Traffic Flow Prediction Method Research Articles

Traffic flow prediction for highway vehicle detectors through decomposition and machine learning

ABSTRACT Traffic flow prediction is of significant importance in traffic planning. Currently, traffic flow data are primarily collected through loop detectors. However, factors such as road conditions can affect the accuracy of these data. To address this issue, this paper proposes a traffic flow prediction method based on decomposition and machine learning. The improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) method decomposes the sequence into multiple intrinsic mode functions (IMFs). The complexity of each IMF is calculated using the sample entropy (SE), and then the IMFs are reconstructed. Parameters of the variational mode decomposition (VMD) are optimized using the whale optimization algorithm (WOA) for the secondary decomposition, and predictions are made using gated recurrent units (GRU). Finally, the prediction results are reconstructed to obtain the final prediction values. In the case study section, experiments are conducted using datasets from three detectors to explore different decomposition forms and methods.

A combined short- and medium-term traffic flow prediction method for proactive traffic control at expressway toll stations

Traffic flow prediction can support proactive control strategies to alleviate queues and delays at expressway toll stations. The prediction should be enough accurate and advanced that leaves sufficient preparation time to implement control measures for upcoming congestions. The accuracy of prediction and the lead time for accurate prediction are contradictory. To balance both aspects, a control-oriented combined prediction method was proposed in this study. This method automatically tuned prediction horizons based on predicted level of congestion. It used long short-term memory (LSTM) neural networks to learn the spatial–temporal characteristics of historical traffic flow and predict short-term and medium-term traffic flow at toll stations. Then, the level of congestion was determined for graded congestion warning and dynamic tuning of prediction horizons. To validate the proposed method, the Ganggou toll station was selected as the study site. The proposed integrated model can facilitate reliable proactive control measures at expressway toll stations.

Research on traffic flow prediction method based on adaptive multi-channel graph convolutional neural networks

In order to address the issues of predefined adjacency matrices inadequately representing information in road networks, insufficiently capturing spatial dependencies of traffic networks, and the potential problem of excessive smoothing or neglecting initial node information as the layers of graph convolutional neural networks increase, thus affecting traffic prediction performance, this paper proposes a prediction model based on Adaptive Multi-channel Graph Convolutional Neural Networks (AMGCN). The model utilizes an adaptive adjacency matrix to automatically learn implicit graph structures from data, introduces a mixed skip propagation graph convolutional neural network model, which retains the original node states and selectively acquires outputs of convolutional layers, thus avoiding the loss of node initial states and comprehensively capturing spatial correlations of traffic flow. Finally, the output is fed into Long Short-Term Memory networks to capture temporal correlations. Comparative experiments on two real datasets validate the effectiveness of the proposed model.

A traffic flow prediction method based on constrained dynamic graph convolutional recurrent networks

To enhance the efficiency of predicting future traffic flow trends within a transportation network, we propose a traffic flow prediction approach based on Constrained Dynamic Graph Convolutional Recurrent Network (C-DGCRN). Firstly, we employ matrix linear constraint and low-rank constraint optimization techniques to assist the Dynamic Graph Convolutional Recurrent Network (DGCRN) in extracting relevant internal correlations within the transportation network, leading to the formation of an affinity matrix. Secondly, we utilize the Hadamard product operation to effectively transfer weights from the affinity matrix to DGCRN, further enhancing its predictive accuracy. The experiments conducted an error assessment on the Metro Traffic Los Angeles and Performance Measurement System-Bay Area datasets. The Mean Absolute Error, Root Mean Square Error, and Mean Absolute Percentage Error of C-DGCRN were 2.66, 5.06, and 6.83, respectively. Compared to state-of-the-art methods like Adaptive Graph Convolutional Recurrent Network, C-DGCRN showed a reduction of 7%, 9%, and 11% in these metrics.

