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学者姓名：方捷
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Abstract ：
Travel time prediction has important influence on the overall control of urban Intelligent Transportation Systems (ITS). Urban arterial networks are typically composed of links and intersections, where each link or intersection can be regarded as a spatial node within the network. However, existing researches predominantly focus on modeling spatial nodes in the link modality to predict travel times in urban arterial networks, neglecting the potential correlations among heterogeneous modal nodes. To overcome these limitations, we propose a Heterogeneous Multi-Modal Graph Neural Network (HMGNN) specifically tailored for travel time prediction in arterial networks. Specifically, we innovatively construct spatial correlation graphs that capture the unique traffic characteristics of intersection modal nodes. Furthermore, we design a cross-modal graph generator that captures the latent spatiotemporal features between spatial nodes of distinct modalities, resulting in the generation of heterogeneous modal graphs. Finally, our proposed HMGNN model incorporates tailored network structures for graphs of varying complexities, enabling targeted mining of their inherent information to derive the final prediction results. Extensive experiments conducted using real-world traffic data from Zhangzhou, China, demonstrate that our HMGNN model achieves significant improvements in prediction accuracy.
Keyword ：
Arterial travel time prediction Arterial travel time prediction Artificial intelligence Artificial intelligence Deep learning Deep learning Heterogeneous modal graph Heterogeneous modal graph Spatiotemporal traffic data Spatiotemporal traffic data
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| GB/T 7714 | Fang, Jie , He, Hangyu , Xu, Mengyun et al. Heterogeneous multi-modal graph network for arterial travel time prediction [J]. | APPLIED INTELLIGENCE , 2025 , 55 (6) . |
| MLA | Fang, Jie et al. "Heterogeneous multi-modal graph network for arterial travel time prediction" . | APPLIED INTELLIGENCE 55 . 6 (2025) . |
| APA | Fang, Jie , He, Hangyu , Xu, Mengyun , Wu, Xiongwei . Heterogeneous multi-modal graph network for arterial travel time prediction . | APPLIED INTELLIGENCE , 2025 , 55 (6) . |
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Inferring the complete traffic flow time-space diagram using vehicle trajectories provides a holistic perspective of traffic dynamics at intersections to traffic managers. However, obtaining all vehicle trajectories on the road is infeasible. To this end, a novel framework that combines the conditional deep generative model and physics-based car-following model is proposed to reconstruct all vehicle trajectories from sparsely available connected vehicle (CV) trajectories at the intersection. The proposed framework has two novel components: Arrival Generative Adversarial Network (Arrival-GAN) and Trajectory-GAN. The Arrival-GAN reproduces stochastic vehicle arrival patterns by considering the interaction between adjacent intersections (e.g., signal control scheme) and the interaction between multiple vehicles from historical vehicle trajectories, circumventing the conventionally adopted unrealistic assumptions of uniform vehicle arrivals. The Trajectory-GAN model takes the baseline trajectory deduced by the physics-based carfollowing model as prior information and refines it by dynamically adapting driving behavior in response to the varying traffic conditions in a data-driven manner. This hybrid approach leverages the advantages of data-driven (i.e., flexibility) and theory-driven approaches (i.e., interpretability) complementarily. The proposed framework outperforms conventional benchmark models in the simulated arterial network and the real-world datasets, reconstructing a complete time-space diagram at intersections with markedly enhanced accuracy, particularly in low-trafficdensity scenarios. This study showcases the potential of utilizing CV data and physics-informed deep learning to improve our understanding of traffic dynamics, empowering traffic managers with novel insights for efficient intersection management.
