Spatially partitioning heterogeneous traffic networks into multiple subnetworks is crucial for practical tasks, such as distributed signal control and model parallel processing. Existing partitioning methods that account for traffic characteristics over a certain period typically require calculating similarities between the time series of all sensors. Due to the quadratic increase in complexity with network size, these methods are inefficient for large-scale networks and extended time periods. Additionally, calculating similarities requires complete data, making such methods highly sensitive to missing data and lacking robustness. To address these issues, this article proposes a four-step fundamental-diagram-informed traffic network partitioning method. First, spatially adjacent sensors are clustered into subclusters. Next, the S3 speed-occupancy function is used to fit the aggregated data of each subcluster to extract fundamental diagram information. Then, this information is used to perform secondary clustering to form clusters. Finally, the cluster boundaries are fine-tuned to produce subnetworks with smooth boundaries. The proposed method calculates the parameter similarity between subclusters instead of the time series similarity between all sensors. This reduces computational costs and effectively handles data missing. A case study using real-world data verifies the effectiveness of the proposed method and its stability in the presence of missing data. Compared to spectral clustering, the total within-cluster variance and NcutSilhouette metric decrease by 5.7% and 17.8%, respectively. The proposed method enhances distributed or parallel tasks on traffic networks by providing stable and meaningful partitioning results. This method is beneficial for the analysis and effective management of complex heterogeneous traffic networks.

Traffic state prediction, a classical task for traffic management, is a central component of intelligent transport systems to maintain safe and efficient operation. While extensive and intensive research has been conducted on traffic state prediction, most studies have concentrated on enhancing the accuracy of specific traffic state parameters. However, traffic state is a co-evolutionary multivariate time series with various parameters such as flow, velocity, occupancy, etc. At the same time, traffic state data will inevitably be lost during collection. So accurate traffic prediction still faces the following challenges: First, how to deal with the complex missing situations in observational data? Second, how to learn the co-evolutionary relationships between different traffic state parameters while mining the high-dimensional spatio-temporal traffic state patterns? In this paper, we propose a mechanism-data blending-driven co-evolving traffic state parameter prediction method: multi-parameter hybrid tensor deep learning networks (MHT-Net), which consists of a multi-parameter tensor graph convolutional network (MTGCN) and a tensor recurrent neural network (T-RNN). MTGCN implements knowledge embedding of synergistic mechanisms between traffic parameters, ensuring that the road network spatial dependency and the synergistic influence relationship of the parameters can be obtained simultaneously; T-RNN is used to learn high-dimensional temporal features of traffic states. Experiment results on a real-world dataset from Jiangsu province outperform the state-of-the-art baselines, demonstrating the efficacy of the proposed method and providing an effective tool for traffic state prediction with missing values. A mechanism-data blending driven co-evolving traffic state parameter prediction method, multi-parameters hybrid tensor deep learning networks (MHT-Net) is proposed, which implements knowledge embedding of synergistic mechanisms between traffic parameters and learn the road network spatial dependency and the synergistic influence relationship of the parameters simultaneously. Experiment results demonstrate the efficacy of the proposed method and provide an effective tool for traffic state prediction with missing values.

This paper proposes a framework for deep Long Short-Term Memory (D-LSTM) network embedded model predictive control (MPC) for car-following control of connected automated vehicles (CAVs) in traffic mixed with human-driven vehicles (HDVs) and CAVs. The framework consists of: 1) lead HDV trajectory prediction through D-LSTM; and 2) CAV car-following control via MPC based on the predicted vehicle trajectory. For the trajectory prediction, two D-LSTM structures are developed based on the availability of preceding vehicle information: 1) ‘sufficient’ historical information of the position and speed of multiple vehicles ahead; and 2) ‘insufficient’ information where preceding vehicle information is unavailable (e.g., due to failed communication). Based on the prediction, a distributed MPC is designed for each scenario by incorporating the predicted trajectory into state space construction. The proposed D-LSTM models are trained and tested with the NGSIM data for validation. Numerical simulation results for various traffic conditions suggest that the proposed strategies perform better than traditional MPC methods in terms of control objective cost reduction, smoother control, and stabilizing effect. The results also indicate that the sufficient information case outperforms the insufficient information case as expected, which highlights the importance of stable communication.

Most existing cellular probe-based freeway congestion detection methods rely on on-call WLT (Wireless Location Technologies) signal transition data. However, these techniques facing difficulties such as small sample size, frequent road tests, safety, and privacy issues. This article presents a novel approach using the FCA data for traffic congestion detection on freeways. Two cellular activity features, the link pseudo speed and link probe activity, are defined and calculated. A rule-based algorithm is then developed to determine the traffic congestion state. The proposed method has been implemented and a prototype system has been deployed for a major freeway corridor in China. Validated by fixed-point detector data and incident records, the proposed method is able to identify real-time freeway traffic congestion accurately.

Car-following models have been widely applied and made remarkable achievements in traffic engineering. However, the traffic micro-simulation accuracy of car-following models in a platoon level, especially during traffic oscillations, still needs to be enhanced. Rather than using traditional individual car-following models, we proposed a new trajectory generation approach to generate platoon level trajectories given the first leading vehicle’s trajectory. In this article, we discussed the temporal and spatial error propagation issue for the traditional approach by a car following block diagram representation. Based on the analysis, we pointed out that error comes from the training method and the model structure. In order to fix that, we adopt two improvements on the basis of the traditional LSTM-based car-following model. We utilized a scheduled sampling technique during the training process to solve the error propagation in the temporal dimension. Furthermore, we developed a unidirectional interconnected LSTM model structure to extract trajectories features from the perspective of the platoon. As indicated by the systematic empirical experiments, the proposed novel structure could efficiently reduce the temporal-spatial error propagation. Compared with the traditional LSTM-based car-following model, the proposed model has almost 40% less error. The findings will benefit the design and analysis of micro-simulation for platoon-level car-following models.

Understanding the underlying patterns of the urban mobility dynamics is essential for both the traffic state estimation and management of urban facilities and services. Due to the coupling relationship of generative factors in spatial-temporal domain, it is challenging to model the citywide traffic dynamics under a structural pattern of critical features such as hours of days, days of weeks and weather conditions. To address this challenge, this article develops a disentangled representation learning framework to learn an interpretable factorized representation of the independent data generative factors. In order to make full use of the knowledge on generative factors, this article proposes spatial-temporal generative adversarial network (ST-GAN) to assign the generative factors of traffic flow to the feature vector in latent space and reconstructs the high-dimensional citywide traffic flow from the given factors. With the help of the disentangled representations, the decomposed feature vector in latent space discloses the relationship between underlying patterns and citywide traffic dynamics. Several comprehensively experiments show that ST-GAN not only effectively improves the prediction accuracy but also promisingly characterize structural properties of the traffic evolution process.