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A non‐recurrent road traffic anomaly refers to a sudden change in the capacity of a road segment, which deviates from the general traffic patterns, and is usually caused by abnormal traffic events such as traffic accidents and unexpected road maintenance. Timely and accurate detection of non‐recurrent road traffic anomalies facilitates immediate handling to reduce the wastage of resources and the risk of secondary accidents. Compared with other types of traffic anomaly detection methods, prediction algorithms are suitable for detecting non‐recurrent anomalies for their potential ability to distinguish non‐recurrent anomalies from recurrent congestion (e.g., rush hours). A typical prediction algorithm detects an anomaly when the difference between the predicted traffic parameter (i.e., speed) and the actual one is greater than a threshold. However, the subjective setting of thresholds in many prediction algorithms greatly affects the detection performance. This study proposes a novel framework for non‐recurrent road traffic anomaly detection (NRRTAD). The temporal graph convolutional network (T‐GCN) model acts as the predictor to learn the general traffic patterns of road segments by capturing both the topological effects and temporal patterns of traffic flows, and to predict the "normal" traffic speeds. The hierarchical time memory detector (HTM‐detector) algorithm acts as the detector to evaluate the differences between the predicted speeds and the actual speeds to detect non‐recurrent anomalies without setting a threshold. In the experiments with traffic datasets of Beijing, NRRTAD outperformed other methods, not only achieving the highest detection rates but also exhibiting higher resilience to noise. The main advantages of NRRTAD are as follows: (1) adopting the T‐GCN with a weighted graph to integrate differentiated connection strengths of multiple types of topological relations between road segments as well as temporal traffic patterns improves the prediction performance; and (2) utilizing a flexible mechanism in the HTM‐detector to adapt to changing stream data not only avoids subjective setting of a threshold, but also improves the accuracy and robustness of anomaly detection. [ABSTRACT FROM AUTHOR]

Short-term traffic prediction under corrupted or missing data for large-scale transportation networks has become an important and challenging topic in recent decades. Since the critical roads have predictive power on their adjacent roads, this paper proposes a novel hybrid short-term traffic state prediction method based on critical road selection optimization. First, the utility function of the quality of service (QoS) for the critical roads in a large-scale road network is proposed based on the coverage and the data score. Then, the critical road selection optimization model in the transportation networks is presented by selecting an appropriate set of critical roads with the maximum proportion of the total calculation resources to maximize the utility value of the QoS. Also, an innovative critical road selection method is introduced, which is considering the topological structure and the mobility of the urban road network. Subsequently, the traffic speed of the critical roads is regarded as the input of the convolutional long short-term memory neural network to predict the future traffic states of the entire network. Experiment results on the Beijing traffic network indicate that the proposed method outperforms prevailing DL approaches in the case of considering critical road sections. [ABSTRACT FROM AUTHOR]

Accurate traffic speed forecasting is one of the most critical tasks in proactive traffic management and the deployment of advanced traveler information systems. This paper proposes a hybrid forecasting approach named DeepEnsemble by integrating the three‐dimensional convolutional neural network (3D CNN) with ensemble empirical mode decomposition (EEMD). There are four steps in this hybrid approach. First, EEMD is adopted to decompose the complex traffic speed time series data with noise into several intrinsic mode functions (IMFs) and a residue. Second, a three‐dimensional tensor is established and fed into 3D CNN for prediction. Third, the output of 3D CNN prediction is obtained by a linear combination of the results of all components. Finally, the 3D CNN prediction output, external features, and historical features are fused to predict the network‐wide traffic speed simultaneously. The proposed DeepEnsemble approach is tested on the three‐month traffic speed series data of a real‐world large‐scale urban expressway network with 308 traffic flow detectors in Beijing, China. The experimental results indicate that DeepEnsemble outperforms the state‐of‐the‐art network‐wide traffic speed forecasting models. 3D CNN learns temporal, spatial, and depth information better than 2D CNN. Moreover, forecasting accuracy can be improved by employing EEMD. DeepEnsemble is a promising model with scalability and portability for network‐wide traffic speed prediction and can be further extended to conduct traffic status monitoring and congestion mitigation strategies. [ABSTRACT FROM AUTHOR]