Keyword ：
Connected vehicle Connected vehicle Generative adversarial networks Generative adversarial networks Physics-informed deep learning Physics-informed deep learning Trajectory reconstruction Trajectory reconstruction
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| GB/T 7714 | Xu, Mengyun , Fang, Jie , Bansal, Prateek et al. Physics informed deep generative model for vehicle trajectory reconstruction at arterial intersections in connected vehicle environment [J]. | TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES , 2025 , 171 . |
| MLA | Xu, Mengyun et al. "Physics informed deep generative model for vehicle trajectory reconstruction at arterial intersections in connected vehicle environment" . | TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 171 (2025) . |
| APA | Xu, Mengyun , Fang, Jie , Bansal, Prateek , Kim, Eui-Jin , Qiu, Tony Z. . Physics informed deep generative model for vehicle trajectory reconstruction at arterial intersections in connected vehicle environment . | TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES , 2025 , 171 . |
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Accurate, real-time, and efficient traffic data, crucial for intelligent transportation systems, which is often disrupted in the real world due to the influence of weather, interference, and equipment failures. Generative Adversarial Networks (GANs), have achieved significant results in image restoration, providing insights for traffic data imputation. Recent research has focused on developing more effective and reasonable model by learning transferable common knowledge from different cities or different scenarios, which is also an excellent idea for improving the data imputation performance. In light of, we contrive to design a GANs based transferable traffic data imputation model, namely Multi Domain Generative Adversarial Transfer Learning Network (MDTGAN). Firstly, the model consists of two stages. In pre-training stage, the model utilizes the source domain datasets (Similar cities road network datasets) to update parameters and transferred them. Then, the model parameters are optimized using the target domain dataset (Target city road network dataset) in fine-tuning stage to avoid the issue of insufficient samples caused by data missing. Secondly, to adapt the model to different dataset topologies, MDTGAN models the node-level data by taking node time series as input, enabling the model to autonomously learn the prevalent spatiotemporal correlations inherent in nodes. Additionally, we introduce a domain discriminator module that guides the spatial encoder in learning domain-invariant features of network node spatial information, enhancing the model generalization ability. The experiments conducted on three publicly available datasets, in which one dataset is regarded as the target dataset, while the others serve as source datasets. The experimental results demonstrate that the MDTGAN model consistently outperforms the baseline models. Specifically, the MDTGAN model can transfer valuable knowledge from the source domain datasets to improve data imputation performance on the target domain dataset, providing practical significance for cities lacking historical traffic data. © 2024
Keyword ：
Domain discriminator Domain discriminator Generative adversarial network Generative adversarial network Traffic data imputation Traffic data imputation Transfer learning Transfer learning
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| GB/T 7714 | Fang, J. , He, H. , Xu, M. et al. MDTGAN: Multi domain generative adversarial transfer learning network for traffic data imputation [J]. | Expert Systems with Applications , 2024 , 255 . |
| MLA | Fang, J. et al. "MDTGAN: Multi domain generative adversarial transfer learning network for traffic data imputation" . | Expert Systems with Applications 255 (2024) . |
| APA | Fang, J. , He, H. , Xu, M. , Chen, H. . MDTGAN: Multi domain generative adversarial transfer learning network for traffic data imputation . | Expert Systems with Applications , 2024 , 255 . |
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Traffic forecasting using deep learning represents a crucial aspect of intelligent transportation systems, carrying substantial implications for congestion reduction and efficient route planning. Despite its significance, accurately predicting traffic states remains a challenge. Existing methodologies focus on capturing the temporal trends of traffic states and the spatial dependencies between roads to enhance prediction accuracy. However, two noteworthy limitations persist in these approaches: (1) Many models neglect the interaction between spatiotemporal features across varying time spans, hindering their ability to utilize traffic state information effectively for predicting future conditions. (2) Genuine correlations between roads are time-varying, making it inadequate to rely on static graphs or static pre-trained node embeddings to model dynamic correlations between roads. To address these challenges, we propose the Multiple Time-Scale Graph Attention Network (MTS-GATN), which comprises two key modules: the Multiple Time-Scale Spatiotemporal Features Extraction Module and the Feature Augmentation Module. The first module involves stacking multiple spatiotemporal extraction layers to discern traffic state information at different time scales. In the second module, we employ dynamic spatial semantic embedding for feature augmentation, providing nodes with dynamic representations over time. Subsequently, we leverage a multi-head spatiotemporal attention mechanism to comprehensively consider location information and real-time semantic data, facilitating the interaction of traffic state information across multiple time scales. Experimental results on two distinct traffic datasets validate the superior performance of MTS-GATN in medium-term and long-term forecasting scenarios.
