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教授（高校）

土木工程学院

主要研究方向为智能交通系统，交通流理论，智能交通主动协调控制系统，车联网技术应用等方面。在美国攻读博士期间，参与了多项由美国交通部和威斯康辛州交通厅资助的交通信息化、智能交通控制方面的研究项目；在加拿大阿尔贝塔大学智能交通研究中心进行研究期间，作为联合申请人参与了加拿大交通部重点专项资助（三千万研究经费）的《ACTIVE-AURORA 车联网技术研究实践平台》项目、加拿大国家自然科学基金（NSERC）资助的研究项目三项，以及阿尔贝塔省交通厅资助的多个智能交通控制研究项目。回国后已主持福建省自然科学基金一项，福建省教育厅科技基金一项。所撰写学术论文被国际知名刊物和学术会议发表、录用二十余篇次，其中SCI源国际权威杂志8篇，EI/ISTP 源及国际重要会议15 篇（TRB10 篇），并公开发明专利一项。在多个交通界国际知名杂志担任审稿人，如《Transportation Research Part C》、《Journal of Transportation Engineering(ASCE)》、《Transportation Research Record》、《Journal of Intelligent Transportation System》等。

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Physics informed deep generative model for vehicle trajectory reconstruction at arterial intersections in connected vehicle environment SCIE
期刊论文 | 2025 , 171 | TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
Xu, Mengyun | Fang, Jie | Bansal, Prateek | Kim, Eui-Jin | Qiu, Tony Z. Unfold
Article has an altmetric score of 1
Abstract&Keyword Cite Version(2)

Abstract ：

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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Physics informed deep generative model for vehicle trajectory reconstruction at arterial intersections in connected vehicle environment Scopus
期刊论文 | 2025 , 171 | Transportation Research Part C: Emerging Technologies
Xu, M. | Fang, J. | Bansal, P. | Kim, E.-J. | Qiu, T.Z. Unfold
Physics informed deep generative model for vehicle trajectory reconstruction at arterial intersections in connected vehicle environment EI
期刊论文 | 2025 , 171 | Transportation Research Part C: Emerging Technologies
Xu, Mengyun | Fang, Jie | Bansal, Prateek | Kim, Eui-Jin | Qiu, Tony Z. Unfold
Heterogeneous multi-modal graph network for arterial travel time prediction SCIE
期刊论文 | 2025 , 55 (6) | APPLIED INTELLIGENCE
Fang, Jie | He, Hangyu | Xu, Mengyun | Wu, Xiongwei Unfold
Abstract&Keyword Cite Version(3)

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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Heterogeneous multi-modal graph network for arterial travel time prediction
期刊论文 | 2025 , 55 (6) | Applied Intelligence
Jie Fang | Hangyu He | Mengyun Xu | Xiongwei Wu Unfold
Heterogeneous multi-modal graph network for arterial travel time prediction Scopus
期刊论文 | 2025 , 55 (6) | Applied Intelligence
Fang, J. | He, H. | Xu, M. | Wu, X. Unfold
Heterogeneous multi-modal graph network for arterial travel time prediction EI
期刊论文 | 2025 , 55 (6) | Applied Intelligence
Fang, Jie | He, Hangyu | Xu, Mengyun | Wu, Xiongwei Unfold
A macro-microscopic traffic flow data-driven optimal control strategy for freeway SCIE
期刊论文 | 2024 , 239 (2-3) , 502-513 | PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING
Fang, Jie | Wang, Juanmeizi | Fu, Lina | Lu, Mingwen | Xu, Mengyun Unfold
Abstract&Keyword Cite Version(3)

Abstract ：

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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A macro-microscopic traffic flow data-driven optimal control strategy for freeway Scopus
期刊论文 | 2024 , 239 (2-3) , 502-513 | Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering
Fang, J. | Wang, J. | Fu, L. | Lu, M. | Xu, M. Unfold
A macro-microscopic traffic flow data-driven optimal control strategy for freeway Scopus
期刊论文 | 2025 , 239 (2-3) , 502-513 | Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering
Fang, J. | Wang, J. | Fu, L. | Lu, M. | Xu, M. Unfold
A macro-microscopic traffic flow data-driven optimal control strategy for freeway EI
期刊论文 | 2025 , 239 (2-3) , 502-513 | Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering
Fang, Jie | Wang, Juanmeizi | Fu, Lina | Lu, Mingwen | Xu, Mengyun Unfold
MDTGAN: Multi domain generative adversarial transfer learning network for traffic data imputation SCIE
期刊论文 | 2024 , 255 | EXPERT SYSTEMS WITH APPLICATIONS
Fang, Jie | He, Hangyu | Xu, Mengyun | Chen, Hongting Unfold
WoS CC Cited Count： 1
Abstract&Keyword Cite Version(2)

