Traffic Speed Prediction Research Articles

Real-Time Dynamic Route Optimization Based on Predictive Control Principle

The principle of predictive control is applied to research on real-time dynamic route optimization of traffic travel, and a real-time dynamic route optimization model based on predictive control is proposed. Taking the driving time as the controlled variable, the driving speed as the manipulated variable, some traffic conditions as disturbance factors, the shortest driving time as the set value, the static shortest route as the reference route of the control system, and the objective function with the shortest driving time, which is defined as the control performance is established. According to the change in the road network state and the optimal solution result of the objective function, the real-time dynamic route selection based on the shortest driving time is realized by switching among different static shortest routes, and the rolling optimization and combination of dynamic and static routes are implemented in the process. A unique method is also used to obtain the optimal solution of the objective function in this study, which is scientific, reasonable, fast, and convenient. The optimization model overcomes the shortcomings of determining the dynamic shortest route by depending on traffic flow prediction and speed prediction. The simulation results and case study prove that the predictive control model algorithm of real-time dynamic route optimization is correct and better. The most important feature of the model algorithm is that it takes the static driving route and desired driving time as the control goals, and it can achieve the global optimal solution of the shortest path and shortest time. The proposed model algorithm has good innovation and practical applications.

Urban Short-Term Traffic Speed Prediction with Complicated Informationfusion on Accidents

Urban Short-Term Traffic Speed Prediction with Complicated Informationfusion on Accidents

Multistep Traffic Speed Prediction from Spatial–Temporal Dependencies Using Graph Neural Networks

Accurate traffic forecasting on citywide networks is one of the crucial urban data mining applications that accurately provide congestion warning and transportation scheduling. While previous work has made significant efforts to learn traffic temporal dynamics and spatial dependencies, two key limitations exist in current models: (1) most existing approaches solely capture spatial correlations among neighbors on predefined graph structure, and genuine relation may be missing due to the incomplete graph connections; and (2) existing methods are defective to capture the temporal trends because the recurrent and stacking structure employed in these methods suffers from the long-range temporal dependency vanquish problem. To overcome the difficulty in multistep prediction and further capture the dynamic spatial–temporal dependencies of traffic flows, we propose a new traffic speed prediction framework for multiscale graph attention networks (MS-GATNs). In particular, MS-GATNs is a hierarchically structured graph neural architecture that learns not only the local region-wise geographical dependencies but also the spatial semantics from a global perspective. Furthermore, a multiheads attention mechanism is introduced to empower our model with the capability of capturing complex nonstationary temporal dynamics. Experiments on real-world traffic data sets demonstrate that MS-GATNs outperforms the state-of-the-art baselines in long-term forecasting.

Traffic Speed Prediction Based on Heterogeneous Graph Attention Residual Time Series Convolutional Networks

Accurate and timely traffic forecasting is an important task for the realization of urban smart traffic. The random occurrence of social events such as traffic accidents will make traffic prediction particularly difficult. At the same time, most of the existing prediction methods rely on prior knowledge to obtain traffic maps and the obtained map structure cannot be guaranteed to be accurate for the current learning task. In addition, traffic data is highly non-linear and long-term dependent, so it is more difficult to achieve accurate prediction. In response to the above problems, this paper proposes a new integrated unified architecture for traffic prediction based on heterogeneous graph attention network combined with residual-time-series convolutional network, which is called HGA-ResTCN. First, the heterogeneous graph attention is used to capture the changes in the relationship between the traffic graph nodes caused by social events, so as to learn the link weights between the target node and its neighbor nodes; at the same time, by introducing the timing of residual links convolutional network to capture the long-term dependence of complex traffic data. These two models are integrated into a unified framework to learn in an end-to-end manner. Through testing on real-world data sets, the results show that the accuracy of the model in this paper is better than other proposed baselines.

