Accuracy Analysis of Short-term Traffic Flow Prediction Models for Vehicular Clouds

Short-term traffic flow prediction (STFP) is one of the key technologies in Intelligence Transportation System (ITS). With the development of artificial intelligence technology, deep learning has been employed to STFP and certain achievements have been obtained. However, further improvement of STFP accuracy is constrained by the presence of noise, modal aliasing in the modal components and insufficient feature extraction of neural networks. Therefore, to more accurately capture the changing trend of traffic flow in different time periods, and increase the reliability and interpretability of the prediction models, improved variational mode decomposition (IVMD) and self-attention mechanism (SAM) based hybrid convolutional neural network (CNN) and long short term memory network (LSTM) are proposed for STFP in this study, benefited from the modal decomposition of IVMD and redistribution of the neural weights in CNN-LSTM networks by SAM, the proposed IVMD-CNN-LSTM-SAM effectively suppresses the influence of randomness and volatility of traffic flow, and accurately predict the short-term lane occupancy in the designated area. Furthermore, this study analyses the mechanism of SAM and IVMD in STFP, and proves the superiority of IVMD and SAM in signal decomposition with physical significance and weight allocation respectively. Therefore, the interpretability of the proposed neural network model is significantly improved. Eleven competitive neural networks forecasting methods are used as benchmarks and numerical studies based on actual traffic flow validate the accuracy and stability of the proposed IVMD-CNN-LSTM-SAM. Mean absolute percentage error and goodness of fit of the proposed model are 0.81% and 0.9968, respectively.

Construction of traffic state vector using mutual information for short-term traffic flow prediction

Proactive traffic management is a subset of smart mobility applications in which traffic control strategies are implemented in advance to respond to anticipated roadway conditions. Predicted traffic flows are a key input to proactive traffic control systems, such as proactive freeway ramp metering, proactive variable speed limits, and proactive incident management systems. This paper proposes a machine learning approach for short-term traffic flow prediction where prevailing conditions, such as the traffic volume, speed, and occupancy of roadway segments, are used to predict traffic flow in short-term intervals. Four categories of predictive methods for traffic flow prediction were investigated: deep neural networks, a distributed random forest, a gradient boosting machine, and a generalized linear model. Data from Interstate 64 in St. Louis, Missouri, in the United States were used to calibrate and evaluate the models. The results obtained by the four predictive methods were very similar, with the distributed random forest model slightly outperforming the models obtained by the other three methods. The case study showed that the inclusion of traffic flow, speed, occupancy, and time of day in the traffic prediction process reduces the traffic prediction error. However, the two-sample Kolmogorov-Smirnov test did not show a statistically significant benefit from the inclusion of upstream traffic data in the distributed random forest, and gradient boosting machine models.

Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions

Because of the population increasing so high, and traffic density remaining the same, traffic prediction has become a great challenge today. Creating a higher degree of communication in automobiles results in the time wastage, fuel wastage, environmental damage, and even death caused by citizens being trapped in the middle of traffic. Only a few researchers work in traffic congestion prediction and control systems, but it may provide less accuracy. So, this paper proposed an efficient IoT-based traffic prediction using OWENN algorithm and traffic signal control system using Intel 80,286 microprocessor for a smart city. The proposed system consists of ‘5’ phases, namely IoT data collection, feature extraction, classification, optimized traffic IoT values, and traffic signal control system. Initially, the IoT traffic data are collected from the dataset. After that, traffic, weather, and direction information are extracted, and these extracted features are given as input to the OWENN classifier, which classifies which place has more traffic. Suppose one direction of the place has more traffic, it optimizes the IoT values by using IBSO, and finally, the traffic is controlled by using Intel 80,286 microprocessor. An efficient OWENN algorithm for traffic prediction and traffic signal control using a Intel 80,286 microprocessor for a smart city. After extracting the features, the classification is performed in this step. Hereabout, the classification is done by using the optimized weight Elman neural network (OWENN) algorithm that classifies which places have more traffic. OWENN attains 98.23% accuracy than existing model also its achieved 96.69% F-score than existing model. The experimental results show that the proposed system outperforms state-of-the-art methods.