Keyword ：
Artificial intelligence Artificial intelligence Attention mechanism Attention mechanism Deep learning Deep learning Traffic speed prediction Traffic speed prediction
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| GB/T 7714 | Fang, Jie , Wu, Zhichao , Xu, Mengyun et al. Multistep traffic speed prediction from multiple time-scale spatiotemporal features using graph attention network [J]. | APPLIED INTELLIGENCE , 2024 , 54 (15-16) : 7479-7492 . |
| MLA | Fang, Jie et al. "Multistep traffic speed prediction from multiple time-scale spatiotemporal features using graph attention network" . | APPLIED INTELLIGENCE 54 . 15-16 (2024) : 7479-7492 . |
| APA | Fang, Jie , Wu, Zhichao , Xu, Mengyun , Chen, Hongting . Multistep traffic speed prediction from multiple time-scale spatiotemporal features using graph attention network . | APPLIED INTELLIGENCE , 2024 , 54 (15-16) , 7479-7492 . |
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To estimate the amount of emissions, most state-of-the-art microscopic emission models, such as VT-micro, takes the individual vehicle speed and acceleration as the model input, which can be collected efficiently with V2I technology. However, there is a gap in freeway traffic control since most of them rely on the macroscopic traffic model and omit the individual vehicle status. To fill this gap, this study proposed an individual vehicle status prediction method that utilized the convolutional neural network (CNN) for freeway proactive controls. Then the overall performance of the road network in multi-objective, namely mobility, safety, and emissions, will be evaluated to determine the optimal control signal. The proposed CNN enabled individual vehicle status prediction method reported a good match to the ground truth data compared with the support vector machine and artificial neural network. Furthermore, a field data-based simulation platform was established to implement the proposed control algorithm with the CNN prediction network. The result showed that the multi-objective performance was significantly improved compared with the uncontrolled case and achieved further optimization of multi-objective compared with the original model.
Keyword ：
convolutional neural network convolutional neural network MOPSO MOPSO MPC MPC multi-objective multi-objective Traffic control Traffic control traffic emission traffic emission
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| GB/T 7714 | Fang, Jie , Wang, Juanmeizi , Fu, Lina et al. A macro-microscopic traffic flow data-driven optimal control strategy for freeway [J]. | PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING , 2024 , 239 (2-3) : 502-513 . |
| MLA | Fang, Jie et al. "A macro-microscopic traffic flow data-driven optimal control strategy for freeway" . | PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 239 . 2-3 (2024) : 502-513 . |
| APA | Fang, Jie , Wang, Juanmeizi , Fu, Lina , Lu, Mingwen , Xu, Mengyun . A macro-microscopic traffic flow data-driven optimal control strategy for freeway . | PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING , 2024 , 239 (2-3) , 502-513 . |
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Abstract ：
Accurate, real-time, and efficient traffic data, crucial for intelligent transportation systems, which is often disrupted in the real world due to the influence of weather, interference, and equipment failures. Generative Adversarial Networks (GANs), have achieved significant results in image restoration, providing insights for traffic data imputation. Recent research has focused on developing more effective and reasonable model by learning transferable common knowledge from different cities or different scenarios, which is also an excellent idea for improving the data imputation performance. In light of, we contrive to design a GANs based transferable traffic data imputation model, namely Multi Domain Generative Adversarial Transfer Learning Network (MDTGAN). Firstly, the model consists of two stages. In pre-training stage, the model utilizes the source domain datasets (Similar cities road network datasets) to update parameters and transferred them. Then, the model parameters are optimized using the target domain dataset (Target city road network dataset) in fine-tuning stage to avoid the issue of insufficient samples caused by data missing. Secondly, to adapt the model to different dataset topologies, MDTGAN models the node-level data by taking node time series as input, enabling the model to autonomously learn the prevalent spatiotemporal correlations inherent in nodes. Additionally, we introduce a domain discriminator module that guides the spatial encoder in learning domain-invariant features of network node spatial information, enhancing the model generalization ability. The experiments conducted on three publicly available datasets, in which one dataset is regarded as the target dataset, while the others serve as source datasets. The experimental results demonstrate that the MDTGAN model consistently outperforms the baseline models. Specifically, the MDTGAN model can transfer valuable knowledge from the source domain datasets to improve data imputation performance on the target domain dataset, providing practical significance for cities lacking historical traffic data.
Keyword ：
Domain discriminator Domain discriminator Generative adversarial network Generative adversarial network Traffic data imputation Traffic data imputation Transfer learning Transfer learning
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| GB/T 7714 | Fang, Jie , He, Hangyu , Xu, Mengyun et al. MDTGAN: Multi domain generative adversarial transfer learning network for traffic data imputation [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2024 , 255 . |
| MLA | Fang, Jie et al. "MDTGAN: Multi domain generative adversarial transfer learning network for traffic data imputation" . | EXPERT SYSTEMS WITH APPLICATIONS 255 (2024) . |
| APA | Fang, Jie , He, Hangyu , Xu, Mengyun , Chen, Hongting . MDTGAN: Multi domain generative adversarial transfer learning network for traffic data imputation . | EXPERT SYSTEMS WITH APPLICATIONS , 2024 , 255 . |
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The rapid development of Internet of Vehicles (IoV) data powers various online intelligent transportation applications, such as network travel time reporting. However, the accuracy might be severely compromised due to limited probe vehicle sampling frequency. On that account, this article -proposes a dynamic multigraph model-enabled framework to estimate reliable network travel time, even in low-IoV-frequency arterial corridors. The proposed framework first develops an improved sparse IoV travel time decomposition method. The segment travel time is further divided into the free-flow running time and static and dynamic delays. Second, a dynamic multigraph traffic network model (DMGTN) is developed to aid the proposed decomposition method. The model analyzes complicated spatiotemporal relevance between segments from multiple perspectives: the real-time travel time, congestion level, signal control (which is frequently neglected in previous research), and segment properties. Additionally, two distinct enhanced modules are designed for handing dense and sparse network graphs, respectively. This allows for a more efficient inspection over large-scale intricate arterial networks while maintaining precision. Field implementation is conducted in the downtown area of Zhangzhou, China. Compared to other high-performance baseline models, the designed DMGTN model as well as the proposed decomposition method demonstrate state-of-the-art accuracy and successfully capture travel time variability. The proposed framework better utilizes available IoV data to provide valuable traffic information for commuters and traffic management agencies.