Spatial–temporal attention fusion for traffic speed prediction

Accurate vehicle speed prediction is of great significance to the urban traffic intelligent control system. However, in terms of traffic speed prediction, the modules that integrate temporal and spatial features in the existing traffic speed prediction methods are effective in short-term prediction, but the medium-term or long-term prediction errors are relatively large. In order to reduce the errors of existing methods in short-term prediction and predict the medium-term and long-term traffic speed, this paper proposes a traffic speed prediction method that combines attention and Spatial–temporal features, referred to as ASTCN. Specifically, unlike previous methods, ASTCN can use the temporal attention convolutional network (ATCN) to separately extract temporal features from the traffic speed features collected by each sensor, and use the spatial attention mechanism to extract spatial features and then perform spatial–temporal feature fusion. Experiments on three real-world datasets show that the proposed ASTCN model outperforms the state-of-the-art baselines.

Multistep traffic speed prediction: A deep learning based approach using latent space mapping considering spatio-temporal dependencies

Traffic management in a city has become a major problem due to the increasing number of vehicles on roads. Intelligent Transportation System (ITS) can help the city traffic managers to tackle the problem by providing accurate traffic forecasts. For this, ITS requires a reliable traffic prediction algorithm that can provide accurate traffic prediction at multiple time steps based on past and current traffic data. In recent years, a number of different methods for traffic prediction have been proposed which have proved their effectiveness in terms of accuracy. However, most of these methods have either considered spatial information or temporal information only and overlooked the effect of other. In this paper, to address the above problem a deep learning based approach has been developed using both the spatial and temporal dependencies. To consider spatio-temporal dependencies, nearby road sensors at a particular instant are selected based on the attributes like traffic similarity and distance. Two pre-trained deep auto-encoders were cross-connected using the concept of latent space mapping and the resultant model was trained using the traffic data from the selected nearby sensors as input. The proposed deep learning based approach was trained using the real-world traffic data collected from loop detector sensors installed on different highways of Los Angeles and Bay Area. The traffic data is freely available from the web portal of the California Department of Transportation Performance Measurement System (PeMS). The effectiveness of the proposed approach was verified by comparing it with a number of machine/deep learning approaches. It has been found that the proposed approach provides accurate traffic prediction results even for 60-min ahead prediction with least error than other techniques.

An Efficient Short-Term Traffic Speed Prediction Model Based on Improved TCN and GCN.

Timely and accurate traffic speed predictions are an important part of the Intelligent Transportation System (ITS), which provides data support for traffic control and guidance. The speed evolution process is closely related to the topological structure of the road networks and has complex temporal and spatial dependence, in addition to being affected by various external factors. In this study, we propose a new Speed Prediction of Traffic Model Network (SPTMN). The model is largely based on a Temporal Convolution Network (TCN) and a Graph Convolution Network (GCN). The improved TCN is used to complete the extraction of time dimension and local spatial dimension features, and the topological relationship between road nodes is extracted by GCN, to accomplish global spatial dimension feature extraction. Finally, both spatial and temporal features are combined with road parameters to achieve accurate short-term traffic speed predictions. The experimental results show that the SPTMN model obtains the best performance under various road conditions, and compared with eight baseline methods, the prediction error is reduced by at least 8%. Moreover, the SPTMN model has high effectiveness and stability.

Graph Convolutional Network: Traffic Speed Prediction Fused with Traffic Flow Data.

Traffic speed prediction plays an important role in intelligent transportation systems, and many approaches have been proposed over recent decades. In recent years, methods using graph convolutional networks (GCNs) have been more promising, which can extract the spatiality of traffic networks and achieve a better prediction performance than others. However, these methods only use inaccurate historical data of traffic speed to forecast, which decreases the prediction accuracy to a certain degree. Moreover, they ignore the influence of dynamic traffic on spatial relationships and merely consider the static spatial dependency. In this paper, we present a novel graph convolutional network model called FSTGCN to solve these problems, where the model adopts the full convolutional structure and avoids repeated iterations. Specifically, because traffic flow has a mapping relationship with traffic speed and its values are more exact, we fused historical traffic flow data into the forecasting model in order to reduce the prediction error. Meanwhile, we analyzed the covariance relationship of the traffic flow between road segments and designed the dynamic adjacency matrix, which can capture the dynamic spatial correlation of the traffic network. Lastly, we conducted experiments on two real-world datasets and prove that our model can outperform state-of-the-art traffic speed prediction.