The HOV carpooling lane offers a feasible approach to alleviate traffic congestion. The connected vehicle environment is able to provide accurate traffic data, which could optimize the design of HOV carpooling schemes. In this paper, significant tidal traffic flow phenomenon with severe traffic congestion was identified on North Beijing road (bidirectional four-lane) and South Huaihai road (bidirectional six-lane) in Huai'an, Jiangsu Province. The historical traffic data of the road segments were collected through the connected vehicle environment facilities. The purpose of this study is to investigate the effect of adopting two HOV schemes (regular HOV scheme and reversible HOV carpooling scheme) on the urban arterial road under connected vehicle environment. VISSIM was used to simulate the proposed two HOV carpooling schemes at the mentioned road segment. The simulation results showed that the reversible HOV carpooling scheme could not only mitigate the traffic congestion caused by traffic tidal phenomenon but also improve the average speed and traffic volume of the urban arterial road segment, while the regular HOV scheme may exert a negative impact on the average speed and traffic volume on the urban arterial road segment. [ABSTRACT FROM AUTHOR]

• A two-layer deep learning structure based on the LSTM and the GRU was developed to capture the spatio-temporal features of the travel speed of lanes. • Real-world traffic speed data of different lanes in Beijing was used to validate the proposed FDL model. • The proposed FDL model outperforms both the traditional and the state-of-the-art benchmark algorithms, including two deep learning methods. • The predicted speeds of the FDL model are most robust during the 24 h among NN-based benchmark models. Accurate and robust short-term traffic prediction is an important part of advanced traveler information systems. With the development of intelligent navigation and autonomous driving, it is necessary to explore the lane-level predictions of traffic speeds. However, most existing traffic prediction models concentrate on forecasting the traffic flow characteristics of the entire road sections rather than those of certain lanes. This paper proposes a fusion deep learning (FDL) model to predict lane-level traffic speed. First, the entropy-based grey relation analysis is introduced to choose lane sections that are strongly correlated with the lane section to be predicted. Second, a two-layer deep learning framework is established by combining the long short-term memory (LSTM) neural network and the gated recurrent unit (GRU) neural network. Third, the ground-truth data of several lane sections captured by remote traffic microwave sensors (RTMS) on the 2nd Ring Road of Beijing are utilized to examine the FDL model and compare it with several benchmark models. The experimental result indicates that in addition to capturing the fluctuations of traffic speed at the lane level, the FDL model has better performance than the benchmark models in terms of prediction accuracy and stability. [ABSTRACT FROM AUTHOR]

Measures of traffic organization and traffic management in school zones, key protection areas for traffic safety, have been increasingly discussed by government administrations at all levels and researchers as well. Juveniles' trips occur frequently near school areas, especially in primary and secondary school zones. The sensitive topic of these vulnerable road users encountering traffic accidents tends to draw the whole society's attention. In this regard, the paper analyzed different traffic characteristics of 20 primary school zones in Beijing. With the starting point of traffic safety in the area, the paper divided these school zones by type, which was then verified preliminarily reliable and proper, by means of field measurement and driving simulation test, with speed characteristics as the major basis for judgment. By doing this, the authors look forward to providing a theoretical basis for carrying out measures of traffic organization and traffic management. [ABSTRACT FROM AUTHOR]

Short-term traffic forecasting has played a key role in supporting the need of proactive and dynamic traffic control system. K-nearest neighbor (KNN) nonparametric regression models have been widely used in traffic prediction. KNN models give predictions based on the future state of traffic speed that is completely determined by the current state, but with no dependence on the past sequences of traffic speed that produced the current state. In fact, traffic speed is not completely random in nature, and some patterns repeat in the traffic stream. In this paper, we proposed a methodology called bi-pattern recognition KNN model (BKNN) which uses pattern recognition technique twice in the searching process to predict the future traffic state. Then the proposed BKNN model is applied to predict one day real traffic speed series of two sites, which are located near the North 2nd and 3rd Ring Road in Beijing, respectively. With the optimal neighbor and pattern size, the BKNN model provides good predictions. Moreover, in comparison with the KNN model, PKNN model (a modified model based on KNN), seasonal autoregressive integrated moving average (SARIMA) and the artificial neural networks (ANN), the BKNN model appears to be the most promising and robust of the five models to provide better short-term traffic prediction. [ABSTRACT FROM AUTHOR]