Keyword ：
Computational modeling Computational modeling Correlation Correlation Delays Delays Estimation Estimation Spatiotemporal phenomena Spatiotemporal phenomena Trajectory Trajectory Vehicle dynamics Vehicle dynamics
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| GB/T 7714 | Qiu, Tony Z. , Xu, Mengyun , Fang, Jie . Internet of Vehicles Data-Oriented Arterial Travel Time Estimation Framework With Dynamic Multigraph Model [J]. | IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE , 2023 , 15 (3) : 101-116 . |
| MLA | Qiu, Tony Z. et al. "Internet of Vehicles Data-Oriented Arterial Travel Time Estimation Framework With Dynamic Multigraph Model" . | IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE 15 . 3 (2023) : 101-116 . |
| APA | Qiu, Tony Z. , Xu, Mengyun , Fang, Jie . Internet of Vehicles Data-Oriented Arterial Travel Time Estimation Framework With Dynamic Multigraph Model . | IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE , 2023 , 15 (3) , 101-116 . |
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Forecasting the forthcoming intersection movement-based traffic volume enables adaptive traffic control systems to dynamically respond to the fluctuation of traffic demands. In this paper, a deep-learning based Signal-control Refined Dynamic Traffic Graph (ScR-DTG) Model is proposed for advancing the network-level movement-based traffic volume prediction task. The proposed model attempts to further improve the-state-of-art and practice algorithms in traffic prediction for arterial network adaptive signal control utilizing tradition traffic flow theory boosted deep-learning methodology. For precisely inferencing the movement-based demand at cycle-to-cycle level, the proposed model incorporates spatial graph convolution inferencing layer and temporal inferencing layer to explore both the intricate spatial temporal dependencies, respectively. A signal control refining module is contrived to deduce the controlled movement saturation flow and introduce the essential control inferences, which is of great significance but frequently neglected in the previous researches. Additionally, according to the real-time movement specified travel time, this paper creatively constructs an adjacent graph with dynamic order for more accurately capturing the ever-changing spatial relevancies. Field experiments with multiple signal schemes were conducted in the downtown area of Zhangzhou (China). The promising results demonstrated the state-of-the-art accuracy than other high-performance volume prediction algorithms. Implementing the proposed model enables to obtain accurate movement-based volume predictions, which would assist the traffic manage-ment agencies in adjusting signal timing adaptively and further improve the efficiency of signal intersection.