Interval prediction of short-term traffic speed with limited data input: Application of fuzzy-grey combined prediction model

Short-term traffic speed prediction, including level and interval prediction, is a key component of proactive traffic control in the intelligent transportation systems (ITS). In particular, predicting intervals may provide traffic managers with more useful information for making reasonable decisions than predicting traffic levels. In this study, a combined model (FIG-GARM) of fuzzy information granulation (FIG) and grey autoregressive model (GARM) is proposed for the prediction interval (PI) of traffic speed. In order to investigate the performance of the FIG-GARM model, using real-world traffic speed data collected from an urban freeway in Edmonton, Canada, and the proposed FIG-GARM model is compared with the interval-grey model first order single variable (GM (1,1)), FIG-GM (1,1), and interval-GARM for PI of traffic speed. The results show that the FIG-GARM model can generate workable PI of the traffic speed, proving the validity of the proposed model. In addition, the PI of traffic speed obtained by FIG-GARM model has higher prediction interval coverage probability (PICP), narrower width interval (WI), and higher index P, which can provide decision support for the robust and accurate prediction of intelligent transportation systems.

Highway Traffic Speed Prediction in Rainy Environment Based on APSO-GRU

In order to accurately analyse the impact of the rainy environment on the characteristics of highway traffic flow, a short-term traffic flow speed prediction model based on gate recurrent unit (GRU) and adaptive nonlinear inertia weight particle swarm optimization (APSO) was proposed. Firstly, the rainfall and highway traffic flow data were cleaned, and then they are matched according to the spatiotemporal relationship. Secondly, through the method of multivariate analysis of variance, the significance of the impact of potential factors on traffic flow speed was explored. Then, a GRU-based traffic flow speed prediction model in rainy environment is proposed, and the actual road sections under different rainfall scenarios were verified. After that, in view of the problem that the prediction accuracy of the GRU model was low in the continuous rainfall scenario, the APSO algorithm was used to optimize the parameters of the GRU network, and the APSO-GRU prediction model was constructed and verifications under the same road section and rain scene were carried out. The results show that the APSO-GRU model has significantly improved prediction stability than the GRU model and can better extract rainfall features during continuous rainfall, with an average prediction accuracy rate of 96.74%.

Multiple dynamic graph based traffic speed prediction method

Traffic speed prediction is a crucial and challenging task for intelligent transportation systems. The prediction task can be accomplished via graph neural networks with structured data, but accurate traffic speed prediction is challenging due to the complexity of traffic systems and the constantly dynamic changing nature. To address these issues, a novel evolution temporal graph convolutional network (ETGCN) model is proposed in this paper. The ETGCN model first fuses multiple graph structures, and utilizes graph convolutional network (GCN) to model spatial correlation. Then, the spatial–temporal dependence and their dynamical changes are learned simultaneously to predict traffic speed on a road network graph. Especially, a similarity-based attention method is proposed to fuse multiple graph adjacency matrices. Then, the gated recurrent unit is combined with GCN to capture spatial–temporal correlations and their changing status, simultaneously. Extensive experiments on two large-scale datasets show that our methods provide more accurate prediction results than the existing state-of-the-art methods in every prediction horizon.

A Novel Approach to Calculate the Spatial-Temporal Correlation for Traffic Flow Based on the Structure of Urban Road Networks and Traffic Dynamic Theory.

