Network-Scale Traffic Modeling and Forecasting with Graphical Lasso and Neural Networks

Traffic flow forecasting is an important issue for the application of intelligent transportation systems (ITS). How to improve the traffic flow forecasting precision is a crucial problem. Traffic models in different time sections have great differences. The forecasting precision could be improved if the traffic flow forecasting models were built on different time sections respectively. Traffic flow forecasting usually is real-time and too many forecasting variables will reduce the real-time performance. So the selection of the most informative forecasting variable combination is significant. It can save computation cost and improve forecasting precision. In this paper, information bottleneck theory based on extended entropy is used to partition traffic flow of a day into different time sections. Corresponding to each time section, feature selection based on mutual information is generalized to regression problems and is used to select the most informative variable combination. Selected variables are input to support vector machines (SVM) for traffic flow forecasting. Bayesian inference is used to determine the kernel parameters of SVM. The efficiency of the method is illustrated through analyzing the traffic data of Jinan urban transportation.

Traffic flow prediction plays an important role in intelligent transportation systems and is of great significance in the applications of traffic control and urban planning. Due to the complexity of road traffic flow data, traffic flow prediction has been one of the challenging tasks to fully exploit the spatiotemporal characteristics of roads to improve prediction accuracy. In this study, a combined flow direction level traffic flow prediction graph convolutional network (GCN) and long short-term memory (LSTM) model based on spatiotemporal characteristics is proposed. First, a GCN model is employed to capture the topological structure of the data graph and extract the spatial features of road networks. Additionally, due to the capability to handle long-term dependencies, the long-term memory is used to predict the time series of traffic flow and extract the time features. The proposed model is evaluated using real-world data, which are obtained from the intersection of Liuquan Road and Zhongrun Avenue in the Zibo High-Tech Zone of China. The results show that the developed combined GCN-LSTM flow direction level traffic flow prediction model can perform better than the single models of the LSTM model and GCN model, and the combined ARIMA-LSTM model in traffic flow has a strong spatiotemporal correlation.

Traffic flow forecasting is an essential task of an intelligent transportation system (ITS), closely related to intelligent transportation management and resource scheduling. Dynamic spatial-temporal dependencies in traffic data make traffic flow forecasting to be a challenging task. Most existing research cannot model dynamic spatial and temporal correlations to achieve well-forecasting performance. The multi-head self-attention mechanism is a valuable method to capture dynamic spatial-temporal correlations, and combining it with graph convolutional networks is a promising solution. Therefore, we propose a multi-head self-attention spatiotemporal graph convolutional network (MSASGCN) model. It can effectively capture local correlations and potential global correlations of spatial structures, can handle dynamic evolution of the road network, and, in the time dimension, can effectively capture dynamic temporal correlations. Experiments on two real datasets verify the stability of our proposed model, obtaining a better prediction performance than the baseline algorithms. The correlation metrics get significantly reduced compared with traditional time series prediction methods and deep learning methods without using graph neural networks, according to MAE and RMSE results. Compared with advanced traffic flow forecasting methods, our model also has a performance improvement and a more stable prediction performance. We also discuss some problems and challenges in traffic forecasting.

Accurate short‐term traffic flow modeling is an essential prerequisite to analyze and control traffic flow. Canonical data‐driven methods are a large account of parameters that may be underfitted with limited training samples, yet they cannot adaptively boost their understanding of the spatiotemporal dependencies of the traffic flow. The noisy and unstable traffic flow data also prevent the models from effectively learning the underlying patterns for forecasting future traffic flow. To address these issues, we propose an easy‐to‐implement yet effective boosting model based on extreme gradient boosting and enhance it by wavelet denoising for short‐term traffic flow forecasting. The discrete wavelet denoising is employed to preprocess the noisy traffic flow data. Then, the denoised training datasets are reconstructed to train the extreme gradient boosting model. These two components are integrated seamlessly in a unified framework, and the whole framework can retain the features in the data as much as possible. Our model can precisely capture the hidden spatial dependency in the data. Extensive experiments are conducted on four benchmark datasets compared with frequently used models. The results demonstrate that the proposed model can precisely capture the hidden spatial dependency of the traffic flow data and achieve superior performance.

Accurate traffic flow forecasting is a crucial need for Intelligent Transportation Systems (ITSs). This supports dynamic and proactive traffic control management. The challenging part lies in reducing the forecast error rate. There was limited success in the previous attempts put forth to develop traffic flow forecasting systems. In this work, the MIDAS Site—UK Highways data is used to predict the traffic flow using the hybrid CNN-LSTM model. The data is preprocessed using Z-Score Normalization in order to scale the data below and above the standard deviation. The features from the preprocessed data are learnt using the CNN model and with these extracted features as input, the LSTM network forecasts the traffic flow. The CNN model, LSTM model and hybrid CNN-LSTM model are trained with the preprocessed data. The trained models are tested for estimating the traffic flow. The results convey that the hybrid CNN-LSTM model proposed forecasts the traffic flow with a reduced error rate.KeywordsTraffic flow forecastingConvolutional Neural Network (CNN)Long Short-Term Memory (LSTM)Hybrid CNN-LSTM modelIntelligent transportation system

The forecasting of traffic flow is an important part of intelligent transportation system; actual and accurate forecasting of traffic flow can give scientific support for urban traffic guidance and control. As there is big forecast error when modeling toward traffic flow data with discrete grey model DGM (1, 1), this paper amends the equal interval time sequence. According to the characteristic of time coefficient and backpropagation (BP) neural network, we propose an improved grey model by combining DGM (1, 1) model with BP neural network model. The experimental result indicates that the improved grey model is scientific and effective for the forecasting of traffic flow.

