problem in many metropolitan areas worldwide. In addition to causing substantial number of casualties and high pollution rates, these congestions are decelerating economic growth by reducing mobility of people and goods as well as increasing the loss of working hours and fuel consumption. In order to deal with this problem, extensive research works have successively focused on predicting road traffic jams and then predicting their propagations. In spite of their relevance, the proposed solutions to traffic jam propagation have been profoundly dependent on historical data. They have not also used their predictions to intelligently allocate traffic control resources accordingly. We, therefore, propose in this paper a new two-stage traffic resource dispatching solution which is ultimately aiming to implement a self-organizing traffic control system based on Internet of Things. Our solution uses in its first phase a Markov Random Field (MRF) to model and predict the spread of traffic congestions over a road network. According to the obtained predictions, the solution uses Markov Decision Process (MDP) to automatically allocate the road traffic resources. Our simulations are showing satisfactory results in terms of efficient intervention ratios compared to other solutions. [ABSTRACT FROM AUTHOR]

Traffic Congestion is a socio-economic problem that swelled in the past few decades. Intelligent Transportation Systems (ITS) has become the cutting edge solution to most traffic problems. One of the important problems is the prediction of the incoming traffic pattern. There are a number of available approaches for traffic congestion prediction. One approach using NeuroFuzzy is discussed here. The approach is modified into a hybrid one using Hidden Markov Models (HMM). HMM is implemented to take into consideration time factor. It is used to select the right NeuroFuzzy network suitable for this particular time period for efficient congestion prediction. The novelty in this research is: 1) showing that the right choice of traffic pattern for training affects the quality of the prediction dramatically. 2) The results from the hybrid model showing 6% MAE rate which outperforms the standard standalone NeuroFuzzy approach of 15% error. [ABSTRACT FROM AUTHOR]

Traffic problems have seriously affected people’s life quality and urban development, and forecasting short-term traffic congestion is of great importance to both individuals and governments. However, understanding and modeling the traffic conditions can be extremely difficult, and our observations from real traffic data reveal that: 1) similar traffic congestion patterns exist in the neighboring time slots and on consecutive workdays and 2) the levels of traffic congestion have clear multiscale properties. To capture these characteristics, we propose a novel method named PCNN, which is based on a deep convolutional neural network, modeling periodic traffic data for short-term traffic congestion prediction. PCNN has two pivotal procedures: time series folding and multi-grained learning. It first temporally folds the time series and constructs a 2-D matrix as the network input, such that both the real-time traffic conditions and past traffic patterns are well considered; then, with a series of convolutions over the input matrix, it is able to model the local temporal dependency and multiscale traffic patterns. In particular, the global trend of congestion can be addressed at the macroscale, whereas more details and variations of the congestion can be captured at the microscale. Experimental results on a real-world urban traffic data set confirm that folding time series data into a 2-D matrix is effective and PCNN outperforms the baselines significantly for the task of short-term congestion prediction. [ABSTRACT FROM AUTHOR]

This paper presents a method of optimizing the elements of a hierarchy of fuzzy-rule-based systems (FRBSs). It is a hybridization of a genetic algorithm (GA) and the cross-entropy (CE) method, which is here called GACE. It is used to predict congestion in a 9-km-long stretch of the I5 freeway in California, with time horizons of 5, 15, and 30 min. A comparative study of different levels of hybridization in GACE is made. These range from a pure GA to a pure CE, passing through different weights for each of the combined techniques. The results prove that GACE is more accurate than GA or CE alone for predicting short-term traffic congestion. [ABSTRACT FROM PUBLISHER]

Highlights: [•] Data about 87 loop detectors in a real highway during a month is taken. [•] 3 Datasets about congestion in the next 5, 15 and 30min are defined. [•] A codification for hierarchical fuzzy rule-based system is stated. [•] A genetic algorithm to optimize such hierarchical systems is implemented. [•] Results are compared with other classifiers in terms of accuracy and simplicity. [Copyright &y& Elsevier]

Abstract: Traffic congestion prediction plays an important role in route guidance and traffic management. We formulate it as a binary classification problem. Through extensive experiments with real-world data, we found that a large number of sensors, usually over 100, are relevant to the prediction task at one sensor, which means wide area correlation and high dimensionality of the data. This paper investigates the first time into the feature selection problem for traffic congestion prediction. By applying feature selection, the data dimensionality can be reduced remarkably while the performance remains the same. Besides, a new traffic jam probability scoring method is proposed to solve the high-dimensional computation into many one-dimensional probabilities and its combination. [Copyright &y& Elsevier]

The prediction of urban traffic congestion has emerged as one of the most pivotal research topics of intelligent transportation systems (ITSs). Currently, different neural networks have been put forward in the field of traffic congestion prediction and have been put to extensive use. Traditional neural network training takes a long time in addition to easily falling into the local optimal and overfitting. Accordingly, this inhibits the large-scale application of traffic prediction. On the basis of the theory of the extreme learning machine (ELM), the current paper puts forward a symmetric-ELM-cluster (S-ELM-Cluster) fast learning methodology. In this suggested methodology, the complex learning issue of large-scale data is transformed into different issues on small- and medium-scale data sets. Additionally, this methodology makes use of the extreme learning machine algorithm for the purpose of training the subprediction model on each different section of road, followed by establishing a congestion prediction model cluster for all the roads in the city. Together, this methodology fully exploits the benefits associated with the ELM algorithm in terms of accuracy over smaller subsets, high training speed, fewer parameters, and easy parallel acceleration for the realization of high-accuracy and high-efficiency large-scale traffic congestion data learning. [ABSTRACT FROM AUTHOR]

Since speed sensors are not as widely used as GPS devices, the traffic congestion level is predicted based on processed GPS trajectory data in this article. Hidden Markov model is used to match GPS trajectory data to road network and the average speed of road sections can be estimated by adjacent GPS trajectory data. Four deep learning models including convolutional neural network, recurrent neural network, long short-term memory, and gated recurrent unit and three conventional machine learning models including autoregressive integrated moving average model, support vector regression, and ridge regression are used to perform congestion level prediction. According to the experimental results, deep learning models obtain higher accuracy in traffic congestion prediction compared with conventional machine learning models. [ABSTRACT FROM AUTHOR]