A Short-Term Traffic Flow Prediction Method for Airport Group Route Waypoints Based on the Spatiotemporal Features of Traffic Flow

To fully leverage the spatiotemporal dynamic correlations in air traffic flow and enhance the accuracy of traffic flow prediction models, thereby providing a more precise basis for perceiving congestion situations in the air route network, a study was conducted on a traffic flow prediction method based on deep learning considering spatiotemporal factors. A waypoint network topology graph was constructed, and a neural network model called graph convolution and self-attention-based long short-term memory neural network (GC-SALSTM) was proposed. This model utilized waypoint flow and network efficiency loss rate as input features, with graph convolution extracting spatial features from the waypoint network. Additionally, a long short-term memory network based on a self-attention mechanism was used to extract temporal features, achieving accurate prediction of waypoint traffic. An example analysis was performed on a typical busy sector of airports in the Central and Southern China region. The effectiveness of adding the network efficiency loss rate as an input feature to improve the accuracy of critical waypoint traffic prediction was validated. The performance of the proposed model was compared with various typical prediction models. The results indicated that, with the addition of the network efficiency loss rate, the root mean square error (RMSE) for eight waypoints decreased by more than 10%. Compared to the historical average (HA), autoregressive integrated moving average (ARIMA), support vector regression (SVR), long short-term memory (LSTM), and graph convolution network and long short-term memory network (GCN-LSTM) models, the RMSE of the proposed model decreased by 11.78%, 5.55%, 0.29%, 2.53%, and 1.09%, respectively. This suggests that the adopted network efficiency loss rate indicator effectively enhances prediction accuracy, and the constructed model exhibits superior predictive performance in short-term waypoint traffic forecasting compared to other prediction models. It contributes to optimizing flight paths and high-altitude air routes, minimizing flight delays and airborne congestion to the greatest extent, thus enhancing the overall efficiency of the entire aviation system.

Short-term traffic flow prediction at isolated intersections based on parallel multi-task learning

This paper proposes a novel phase-based short-term traffic flow prediction method based on parallel multi-task learning for isolated intersections. Different from traditional short-term traffic flow prediction methods, we take the traffic flow of each phase as the minimum prediction unit, instead of directly utilising the traffic flow of a single lane with large random fluctuations. Meanwhile, we design a novel encoding and decoding structure whereby external influencing factors have been incorporated both into encoding and decoding operations. Furthermore, a fusion strategy is proposed to predict the traffic flow of each phase by integrating the traffic flows of lanes whose right of way are provided by the phase. In the fusion strategy, we develop a parallel multi-task prediction framework whereby a new loss function is defined to improve the prediction accuracy. Finally, the proposed method is tested with the traffic flow data collected from an intersection of South Changsheng Road located in the city of Jiaxing. The findings illustrate that the proposed method can achieve better prediction results at different sampling time scales, compared to the existing short-term traffic flow prediction methods.

Improving Short-Term Traffic Flow Prediction using Grey Relational Analysis for Data Filtering and Stacked LSTM Modeling

Traffic flow prediction is one of the critical measures to alleviate traffic congestion. Currently, traffic flow prediction research has made some achievements, but there are still some deficiencies. In order to solve the problems of low prediction accuracy, poor real-time performance, and high data dimensions. This paper proposes a new traffic flow prediction method that employs Grey Relation Analysis (GRA) to detect the correlation between detection points, remove insignificant or uncorrelated traffic flow data points, and hence reduce the data dimensionality of the prediction model. Multiple Long Short-Term Memory (LSTM) models are then stacked to establish the traffic flow prediction model, considering that traffic flow is affected by multi-dimensional spatiotemporal factors, incorporating vehicle speed, occupancy, and traffic volume as inputs. We conducted experiments on real datasets, and the results showed that our GRA-SLSTM model improved prediction accuracy by 3.6% compared to other models, while reducing model prediction time by 27.33%. The proposed model’s generalization ability is validated by predicting other detection points, which provides significant references for traffic flow prediction research and practical applications.