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

One-step vs horizon-step training strategies for multi-step traffic flow forecasting with direct particle swarm optimization grid search support vector regression and long short-term memory

As one of the most widely used applications of the Internet of Things (IoT), intelligent transportation system (ITS) is of great significance for urban traffic planning, traffic control, and traffic guidance. However, widespread traffic congestion occurs with the increased number of vehicles. The traffic flow prediction is a good idea for traffic congestion. Therefore, many schemes have been proposed for accurate and real-time traffic flow prediction, but there still exist many issues, including low accuracy, weak adaptability and inferior real-time. Meanwhile, the complex spatial and temporal dependencies in traffic flow are still challenging. To address the above issues, we propose a novel spatial-temporal Chebyshev graph neural network model (ST-ChebNet) for traffic flow prediction to capture the spatial-temporal features, which can ensure accurate traffic flow prediction. Concretely, we first add a fully connected layer to fuse the features of traffic data into a new feature to generate a matrix, and then the long short-term memory (LSTM) model is adopted to learn traffic state changes for capturing the temporal dependencies. Then, we use the Chebyshev graph neural network (ChebNet) to learn the complex topological structures in the traffic network for capturing the spatial dependencies. Eventually, the spatial features and the temporal features are fused to guarantee the traffic flow prediction. The experiments show that ST-ChebNet can make accurate and real-time traffic flow prediction compared with other eight baseline methods on real-world traffic data sets PeMS.

In metropolitan areas, common traffic issues include traffic congestion, traffic accidents, air pollution, and energy consumption occur. To resolve this issues, Intelligent Transportation Systems (ITS) have been evolved by many researchers. One of the important sub-systems in the development of ITS is a Traffic Management System (TMS) which attempts to reduce a traffic congestion. In fact, TMS itself relies on the estimation of traffic flow, therefore providing such an accurate traffic flow prediction is needed. For this reason, we aim to provide an accurate traffic flow prediction to facilitate this system. In this works, a Supervised Deep Learning Based Traffic Flow Prediction (SDLTFP) was proposed which is a type of fully-connected deep neural network (FC-DNN). Timely prediction is also a major issue in guaranteeing reliable traffic flow prediction. However, training a deep network could be time-consuming, and overfitting is might be happening, especially when feeding small data into the deep architecture. The network is learned perfectly during the training, but in testing with the new data, it could fail to generalize the model. We adopt the Batch Normalization (BN) and Dropout techniques to help the network training. SGD and momentum are carried out to update the weight. We then take advantage of open data as historical traffic data which are then used to predict future traffic flow with the proposed method and model above. Experiments show that the Mean Absolute Percentage Error (MAPE) for our traffic flow prediction is within 5 % using sample data and between 15% to 20% using out of the sample data. Training a deep network faster with BN and Dropout reduces the overfitting.

With the rapid growth of transportation, all kinds of traffic data show explosive growth, and accurate and timely traffic forecasting is also particularly important. Among them, traffic flow prediction is the basis for realizing reasonable traffic guidance and control, and it is also a prerequisite for intelligent transportation. Traffic flow data belongs to a typical chaotic time series with strong non-linearity. In the application of neural network, a two-layer neural network can theoretically approach any continuous function infinitely, so as to achieve very accurate prediction. This paper proposes a predictive traffic flow model based on the combination of GRU network and BP neural network, and uses the US Twin Cities traffic data experiment to verify that the model is feasible in traffic flow prediction. The experimental results show that the combined model has high prediction accuracy and can capture the volatility of traffic flow at rush hour. At the same time, the model has strong robustness.

With the acceleration of urbanization and the continuous growth of transportation demand, traffic flow prediction has become a key research issue in ITS (Intelligent Transportation System). In this paper, a traffic flow prediction model based on VMD-ARO-BiLSTM is proposed, which can make full use of the spatio-temporal characteristics of traffic data to achieve high-precision traffic flow prediction. First, the traffic flow data are input into VMD (Variational Mode Decomposition) for data reconstruction. Then, the optimal parameter values of BiLSTM (Bi-directional Long Short-Term Memory) are solved using ARO (Artificial Rabbits Optimization) to complete the optimization of BiLSTM. Finally, the reconstructed data are input into BiLSTM to realize the accurate prediction of traffic flow. To verify the model performance, we conducted experiments using traffic flow data from different time periods. The experimental results show that on weekdays, the combined VMD-ARO-BiLSTM prediction model decreases the RMSE (Root Mean Square Error) by 47.2% and the MAE (Mean Absolute Error) by 44.6% compared to BiLSTM; on weekends, compared to BiLSTM, the RMSE decreases by 26.1% and MAE decreases by 23.4%, reflecting the strong robustness and applicability of this model, which has certain application value.

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.

Spatial–temporal dependence and similarity aware traffic flow forecasting

Traffic flow prediction accuracy is very important for intelligent transportation systems (ITS). Many studies have proposed different methods for traffic flow prediction including ARIMA, ANN and SVM. With the development of deep learning technology, the evolutionary models of RNN such as LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Units) models have been found to perform well in traffic flow prediction. This paper aims at investigating the use of the Random Forest Regressor Model, an ensemble learning algorithm, for improved and accurate traffic prediction. Random Forest algorithm is highly robust and is well suited for large datasets with many features, thus making it suitable for traffic forecasting. In this research, historical traffic data is used to train the model together with other variables such as traffic flow, weather and time. The Random Forest model performance is compared with the traditional prediction methods using Mean Squared Error. It shows that the Random Forest model is better than the conventional methods and can give better accurate forecasts of traffic flow and can be used in real time traffic management. It presents the actual and predicted vehicle count per hour.