Accurate Traffic Flow Prediction Research Articles

An Incremental Learning Based Convolutional Neural Network Model for Large-Scale and Short-Term Traffic Flow

Traffic flow prediction is very important for smooth road conditions in cities and convenient travel for residents. With the explosive growth of traffic flow data size, traditional machine learning algorithms cannot fit large-scale training data effectively and the deep learning algorithms do not work well because of the huge training and update costs, and the prediction accuracy may need to be further improved when an emergency affecting traffic occurs. In this study, an incremental learning based convolutional neural network model, TF-net, is proposed to achieve the efficient and accurate prediction of large-scale and short-term traffic flow. The key idea is to introduce the uncertainty features into the model without increasing the training cost to improve the prediction accuracy. Meanwhile, based on the idea of combining incremental learning with active learning, a certain percentage of typical samples in historical traffic flow data are sampled to fine-tune the prediction model, so as to further improve the prediction accuracy for special situations and ensure the real-time requirement. The experimental results show that the proposed traffic flow prediction model has better performance than the existing methods.

Intelligent Traffic Flow Prediction Using Optimized GRU Model

Facilitating citizens with accurate traffic flow prediction increases the quality of life. Roadside sensors and devices are used to capture live streams of huge data and the Internet of Things (IoT) is becoming popular for the deployment of effective Intelligent Transportation Systems (ITS). Traffic flow prediction from the live datastreams require building a data-driven model. This is a challenging task and has attracted researchers for better interpretation of the traffic characteristics. The core problem in traffic prediction is modeling a diversity of traffic trends and unpredictable flow variations with temporal dependencies. Initially, statistical and shallow neural network models were applied to some extent. Recently, deep learning has come up with proven and promising outcomes. Gated Recurrent Unit (GRU) is a variation of recurrent neural networks used effectively for traffic flow prediction. Like other deep networks, GRU uses hyperparameters and a sliding window time-steps mechanism to prepare and tune the model. Better tuning for hyperparameters and search for optimal window size is a tedious process. In this research work, we present an algorithm for hyperparameters tuning along with sliding window steps optimization. Results obtained on a real-time public traffic dataset show a higher capability of the proposed method to reduce the error and an average gain of the optimized model over the untuned network is 4.5%. Furthermore, we apply the optimal hyperparameters obtained in the experiment to other deep learning models and present that our approach improves prediction accuracy and stability.

Traffic Flow Prediction Based on Deep Learning in Internet of Vehicles

In Internet of Vehicles (IoV), accurate traffic flow prediction is helpful for analyzing road condition and then timely feedback traffic information to managers as well as travelers. Traditional traffic flow predictions are generally suffering from the performance degradation by over-fitting and manual intervening, which cannot support large-scale and high-dimensional urban road network data. To address this issue, in this paper, a traffic flow prediction framework for urban road network based on deep learning is proposed. Firstly, the feature engineering is introduced to extract the features from a large volume of traffic dataset, with the anomaly nodes eliminated. Next, the big traffic dataset is compressed through the spectral clustering compression scheme. Finally, we designed a hybrid traffic flow prediction scheme based on LSTM (Long Short Term Memory) and Sparse Auto-Encoder (SAE). Experimental results show that our proposed model is superior to other models with an average prediction accuracy approaching 97.7%.

GeoTraPredict: A machine learning system of web spatio-temporal traffic flow

Traffic flow prediction is an important component for self-driving. Traffic flow is closely related to population distribution, and the traffic flow is not only related to the absolute number of human population but also to their concerns and interests. Accurate spatio-temporal web traffic flow prediction is critical in many applications, such as bandwidth allocation, anomaly detection, congestion control and admission control. Most existing traffic flow prediction methods use models based on time-series analysis and remain inadequate for many real-world applications. Web traffic flow is found to be strongly associated with the spatio-temporal distribution of the population. Increasingly, it is critical to understand and make decisions based on the relationship between population patterns and web traffic flow patterns. It has been proven that different people have different responses to web events. Due to the complexity of spatial data structures and the huge volume of web traffic flow log data, it is difficult to routinely find the relationship between web events and population distributions without an appropriate processing framework. In this paper, we propose an innovative framework named GeoTrafficPredict to support the accurate spatio-temporal prediction of web traffic flow. GeoTrafficPredict provides a machine learning platform to learn the spatio-temporal pattern of traffic flow and use the pattern to predict the trend in both spatial and temporal dimension. Also, GeoTrafficPredict provide data aggregation portal and cloud-based computation function. GeoTrafficPredict deploys a series of computational images in a cloud computing environment, and the implementation on China’s CSTNET illustrates the performance of our platform.