GMHANN: A Novel Traffic Flow Prediction Method for Transportation Management Based on Spatial-Temporal Graph Modeling

GMHANN: A Novel Traffic Flow Prediction Method for Transportation Management Based on Spatial-Temporal Graph Modeling

Towards Green Innovation in Smart Cities: Leveraging Traffic Flow Prediction with Machine Learning Algorithms for Sustainable Transportation Systems

The emergence of smart cities has presented the prospect of transforming urban transportation systems into more sustainable and environmentally friendly entities. A pivotal facet of achieving this transformation lies in the efficient management of traffic flow. This paper explores the utilization of machine learning techniques for predicting traffic flow and its application in supporting sustainable transportation management strategies in smart cities based on data from the TRAFFIC CENSUS of the Hong Kong Transport Department. By analyzing anticipated traffic conditions, the government can implement proactive measures to alleviate congestion, reduce fuel consumption, minimize emissions, and ultimately improve quality of life for urban residents. This study proposes a way to develop traffic flow prediction methods with different methodologies in machine learning with a comparison with other results. This research aims to highlight the importance of leveraging machine learning technology in traffic flow prediction and its potential impact on sustainable transportation systems for the green innovation paradigm. The findings of this research have practical implications for transportation planners, policymakers, and urban designers. The predictive models demonstrated can support decision-making processes, enabling proactive measures to optimize traffic flow, reduce emissions, and improve the overall sustainability of transportation systems.

Research on the Application of Smart City Super Brain in the Upgrading and Reconstruction of Expressway—Take Chengdu Old Chuan-Zang Road as an Example

This paper explores the smart city super brain traffic flow prediction method. Through monitoring and studying the traffic volume and average speed of related intersections in the morning and evening peak hour and one normal hour, a total of 7413 data are collected, of which 5930 are used as training sets and 1483 are used as test sets. Long short-term memory network (LSTM) method is used to predict the changes of traffic flow at important traffic nodes along old Chuan-Zang Road in Chengdu and the changes of vehicle speed at typical time of one day. The predicted results are highly coincident with the actual data, which proves the accuracy and effectiveness of the model. According to the predicted results, through the simulation analysis of the transformation scheme of important nodes, the traffic operation of the study section before and after the transformation is evaluated. The results show that the average delay of vehicles is reduced by 23.82% compared with that before the transformation, and the pollutant emission and fuel consumption are also significantly reduced. LSTM, with its unique advantages in processing sequence data and solving long-term nonlinear problems, provides algorithms and basis for urban road upgrading.

Learning traffic as videos: Short-term traffic flow prediction using mixed-pointwise convolution and channel attention mechanism

In the construction of intelligent transportation systems, short-term traffic flow prediction is of great significance for the advancement of traffic network management. But due to the presence of many complex factors in both spatial and temporal domains, it remains a complex and challenging task. Existing literature usually employs the convolutional neural network (CNN)-based methods in capturing spatiotemporal correlations. These CNN-based methods often use a single-channel data structure to represent different periodic patterns, which makes the model susceptible to over-parameterization when capturing periodic dependencies and prone to information loss after convolution. To overcome these limitations, this paper presents a hybrid deep learning method for short-term traffic flow prediction. In this method, a video-shaped multi-channel data structure is designed to represent different periodic patterns more efficiently. Next, a new mixed-pointwise convolution is introduced for capturing periodic dependencies without the negative impacts mentioned above. Lastly, an improved channel attention mechanism is proposed to learn channel inter-dependencies with controllable parameter usage. The proposed method is lightweight, yet highly effective. Compared to the state-of-the-art baseline method, it reduces the root mean squared error by up to 6.7% on the PeMSD4 dataset and 13.3% on the PeMSD7 dataset, while also achieving substantial improvement in two additional metrics, exhibiting strong robustness and great scalability across various settings.