Short-term traffic speed prediction is a promising research topic in intelligent transportation systems (ITSs), which also plays an important role in the real-time decision-making of traffic control and guidance systems. However, the urban traffic speed has strong temporal, spatial correlation and the characteristic of complex nonlinearity and randomness, which makes it challenging to accurately and efficiently forecast short-term traffic speeds. We investigate the relevant literature and found that although most methods can achieve good prediction performance with the complete sample data, when there is a certain missing rate in the database, it is difficult to maintain accuracy with these methods. Recent studies have shown that deep learning methods, especially long short-term memory (LSTM) models, have good results in short-term traffic flow prediction. Furthermore, the attention mechanism can properly assign weights to distinguish the importance of traffic time sequences, thereby further improving the computational efficiency of the prediction model. Therefore, we propose a framework for short-term traffic speed prediction, including data preprocessing module and short-term traffic prediction module. In the data preprocessing module, the missing traffic data are repaired to provide a complete dataset for subsequent prediction. In the prediction module, a combined deep learning method that is an attention-based LSTM (ATT-LSTM) model for predicting short-term traffic speed on urban roads is proposed. The proposed framework was applied to the urban road network in Nanshan District, Shenzhen, Guangdong Province, China, with a 30-day traffic speed dataset (floating car data) used as the experimental sample. Results show that the proposed method outperforms other deep learning algorithms (such as recurrent neural network (RNN) and convolutional neural network (CNN)) in terms of both calculating efficiency and prediction accuracy. The attention mechanism can significantly reduce the error of the LSTM model (up to 12.4%) and improves the prediction performance.

Accurate selection of spatial-temporal features is key to the short-term traffic flow prediction model outputting higher quality results, which can effectively reduce the difficulty of constructing the prediction model. The spatial-temporal feature selection of most existing short-term traffic flow prediction models mainly relies on empirical knowledge methods and lacks interpretability. The proposed short-term traffic flow prediction network, named STFP-FGFS, utilizes a filter-genetic feature selection method to better explain the results of short-term traffic flow predictions. It consists of three stages: initial generation of temporal-spatial feature set, filtering, and feature optimization, as well as the predicted model. The initial spatial features are generated based on effective travel time, target time granularity, and vehicle type; that is, original spatial features are replaced by standardized spatial features. Four widely used feature selection methods for short-term traffic flow prediction are applied and compared, evaluating three experimental targets and four types of time granularity using four evaluation indexes. The results show that the STFP-FGFS proposed method has overall superior performance, good interpretability, and readability for selected spatial-temporal features.

The increasing complexity of urban transportation systems and the growing volume of vehicles have made traffic congestion a persistent challenge in modern cities. Efficient traffic flow prediction is essential for mitigating congestion, improving road safety, optimizing traffic signal control, and enhancing overall transportation efficiency. In recent years, artificial intelligence (AI) has emerged as a transformative tool in the field of traffic management, offering sophisticated algorithms capable of modeling, analyzing, and predicting complex traffic patterns with high accuracy. The application of AI in traffic flow prediction leverages vast amounts of real-time and historical data to generate precise forecasts, supporting data-driven decision-making by urban planners and traffic control authorities. The prediction of traffic flow involves analyzing time-series data that exhibit nonlinear, dynamic, and often stochastic behavior. Traditional statistical models, such as autoregressive integrated moving average (ARIMA), have proven to be limited in handling the high dimensionality and variability inherent in traffic systems. In contrast, AI algorithms possess the capacity to learn and adapt from complex data inputs without the need for explicit programming, making them particularly suitable for traffic-related applications. AI algorithms used in traffic flow prediction can be broadly categorized into machine learning (ML) and deep learning (DL) approaches. Machine learning algorithms such as k-nearest neighbors (KNN), support vector machines (SVM), decision trees, and random forests have demonstrated effectiveness in short-term traffic prediction tasks. These algorithms are capable of identifying hidden patterns in traffic data and adjusting to changes in traffic behavior over time. Ensemble methods, which combine the strengths of multiple learning models, further enhance prediction accuracy and robustness. Deep learning algorithms, a subfield of AI inspired by the human brain’s neural architecture, have shown exceptional performance in capturing spatial-temporal dependencies in traffic data. Recurrent neural networks (RNNs), particularly long short-term memory (LSTM) networks and gated recurrent units (GRUs), are widely used for their ability to process sequential data and retain information over extended time intervals. Convolutional neural networks (CNNs) are employed to extract spatial features from traffic sensor data or road network imagery. Hybrid models that integrate CNNs with RNNs have achieved high levels of predictive precision by simultaneously learning spatial and temporal correlations. In addition to supervised learning methods, unsupervised and reinforcement learning techniques are also applied in traffic flow prediction. Clustering algorithms, such as k-means and DBSCAN, assist in identifying traffic patterns, while reinforcement learning models optimize adaptive traffic signal control systems by learning optimal actions through environmental interaction. This study explores the different types of AI algorithms used in traffic flow prediction, examining their theoretical foundations, structural differences, and practical applications. It aims to evaluate the comparative advantages of various algorithms in addressing the challenges of real-time traffic prediction in increasingly complex transportation networks.