A distributed WND-LSTM model on MapReduce for short-term traffic flow prediction

Building data-driven intelligent transportation is a significant task for establishing data-centric smart cities, and exceptionally efficient and accurate traffic flow prediction (TFP) is a crucial technology in constructing intelligent transportation systems (ITSs). To address the computation and storage problems of processing traffic flow big data with the centralized model on a traditional mining platform, we propose a distributed long short-term memory weighted model combined with a time window and normal distribution based on a MapReduce parallel processing framework in this paper, named as WND-LSTM. More specifically, under the Hadoop distributed computing platform, a distributed modeling framework of forecasting traffic flow on MapReduce is developed to solve the existing issues of storage and calculation in handling large-scale traffic flow data with the stand-alone learning model. Moreover, a distributed WND-LSTM model is presented on the MapReduce-based distributed modeling framework to enhance the accuracy, efficiency, and scalability of short-term TFP. Finally, we forecast the traffic flow on the Sanlihe East Road of Beijing in China using the proposed WND-LSTM model with the real-world taxi trajectory big data. In particular, the extensively experimental results from a case study demonstrate that the MAPE value of WND-LSTM is 88.48%, 65.79%, 70.46%, 68.21%, 66.95%, 68.43%, and 70.41% lower than that of the autoregressive integrated moving average (ARIMA), logistical regression (LR), support vector regression (SVR), k-nearest neighbor (KNN), stacked autoencoders (SAEs), gated recurrent unit (GRU), and long short-term memory (LSTM), respectively, and achieves 71.25% accuracy improvement on average.

A combined method for short-term traffic flow prediction based on recurrent neural network

The accurate prediction of real-time traffic flow is indispensable to intelligent transport systems. However, the short-term prediction remains a thorny issue, due to the complexity and stochasticity of the traffic flow. To solve the problem, a combined prediction method for short-term traffic flow based on the autoregressive integral moving average (ARIMA) model and long short-term memory (LSTM) neural network was proposed. The method could make short-term predictions of future traffic flow based on historical traffic data. Firstly, the linear regression feature of the traffic data was captured using the rolling regression ARIMA model; then, backpropagation was used to train the LSTM network to capture the non-linear features of the traffic data; and finally, based on the dynamic weighting of sliding window combined the predicted effects of these two techniques. Using MAE, MSE RMSE and MAPE as evaluation indicators, the prediction performance of the combined method proposed was evaluated on three real highway data sets, and compared with the three comparative baselines of ARIMA and LSTM two single methods and equal weight combination. The experimental results show that the dynamic weighted combination model proposed has better prediction effect, which proves the versatility of this method.

Capsules TCN Network for Urban Computing and Intelligence in Urban Traffic Prediction

Predicting urban traffic is of great importance to smart city systems and public security; however, it is a very challenging task because of several dynamic and complex factors, such as patterns of urban geographical location, weather, seasons, and holidays. To tackle these challenges, we are stimulated by the deep-learning method proposed to unlock the power of knowledge from urban computing and proposed a deep-learning model based on neural network, entitled Capsules TCN Network, to predict the traffic flow in local areas of the city at once. Capsules TCN Network employs a Capsules Network and Temporal Convolutional Network as the basic unit to learn the spatial dependence, time dependence, and external factors of traffic flow prediction. In specific, we consider some particular scenarios that require accurate traffic flow prediction (e.g., smart transportation, business circle analysis, and traffic flow assessment) and propose a GAN-based superresolution reconstruction model. Extensive experiments were conducted based on real-world datasets to demonstrate the superiority of Capsules TCN Network beyond several state-of-the-art methods. Compared with HA, ARIMA, RNN, and LSTM classic methods, respectively, the method proposed in the paper achieved better results in the experimental verification.

Long-Term Traffic Prediction Based on LSTM Encoder-Decoder Architecture

Accurate traffic flow prediction is becoming increasingly important for transportation planning, control, management, and information services of successful. Numerous existing models focus on short-term traffic forecasts, but effective long-term forecasting of traffic flows have become a challenging issue in recent years. To solve this problem, this paper proposes a deep learning architecture which consisting of two parts: the long short-term memory encoder-decoder structure at the bottom and the calibration layer at the top. In the encoder-decoder model, we propose an hard attention mechanism based on learning similar patterns to enhance neuronal memory and reduce the accumulation of error propagation. To correct some of the missing details, we design a control gate in the calibration layer to learn the predicted data in groups according to different forms. The proposed method is evaluated on real-world datasets and compared with other state-of-the-art methods. It is verified that our model can accurately learn local feature and long-term dependence, and has better accuracy and stability in long-term sequence prediction.