Self-Supervised Spatiotemporal Masking Strategy-Based Models for Traffic Flow Forecasting

Traffic flow forecasting is an important function of intelligent transportation systems. With the rise of deep learning, building traffic flow prediction models based on deep neural networks has become a current research hotspot. Most of the current traffic flow prediction methods are designed from the perspective of model architectures, using only the traffic features of future moments as supervision signals to guide the models to learn the spatiotemporal dependence in traffic flow. However, traffic flow data themselves contain rich spatiotemporal features, and it is feasible to obtain additional self-supervised signals from the data to assist the model to further explore the underlying spatiotemporal dependence. Therefore, we propose a self-supervised traffic flow prediction method based on a spatiotemporal masking strategy. A framework consisting of symmetric backbone models with asymmetric task heads were applied to learn both prediction and spatiotemporal context features. Specifically, a spatiotemporal context mask reconstruction task was designed to force the model to reconstruct the masked features via spatiotemporal context information, so as to assist the model to better understand the spatiotemporal contextual associations in the data. In order to avoid the model simply making inferences based on the local smoothness in the data without truly learning the spatiotemporal dependence, we performed a temporal shift operation on the features to be reconstructed. The experimental results showed that the model based on the spatiotemporal context masking strategy achieved an average prediction performance improvement of 1.56% and a maximum of 7.72% for longer prediction horizons of more than 30 min compared with the backbone models.

Research on prediction method of expressway section traffic flow considering anomaly detection probability

AbstractReal‐time and accurate short‐term traffic flow prediction can provide a scientific basis for decision making by travellers and traffic management, and alleviate traffic congestion to a certain extent. The existing traffic flow prediction methods often encounter limitations in real‐time performance and accuracy due to the post‐processing required to rectify anomaly detection results during data pre‐processing. This paper presents a novel traffic flow prediction model, termed the With‐Anomaly Detection Probability (ADP) Attention‐Bidirectional Long‐Short Term Memory (BiLSTM) model. This model takes the probability of anomaly detection into consideration, integrating the anomaly detection outcomes as an inherent parameter into the traffic flow prediction framework. Additionally, the model incorporates an attention mechanism within the long‐short term memory network. Through a comprehensive simulation study utilizing actual measured traffic flow data from the Shanghai‐Chongqing Expressway, the effectiveness of the proposed model is rigorously evaluated. The prediction results show that the model proposed in this paper is a real‐time and accurate short‐time traffic flow prediction model compared with the basic models.

Review on short-term traffic flow prediction methods under big data

The goal of traffic forecast is to predict the related traffic situation in the future according to the historical concept. The predicted angle can be divided into short-term prediction and long-term prediction. This method can be used to solve the increasingly serious urban traffic congestion problem, and researchers have proposed a deep learning model to help decision makers in the field of traffic control. It has made great contributions to improving future road capacity and optimizing intelligent transportation services. In this paper, short-term traffic forecast related documents under big data are helpful after sorting out, and the traffic flow data characteristics of intelligent transportation system and related correction methods are analysed. Secondly, it then classifies the applications involved by big data algorithm calculation according to various related principles, and summarizes the prediction accuracy, computational complexity, applicable interval and computation time of each type of algorithm in an overview. According to the relevant data, the combination forecasting model is effectively diversified, and combined with the related combination forecasting, I hope to improve the forecasting accuracy of the future development prospect.

RL-GCN: Traffic flow prediction based on graph convolution and reinforcement learning for smart cities

The traffic flow problem has become essential in urban planning and management in today’s increasingly urbanized world. Traditional traffic flow prediction models cannot fully consider urban traffic networks’ complex and dynamic characteristics. To this end, this paper proposes a traffic flow prediction method for smart cities (RL-GCN) based on graph convolution, LSTM network and reinforcement learning, aiming to solve the problem of urban traffic flow prediction. Firstly, we use the graph convolutional neural network to process the urban traffic network data features, then use the LSTM network model to learn the temporal information, and then combine the reinforcement learning algorithm to develop the optimal traffic control strategy based on which the future traffic flow is predicted. Our experiments on several datasets show that the model developed in this paper has outstanding performance for urban traffic flow prediction. Compared with the traditional traffic flow prediction methods, the method in this paper has significantly improved prediction accuracy. Our research can provide valuable references and inspiration in urban planning and traffic management.