Determining the spatial–temporal correlation (STC) between roads can help clarify the operation characteristics of road traffic. Moreover, this correlation affects the utilization quality of traffic data in related research fields. Therefore, it is of significance to provide more reasonable correlation information for other research, such as in traffic speed prediction. Most of the traditional correlation calculation methods for traffic are based on only statistical theory. These methods are simple, but their ability to explain the actual phenomenon is limited due to the lack of consideration of the actual traffic operation characteristics. Therefore, to provide more reasonable correlation information between roads, this paper analysed the influence mechanism of urban traffic based on the traffic dynamic model, and two parameters, traffic complete influence time and traffic correlation strength, were proposed to bring physical meaning to the calculation of STC. Then, an improved calculation model of the STC between different roads considering the adjacency between roads was proposed in this paper. Finally, this paper verified this method against two common traditional methods through different experiments. The verification results show that the calculation method proposed in this paper has better interpretability for the STC between different roads and can better reveal the internal traffic operation characteristics of the road network.

A Key Path-Based Deep Learning Approach for Urban Traffic Speed Prediction

Traffic speed prediction in the urban road network is an important and promising task of intelligent transportation systems. Precise traffic speed prediction can mitigate traffic congestion and improve road network utilization. This task is challenging because of the complexity of the spatiotemporal dependency of traffic data among road network. Existing approaches mainly focus on the whole road network and it may capture much redundant information and lead to high computational cost. In this paper, we propose a Key Path-Based Deep Learning Approach: Path-Based CNN-1D + GRU + CNN-2D (P-CGC), a novel deep learning model for traffic speed prediction. Specifically, we use EST-matching algorithm to match the float car data into the road network. Then we select several key paths and build the model for the loop detectors which are in the same key path. We introduce CNN-1D, GRU to extract the temporal dependency of the data, where CNN-1D is used to fuse the contextual information, and GRU is used to capture the features of the temporal dimension. Then we concatenate the output of all CNN-1D+GRU models and use CNN-2D to capture the spatial dependency of the data. Finally, a fully connected neural network is used to transform features into the prediction. We conduct extensive experiments on Zhangzhou real-world datasets, and the proposed approach achieves a good result.

A Short-term Traffic Speed Prediction Model Based on LSTM Networks

A Short-term Traffic Speed Prediction Model Based on LSTM Networks

Traffic speed prediction: spatiotemporal convolution network based on long-term, short-term and spatial features

Traffic speed prediction: spatiotemporal convolution network based on long-term, short-term and spatial features

Multi-Step Traffic Speed Prediction Based on Ensemble Learning on an Urban Road Network

Short-term traffic speed prediction plays an important role in the field of Intelligent Transportation Systems (ITS). Usually, traffic speed forecasting can be divided into single-step-ahead and multi-step-ahead. Compared with the single-step method, multi-step prediction can provide more future traffic condition to road traffic participants for guidance decision-making. This paper proposes a multi-step traffic speed forecasting by using ensemble learning model with traffic speed detrending algorithm. Firstly, the correlation analysis is conducted to determine the representative features by considering the spatial and temporal characteristics of traffic speed. Then, the traffic speed time series is split into a trend set and a residual set via a detrending algorithm. Thirdly, a multi-step residual prediction with direct strategy is formulated by the ensemble learning model of stacking integrating support vector machine (SVM), CATBOOST, and K-nearest neighbor (KNN). Finally, the forecasting traffic speed can be reached by adding predicted residual part to the trend one. In tests that used field data from Zhongshan, China, the experimental results indicate that the proposed model outperforms the benchmark ones like SVM, CATBOOST, KNN, and BAGGING.