Application of Quantum Genetic Optimization of LVQ Neural Network in Smart City Traffic Network Prediction

Accurate prediction of traffic flow in urban networks is of great significance for smart city management. A short-term traffic flow prediction algorithm of Quantum Genetic Algorithm - Learning Vector Quantization (QGA-LVQ) neural network is proposed to forecast the changes of traffic flow. Different from BP neural network, Learning Vector Quantization (LVQ) neural network is of simple structure, easy implementation and better clustering effect. Utilizing the global optimization ability of Quantum Genetic Algorithm (QGA), it is combined with LVQ neural network to overcome some shortcomings of LVQ neural network, including sensitive to initial weights and prone to local minima. In order to test the convergence ability and the timeliness of QGA-LVQ neural network in short-term traffic flow, some contrast experiments are performed. Experimental simulation results show that, QGA-LVQ neural network obtains excellent prediction results in prediction accuracy and convergence speed. Besides, compared with GA-BP neural network and wavelet neural network, QGA-LVQ neural network performs better in short-term traffic flow prediction.

Truck Traffic Flow Prediction Based on LSTM and GRU Methods With Sampled GPS Data

Given the enormous traffic issues, such as congestion and crashes, resulting from the conflicts between trucks and passenger cars, an accurate and reliable prediction of truck traffic flow is needed to enhance the traffic flow efficiency and safety in the mixed traffic condition. Enabled by emerging sensing technologies, the GPS data become available and will reveal some insights to improve the understanding of truck traffic flow prediction. In the paper, a novel method of truck traffic flow prediction is proposed by using sampled GPS data in the roadway network. The proposed method consists of two phases, which are expansion and prediction. In the data expansion phase, a piece-wise constant coefficient method is designed to minimize errors between the sampled truck flow and the actual truck flow, where the coefficients are determined according to road grades and traffic flow size. In the prediction phase, Long Short Term Memory (LSTM) and Gated Recursive Unit (GRU) neural network methods are first time employed to improve the prediction accuracy. Considering that the sequence of the expansion and prediction could have different prediction performance, approaches using both `previous-prediction', `post-expansion' and `previous-expansion', `post-prediction' were used and the results compared with the survey data from traffic flows. The results demonstrate that LSTM and GRU have a superior performance compared to existing approaches using SRV and ARIMA for truck traffic flow prediction. For the whole prediction period, LSTM has better prediction results than GRU overall with an accuracy which is 4.10% better than that of GRU. Furthermore, the accuracy of the `previous-prediction', `post-expansion' is 8.26% greater than that of the `previous-expansion', `post-prediction'.

Predicting Traffic Volume and Occupancy at Failed Detectors

Accurate Traffic flow prediction relies on correctness of the values received from detectors. It is often the case that detectors are not working correctly and provide with incorrect values. The aim of this work is to predict the traffic flow variables at the failed detectors using deep learning techniques such as neural network and autoencoders.The major contributions are using neural network to model the complete network of detectors and use of autoencoders to reduce model size by exploiting spatial correlation between detectors. To the best of our knowledge deep learning has never been applied incase of detector failure.

A Denoising Scheme-Based Traffic Flow Prediction Model: Combination of Ensemble Empirical Mode Decomposition and Fuzzy C-Means Neural Network

In the Intelligent Transportation Systems (ITS), highly accurate traffic flow prediction is considered as key technology to evaluate traffic state of the urban road network. However, due to disturbing from environment, the original traffic flow data may be influenced by noise and finally cause the decline of prediction accuracy. This study design a hybrid prediction model combining Ensemble Empirical Mode Decomposition (EEMD) denoising schemes and classifying learning algorithm based on Fuzzy C-means Neural Network (FCMNN) to improve prediction accuracy. In the model training process, several key parameters in EEMD and FCMNN are determined according to prediction errors based on traffic volume detected from highway network in the Minneapolis city. In the model validation, three widely used indicators for error evaluation are applied to estimate the prediction accuracy of four candidate models under single and multi step, including Artificial Neural Network (ANN), EEMD+ANN, FCMNN and EEMD+FCMNN. The results shown in the case study indicate that the prediction models combined with denoising methods are superior to the models without adopting denoising algorithm. Furthermore, the model using classifying learning method FCMNN can produce higher prediction accuracy than traditional ANN model. In addition, the long-term prediction performance of FCMNN is also much better than that of ANN because that sub-NN system is trained according to each classifying patterns to obtain better optimization effect. Results summarized in this study could be helpful for administration to design managing and controlling strategies according to high prediction accuracy.