A Flow Feedback Traffic Prediction Based on Visual Quantified Features

Traffic flow prediction methods commonly rely on historical traffic data, such as traffic volume and speed, but may not be suitable for high-capacity expressways or during peak traffic hours. Furthermore, downstream flow can have significant impacts on traffic flow. To address these challenges, our study proposes a novel traffic flow prediction model, V-STF, which integrates visual methods to quantify macroscopic traffic flow indicators, as well as density features in temporal and flow feedback in spatio features. The contribution of our proposed model lies in its ability to improve prediction accuracy during non-periodic peak hours, by taking into account the impact of congested road conditions on traffic flow. Our experiments using the STREETS dataset demonstrate that V-STF outperforms state-of-the-art methods, especially in predicting sudden changes in traffic flow, resulting in more accurate predictions.

Spatial-temporal Traffic Flow Prediction Model Based on the GAT and BiGRU

Real-time and accurate traffic flow prediction is crucial for improving the safety, stability, and efficiency of intelligent transportation system. Considering that traffic flow prediction methods rarely analyze from the perspective of the road network, in this paper, a spatial-temporal traffic flow prediction model based on the combination of graph attention network (GAT) and bidirectional gated recurrent unit (BiGRU) neural network is proposed. Firstly, GAT is used to analyze the complex topology of the road network, effectively obtaining the spatial features of the road network. Secondly, BiGRU is used to learn the dynamic changes of traffic flow data, effectively obtaining the temporal features. Thirdly, the obtained spatial-temporal features are output by the fully connected layer to complete the prediction of future traffic flow. Finally, the model is validated and evaluated on the California highway dataset. The experimental results show that the accuracy of GAT-BiGRU model is better than other benchmark models in predicting future traffic flows transformation, especially in long-term prediction.

TS-STNN: Spatial-temporal neural network based on tree structure for traffic flow prediction

Traffic flow prediction effectively supports the sustainable expansion and operation of modern transport networks, one of the emerging research areas in intelligent transportation systems. Currently, most common traffic flow prediction methods use deep learning spatial–temporal models based on graph convolution theory, which cannot deeply explore the spatial hierarchy and directional information of traffic flow data due to their structural characteristics. To address this problem, a spatial–temporal neural network based on tree structure (TS-STNN) is created to anticipate future traffic flow at a specific time at a target location. The principle of this method is to use the characteristics of the tree structure to construct a plane tree matrix with hierarchical and directional features, which is finally fused into a spatial tree matrix to extract the spatial information. Meanwhile, the temporal correlation of traffic flow data in the traffic network is analyzed by TS-STNN using Gated Recurrent Units (GRUs). By comparing with the existing baseline methods, it is verified that the TS-STNN model has high prediction accuracy in both Random Uniformly Distributed (RND) and Small-Scale Aggregation of Node Distributed (SSAND) scenarios. It is further demonstrated through ablation experiments that the developed tree convolution module greatly impacts the TS-STNN accuracy.

RSAB-ConvGRU: A hybrid deep-learning method for traffic flow prediction

RSAB-ConvGRU: A hybrid deep-learning method for traffic flow prediction

Spatio-temporal causal graph attention network for traffic flow prediction in intelligent transportation systems.

Accurately predicting traffic flow on roads is crucial to address urban traffic congestion and save on travel time. However, this is a challenging task due to the strong spatial and temporal correlations of traffic data. Existing traffic flow prediction methods based on graph neural networks and recurrent neural networks often overlook the dynamic spatiotemporal dependencies between road nodes and excessively focus on the local spatiotemporal dependencies of traffic flow, thereby failing to effectively model global spatiotemporal dependencies. To overcome these challenges, this article proposes a new Spatio-temporal Causal Graph Attention Network (STCGAT). STCGAT utilizes a node embedding technique that enables the generation of spatial adjacency subgraphs on a per-time-step basis, without requiring any prior geographic information. This obviates the necessity for intricate modeling of constantly changing graph topologies. Additionally, STCGAT introduces a proficient causal temporal correlation module that encompasses node-adaptive learning, graph convolution, as well as local and global causal temporal convolution modules. This module effectively captures both local and global Spatio-temporal dependencies. The proposed STCGAT model is extensively evaluated on traffic datasets. The results show that it outperforms all baseline models consistently.