Short-term traffic flow prediction plays an important role in intelligent transportation system. Numerous researchers have paid much attention to it in the past decades. However, the performance of traditional traffic flow prediction methods is not satisfactory, for those methods cannot describe the complicated nonlinearity and uncertainty of the traffic flow precisely. Neural networks were used to deal with the issues, but most of them failed to capture the deep features of traffic flow and be sensitive enough to the time-aware traffic flow data. In this paper, we propose a deep bi-directional long short-term memory (DBL) model by introducing long short-term memory (LSTM) recurrent neural network, residual connections, deeply hierarchical networks and bi-directional traffic flow. The proposed model is able to capture the deep features of traffic flow and take full advantage of time-aware traffic flow data. Additionally, we introduce the DBL model, regression layer and dropout training method into a traffic flow prediction architecture. We evaluate the prediction architecture on the dataset from Caltrans Performance Measurement System (PeMS). The experiment results demonstrate that the proposed model for short-term traffic flow prediction obtains high accuracy and generalizes well compared with other models.

Traffic-Condition-Awareness Ensemble Learning for Traffic Flow Prediction

With the development of urbanization, the number of residents’ motor vehicles has increased sharply, and traffic congestion problem has become increasingly serious. The construction of Intelligent Traffic System (ITS) has become the main means to alleviate traffic congestion. Short-term traffic flow prediction has guiding significance for residents’ travel planning and intelligent management of transportation, and has become one of the research hotspots in intelligent transportation field.Therefore, A short-term traffic flow prediction method based on the spatio-temporal characteristics of complex road networks is proposed to further improve the prediction accuracy and reduce the prediction cost. Firstly, a graph convolutional network (GCN) capable of processing non-Euclidean data structures is used to extract the spatial characteristics of traffic flow data. Then, the long and short-term memory (LSTM) neural network is used to process the time characteristics. Finally, the two are combined to realize the effective processing of the spatio-temporal characteristics of traffic flow data. Experimental results on the real traffic flow dataset prove the feasibility and effectiveness of the proposed method, and can provide a basis for intelligent traffic control and smart city construction.

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.

In modern intelligent transportation system, traffic control and traffic congestion management are main components of it. Real-time and accurate short-term traffic flow prediction is the precondition and key to realizing traffic control and traffic congestion management. In order to improve the accuracy of short-term traffic flow prediction, a combined model prediction method is proposed in this paper which is based on Xgboost and LightGBM algorithms. First, the short-term traffic flow data is preprocessed and features are sampled. Then, according to different characteristics, different prediction models are constructed by using Xgboost and LightGBM algorithms. Finally, these models are merged to generate the final model. Appling this model for prediction, the average travel time of the road can be obtained to predict the traffic flow and reflect road congestion. Experimental results show that the combined model has higher prediction accuracy than a single model, and it is an effective short-term traffic flow prediction method.

Wang, H. and Yue, P., 2020. Short-term traffic flow prediction of coastal cities based on entropy weight method. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 739–743. Coconut Creek (Florida), ISSN 0749-0208.The traditional short-term traffic flow prediction method has a high prediction error, so a short-term traffic flow prediction method based on entropy weight method is designed. First, identify the lost data, use the weighted average value of historical trend data and measured data to modify, and standardize the original data to collect the short-term traffic flow data of coastal cities. Then, statistical analysis of the original data set, using the minimum maximum standardization method, linear transformation of the original data, and extract the characteristic factors that affect the traffic flow. Finally, based on the correlation of traffic flow at urban intersections, the short-term traffic flow prediction of coastal cities is completed by using entropy weight method. In the experimental part, the prediction errors of the two methods in the period of 7:00-9:00 and 22:00-24:00 are compared. The comparison results show that the prediction errors of the short-term traffic flow prediction method based on entropy weight method in the period of 7:00-9:00 and 22:00-24:00 are small, which proves that the prediction method of this design has practical application significance.

With the successive increase in usage of vehicles, severe traffic congestion is on the rise. This in turn leads to increase in environmental pollution and accidents which ultimately affects the safety, time consumed and money spent of the transport users. The solution to this critical problem is traffic flow prediction depending on which traffic control measures and traffic management can be done. Short term traffic flow prediction has become one of the hot topics of research in intelligent transportation system (ITS). It predicts the traffic flow count in the next time interval that usually varies in the range of five minutes to half an hour. This work presents the application of Support vector machine (SVM) for predicting the traffic flow in a short span. Highway data of midblock segment collected from Chennai, India is used for the analysis. Improved performance measure shows satisfactory results upon application of SVM.