An AutoEncoder and LSTM-Based Traffic Flow Prediction Method.

Smart cities can effectively improve the quality of urban life. Intelligent Transportation System (ITS) is an important part of smart cities. The accurate and real-time prediction of traffic flow plays an important role in ITSs. To improve the prediction accuracy, we propose a novel traffic flow prediction method, called AutoEncoder Long Short-Term Memory (AE-LSTM) prediction method. In our method, the AutoEncoder is used to obtain the internal relationship of traffic flow by extracting the characteristics of upstream and downstream traffic flow data. Moreover, the Long Short-Term Memory (LSTM) network utilizes the acquired characteristic data and the historical data to predict complex linear traffic flow data. The experimental results show that the AE-LSTM method had higher prediction accuracy. Specifically, the Mean Relative Error (MRE) of the AE-LSTM was reduced by 0.01 compared with the previous prediction methods. In addition, AE-LSTM method also had good stability. For different stations and different dates, the prediction error and fluctuation of the AE-LSTM method was small. Furthermore, the average MRE of AE-LSTM prediction results was 0.06 for six different days.

Neuro-Fuzzy Modeling of Data Singular Spectrum Decomposition and Traffic Flow Prediction

Dynamic traffic flow prediction is one of the important topics in traffic engineering and intelligent transportation systems. The traffic flow has stochastic nature and nonlinear dynamics which make accurate prediction of traffic flow a challenging process. In most existing methods, the nonlinear and stochastic qualities of traffic flow have not been taken into consideration simultaneously and these methods do not possess satisfactory accuracy either. In this paper, in order to reduce the prediction error based on traffic flow characteristics, we applied two different methods. At first, we applied a locally linear neuro-fuzzy method using local linear model tree learning algorithm for nonlinear identification of traffic flow. Then, to eliminate the noisy components and also to increase the prediction accuracy, we proposed a method, which is the combination of singular spectrum analysis (SSA) and locally linear neuro-fuzzy model (LLNF). In this method, firstly, the principal components of the traffic flow time series were extracted, several noisy and unimportant components were thrown out and then every remained important component was modeled by using a LLNF network, the trained networks were utilized for one-step-ahead prediction, and finally the predicted patterns were combined to construct the general prediction. Moreover, we compared the results of these two methods with each other and also with those of several other intelligent methods such as multi-layer perceptron, radial-basis function, network and adaptive network fuzzy inference system. The simulation results revealed that the proposed SSA + LLNF approach for traffic flow prediction had promising and superior performance.

Traffic-flow prediction via granular computing and stacked autoencoder

Accurate traffic-flow prediction is essential to traffic management. Traffic data collected in very short intervals normally contain high variability, while common preprocessing approaches applied within a window such as simple average or median operator are unable to obtain sufficient latent information from original data. Moreover, the prediction performance of shallow neural network is not satisfying, since its capacity to represent the temporal–spatial correlation of mass traffic data is insufficient, and its adaptation capacity to noisy data is relatively poor. In this paper, fuzzy information granulation (FIG) and deep neural network are combined to solve these two issues. To be specific, FIG is utilized to process original data series and extract granules, which denote the trend and fluctuation range of each time window. Then, stacked autoencoder is combined to obtain the predictive results based on processed granules, especially, a multi-output mechanism is designed to predict all granulation parameters simultaneously, which makes better use of the correlation of diverse inputs. A real-world traffic volume data set is applied to conduct an empirical study, and the experimental results illustrate that based on the proposed method, the interval prediction of the traffic-flow fluctuation range is realized, and superior traffic trend prediction performance is achieved.

Consensus Ensemble System for Traffic Flow Prediction

Traffic flow prediction is a key component of an intelligent transportation system. Accurate traffic flow prediction provides a foundation for other tasks, such as signal coordination and travel time forecasting. There are many known methods in literature for the short-term traffic flow prediction problem, but their efficacy depends heavily on the traffic characteristics. It is difficult, if not impossible, to pick a single method that works well over time. In this paper, we present an automated framework to address this practical issue. Instead of selecting a single method, we combine predictions from multiple methods to generate a consensus traffic flow prediction. We propose an ensemble learning model that exploits the temporal characteristics of the data, and balances the accuracy of individual models and their mutual dependence through a covariance-regularizer. We additionally use a pruning scheme to remove anomalous individual predictions. We apply our proposed model to multi-step-ahead arterial roadway flow prediction. In tests, our method consistently outperforms recently published ensemble prediction methods based on ridge regression and lasso. Our method also produces steady results even when the standalone models and other ensemble methods make wildly exaggerated predictions.