DistTune: Distributed Fine-Grained Adaptive Traffic Speed Prediction for Growing Transportation Networks

Over the past decade, many approaches have been introduced for traffic speed prediction. However, providing fine-grained, accurate, time-efficient, and adaptive traffic speed prediction for a growing transportation network where the size of the network keeps increasing and new traffic detectors are constantly deployed has not been well studied. To address this issue, this paper presents DistTune based on long short-term memory (LSTM) and the Nelder-Mead method. When encountering an unprocessed detector, DistTune decides if it should customize an LSTM model for this detector by comparing the detector with other processed detectors in the normalized traffic speed patterns they have observed. If a similarity is found, DistTune directly shares an existing LSTM model with this detector to achieve time-efficient processing. Otherwise, DistTune customizes an LSTM model for the detector to achieve fine-grained prediction. To make DistTune even more time-efficient, DisTune performs on a cluster of computing nodes in parallel. To achieve adaptive traffic speed prediction, DistTune also provides LSTM re-customization for detectors that suffer from unsatisfactory prediction accuracy due to, for instance, changes in traffic speed patterns. Extensive experiments based on traffic data collected from freeway I5-N in California are conducted to evaluate the performance of DistTune. The results demonstrate that DistTune provides fine-grained, accurate, time-efficient, and adaptive traffic speed prediction for a growing transportation network.

The Impact of the COVID-19 Movement Restrictions on the Road Traffic in the Czech Republic during the State of Emergency

The COVID-19 pandemic crisis has impacted numerous areas of people’s work and free-time activities. This article aims to present the main impacts of the COVID-19 movement restrictions on the road traffic in the Czech Republic, measured during the first epidemic wave, i.e., from 12 March to 17 May 2020. The state of emergency was imposed by the Czech government as a de jure measure for coping with the perceived crisis, although the measure eventually resulted only in a quite liberal de facto form of stay-at-home instruction. Unique country-scale traffic data of the first six months of 2020 from 37,002 km of roads, constituting 66% of all roads in the Czech Republic, were collected and analyzed. For the prediction of the prepandemic traffic conditions and their comparison with the measured values in the period of the state of emergency, a long-term traffic speed prediction ensemble model consisting of case-based reasoning, linear regression, and fallback submodels was used. The authors found out that the COVID-19 movement restrictions had a significant impact on the country-wide traffic. Traffic density was reduced considerably in the first three weeks, and the weekly average traffic speed in all road types increased by up to 21%, expectedly due to less crowded roads. The exception was motorways, where a different trend in traffic was found. In sum, during the first three weeks of the state of emergency, people followed government regulations and restrictions and changed their travel behavior accordingly. However, following this period, the traffic gradually returned to the prepandemic state. This occurred three weeks before the state of emergency was terminated. From a behavioral perspective, this article briefly discusses the possible causes of such discrepancies between de jure and de facto pandemic measures, i.e., the governmental communication strategy related to loosening of movement restrictions, media reality, and certain culture-related traits.

Features injected recurrent neural networks for short-term traffic speed prediction

Accurate traffic speed forecasting is critical in advanced transportation management and traveler route planing. Considering the important influences of spatial–temporal factors and excellent performance of recurrent neural networks (RNNs) in the field of time series analyzing, in this paper, the features injected recurrent neural networks (FI-RNNs) were proposed, which combines sequential time data with contextual factors to mine the potential relationship between traffic state and its context. In this model, a stacked RNN was used to learn the sequence features of traffic data. Meanwhile, a sparse Autoencoder was trained to expand the contextual features, which are high-level coding and abstract representations of contextual factors. Then an merging mechanism which injects contextual features into sequence features was explored to generate fusion features. Finally, the new fused features were fed to the predictor to learn the traffic patterns and predict future speed. Case studies based on two real-world data sets show that the injection of contextual features can greatly improve the accuracy of time series prediction. Comparison with ten frequently used models, including k-nearest neighbor (k-NN), support vector machine (SVM), decision tree (DT), gradient booting decision tree (GBDT), random forest (RF), stacked autoencoder (SAE), and four classic RNNs, also shows the proposed models outperform these state-of-the-art traffic prediction methods in terms of accuracy and stability.

Traffic Speed Prediction Based on Graph Neural Networks for Intelligent Transportation System

Traffic Speed Prediction Based on Graph Neural Networks for Intelligent Transportation System