Keyword ：
Deep learning Deep learning Dynamic order arterial graph Dynamic order arterial graph Movement -based traffic volume prediction Movement -based traffic volume prediction Signal control inferences Signal control inferences
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| GB/T 7714 | Xu, Mengyun , Qiu, Tony Z. , Fang, Jie et al. Signal-control refined dynamic traffic graph model for movement-based arterial network traffic volume prediction [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2023 , 228 . |
| MLA | Xu, Mengyun et al. "Signal-control refined dynamic traffic graph model for movement-based arterial network traffic volume prediction" . | EXPERT SYSTEMS WITH APPLICATIONS 228 (2023) . |
| APA | Xu, Mengyun , Qiu, Tony Z. , Fang, Jie , He, Hangyu , Chen, Hongting . Signal-control refined dynamic traffic graph model for movement-based arterial network traffic volume prediction . | EXPERT SYSTEMS WITH APPLICATIONS , 2023 , 228 . |
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The optimization of intersection signal control can improve traffic efficiency, reduce congestion degree, and improve traffic safety. Aiming at implementing the coordinated adaptive traffic signal control (ATSC) across large-scale arterial network, multi-agent reinforcement learning (MARL) has been widely concerned and lucubrated. Nevertheless, the existing MARL-based ATSC studies suffers from several limitations: (1) While most existing researches focused on the mobility per-formance of controlled corridor, there calls for a methodology that aims at combine multi-objective performance on traffic safety, efficiency, and network coor-dination simultaneously; (2) Most methods ignore the correlations between multiple agents, nor considers the spatial-temporal dependencies among the corelated neighboring intersections due to high communications requirements, which can hardly be achieved in real adaptive coordination control. To overcome the afore-mentioned difficulties, a multi-objective reinforcement learning model (NACRL) for network-wide coordinated signal control is proposed. Firstly, to enforce a co-ordinated network control with safety and efficiency considerations, a reward mechanism inspecting both traffic safety and traffic efficiency indicators was designed to achieve ideal performance in terms of mobility, safety and smooth. Secondly, the proposed NACRL conducted a centralized training-decentralized execution framework, this overcomes the critical limitation of data transmission in the field implementation while explicitly analyzing the traffic state over the entire network instead of examining each isolated intersection. Last but not least, the proposed algorithm utilized the attention mechanism to dynamically capture the sophisticated spatial-temporal dependencies over the complex arterial network, which aids the better coordinated control over multi-agents deployed at the intersections across the corridor. To testify the effeteness of the proposed algorithm, extensive experiments were implemented in both large-scale synthetic traffic grid and real-world arterial network. The experiment demonstrated that the proposed NACRL algorithm outperforms other state-of-the-art baselines with simultaneously improved performance in terms of traffic safety, traffic efficiency and network coordination, as well as improved algorithm convergence and interpretability.
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| GB/T 7714 | Fang, Jie , You, Ya , Xu, Mengyun et al. Multi-Objective Traffic Signal Control Using Network-Wide Agent Coordinated Reinforcement Learning [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2023 , 229 . |
| MLA | Fang, Jie et al. "Multi-Objective Traffic Signal Control Using Network-Wide Agent Coordinated Reinforcement Learning" . | EXPERT SYSTEMS WITH APPLICATIONS 229 (2023) . |
| APA | Fang, Jie , You, Ya , Xu, Mengyun , Wang, Juanmeizi , Cai, Sibin . Multi-Objective Traffic Signal Control Using Network-Wide Agent Coordinated Reinforcement Learning . | EXPERT SYSTEMS WITH APPLICATIONS , 2023 , 229 . |
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The great increase in car ownership has led to the daily recurrence of traffic congestion. Thus, traffic mobility, safety and emission concerns have become the most serious challenges for transportation researchers. To mitigate traffic congestion, a variety of proactive traffic-control strategies, such as ramp metering (RM), have been intensively investigated and deployed. With the aim of improving freeway traffic conditions, RM regulates the on-ramp flows dynamically in response to dynamic road conditions. However, most early RM strategies focus on optimising the traffic from one single aspect. This paper presents an RM control algorithm that predicts and evaluates the RM-controlled future traffic states. The impact of RM control was evaluated using a macroscopic traffic-flow model. The designed RM control algorithm possesses a multi-objective optimisation module, which improves the traffic network from the aspects of mobility, safety and emissions. The designed algorithm is evaluated through simulation and calibrated using field data collected over an 11 km major freeway stretch in Edmonton, Alberta, Canada. The comparison of the proposed algorithm-controlled scenario and the uncontrolled scenario shows that the proposed RM control algorithm can effectively relieve traffic congestion, improve safety and reduce carbon emissions concurrently.
Keyword ：
energy conservation energy conservation risk & probability analysis risk & probability analysis roads & highways roads & highways
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| GB/T 7714 | Xie, Huahui , Tu, Lili , Fang, Jie et al. Proactive highway traffic control with intelligent multi-objective optimisation algorithm [J]. | PROCEEDINGS OF THE INSTITUTION OF CIVIL ENGINEERS-TRANSPORT , 2022 , 175 (2) : 65-75 . |
| MLA | Xie, Huahui et al. "Proactive highway traffic control with intelligent multi-objective optimisation algorithm" . | PROCEEDINGS OF THE INSTITUTION OF CIVIL ENGINEERS-TRANSPORT 175 . 2 (2022) : 65-75 . |
| APA | Xie, Huahui , Tu, Lili , Fang, Jie , Easa, Said M. . Proactive highway traffic control with intelligent multi-objective optimisation algorithm . | PROCEEDINGS OF THE INSTITUTION OF CIVIL ENGINEERS-TRANSPORT , 2022 , 175 (2) , 65-75 . |
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