Multi‐model ensemble for short‐term traffic flow prediction under normal and abnormal conditions

Accurate traffic flow prediction under abnormal conditions, such as accidents, adverse weather, work zones, and holidays, is significant for proactive traffic control. Here, the authors focus on a special challenge of how to develop robust responsive algorithms and prediction models for short-term traffic forecasting in different traffic conditions. To this end, this study presents an ensemble learning algorithm for the short-term traffic flow prediction via the integration of gradient boosting regression trees (GBRT) and the least absolute shrinkage and selection operator (Lasso). Four different model structures whether considering the feature selection are proposed and tested for multi-step-ahead prediction under both normal and abnormal conditions. The results indicate that the proposed multi-model ensemble models are superior to the benchmark algorithms, i.e., support vector regression, and random forests, the GBRT model outperforms the Lasso model under normal traffic conditions, and the Lasso model has a better prediction accuracy under abnormal traffic conditions. In addition, the Lasso model with the feature selection is superior to the full feature model under either normal or abnormal conditions, while the GBRT model is not always better under normal conditions. The proposed integration framework is general and flexible to assemble various traffic prediction algorithms.

Accurate Traffic Flow Prediction in Heterogeneous Vehicular Networks in an Intelligent Transport System Using a Supervised Non-Parametric Classifier.

Heterogeneous vehicular networks (HETVNETs) evolve from vehicular ad hoc networks (VANETs), which allow vehicles to always be connected so as to obtain safety services within intelligent transportation systems (ITSs). The services and data provided by HETVNETs should be neither interrupted nor delayed. Therefore, Quality of Service (QoS) improvement of HETVNETs is one of the topics attracting the attention of researchers and the manufacturing community. Several methodologies and frameworks have been devised by researchers to address QoS-prediction service issues. In this paper, to improve QoS, we evaluate various traffic characteristics of HETVNETs and propose a new supervised learning model to capture knowledge on all possible traffic patterns. This model is a refinement of support vector machine (SVM) kernels with a radial basis function (RBF). The proposed model produces better results than SVMs, and outperforms other prediction methods used in a traffic context, as it has lower computational complexity and higher prediction accuracy.

Wavelet‐based short‐term forecasting with improved threshold recognition for urban expressway traffic conditions

Accurate traffic flow prediction can provide reliable and precise information for traffic departments to formulate effective management measures and assist drivers in performing more intelligent route planning and rerouting. The authors propose a short-term traffic flow forecasting framework for urban expressways based on data-driven mixed models including an approach to traffic flow threshold identification based on an improved semi-supervised K -means clustering algorithm, a hybrid multi-scale traffic speed forecasting method based on wavelet decomposition, and a traffic condition index corresponding to three-phase traffic flow theory for reflecting traffic status in real time. Model performance evaluation is performed using multi-source travel speed data. The results show that the traffic threshold recognition algorithm can correctly identify traffic speed thresholds confirming to the three-phase traffic flow transition and that the proposed short-term estimation technique outperforms traditional auto-regressive integrated moving average models, extended Kalman filtering methods, and artificial neural network models in terms of both accuracy and robustness. The proposed traffic condition index using adaptive thresholds and predicted speeds can provide real-time quantitative surveillance for urban expressway traffic.

Efficient and Accurate Traffic Flow Prediction via Incremental Tensor Completion

Timely and accurate prediction of traffic flow plays an important role in improving living quality of the public, which greatly influences the policies and regulations to be enforced and abided by. In this paper, we propose to model urban highway traffic data with an incremental tensor structure to exploit all available feature aspects. It is conceived on the solid basis of dynamic tensor model for traffic prediction, and a fast low-rank tensor completion algorithm, equipped with gravitational search algorithm, is harnessed to optimize the parameters. The proposed method excavates the inner law of traffic flow data by taking account of multi-mode features, such as daily and weekly periodicity, spatial information, and temporal variations, and so on. Empirically, multi-view experiments demonstrate the superiority of Trapit , and indicate that the proposed method is potentially applicable in large and dynamic highway networks.