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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MDTGAN: Multi domain generative adversarial transfer learning network for traffic data imputation Scopus
期刊论文 | 2024 , 255 | Expert Systems with Applications
Fang, J. | He, H. | Xu, M. | Chen, H. Unfold
MDTGAN: Multi domain generative adversarial transfer learning network for traffic data imputation EI
期刊论文 | 2024 , 255 | Expert Systems with Applications
Fang, Jie | He, Hangyu | Xu, Mengyun | Chen, Hongting Unfold
Multistep traffic speed prediction from multiple time-scale spatiotemporal features using graph attention network SCIE
期刊论文 | 2024 , 54 (15-16) , 7479-7492 | APPLIED INTELLIGENCE
Fang, Jie | Wu, Zhichao | Xu, Mengyun | Chen, Hongting Unfold
Abstract&Keyword Cite Version(3)

Abstract ：

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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Multistep traffic speed prediction from multiple time-scale spatiotemporal features using graph attention network
期刊论文 | 2024 , 54 (15-16) , 7479-7492 | Applied Intelligence
Jie Fang | Zhichao Wu | Mengyun Xu | Hongting Chen Unfold
Multistep traffic speed prediction from multiple time-scale spatiotemporal features using graph attention network Scopus
期刊论文 | 2024 , 54 (15-16) , 7479-7492 | Applied Intelligence
Fang, J. | Wu, Z. | Xu, M. | Chen, H. Unfold
Multistep traffic speed prediction from multiple time-scale spatiotemporal features using graph attention network EI
期刊论文 | 2024 , 54 (15-16) , 7479-7492 | Applied Intelligence
Fang, Jie | Wu, Zhichao | Xu, Mengyun | Chen, Hongting Unfold
Internet of Vehicles Data-Oriented Arterial Travel Time Estimation Framework With Dynamic Multigraph Model SCIE
期刊论文 | 2023 , 15 (3) , 101-116 | IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE
Qiu, Tony Z. | Xu, Mengyun | Fang, Jie
WoS CC Cited Count： 2
Abstract&Keyword Cite Version(2)

Abstract ：

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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Internet of Vehicles Data-Oriented Arterial Travel Time Estimation Framework With Dynamic Multigraph Model Scopus
期刊论文 | 2023 , 15 (3) , 2-19 | IEEE Intelligent Transportation Systems Magazine
Qiu, T.Z. | Xu, M. | Fang, J.
Internet of Vehicles Data-Oriented Arterial Travel Time Estimation Framework With Dynamic Multigraph Model EI
期刊论文 | 2023 , 15 (3) , 101-116 | IEEE Intelligent Transportation Systems Magazine
Qiu, Tony Z. | Xu, Mengyun | Fang, Jie
Prediction of Public Bus Passenger Flow Using Spatial-Temporal Hybrid Model of Deep Learning SCIE
期刊论文 | 2022 , 148 (4) | JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS
Chen, Tao | Fang, Jie | Xu, Mengyun | Tong, Yingfang | Chen, Wentian Unfold
WoS CC Cited Count： 8
Abstract&Keyword Cite Version(1)

Abstract ：

Passenger flow predictions are of great significance to bus scheduling and route optimization. In this paper, a novel algorithm, namely the Spatial-Temporal Graph Sequence with Attention Network (STGSAN), was proposed to predict transit passenger flow. The algorithm mainly focused on the following three aspects: (1) a graph attention network (GAT) was used to capture the spatial correlation of various bus stops; (2) to make full use of the historical and real-time data, a bidirectional long short-term memory and attention mechanism was conducted to extract the temporal correlation of historical ridership at bus stations; and (3) external factors that affect passenger choices were taken into account. We conducted an experiment using field data collected in Urumqi, China. After comparison with five other models, the proposed model was proven to have excellent performance prediction.

Keyword ：

Deep learning Deep learning Short-term bus passenger flow prediction Short-term bus passenger flow prediction Spatiotemporal correlation Spatiotemporal correlation

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GB/T 7714 Chen, Tao , Fang, Jie , Xu, Mengyun et al. Prediction of Public Bus Passenger Flow Using Spatial-Temporal Hybrid Model of Deep Learning [J]. | JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS , 2022 , 148 (4) .
MLA Chen, Tao et al. "Prediction of Public Bus Passenger Flow Using Spatial-Temporal Hybrid Model of Deep Learning" . | JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS 148 . 4 (2022) .
APA Chen, Tao , Fang, Jie , Xu, Mengyun , Tong, Yingfang , Chen, Wentian . Prediction of Public Bus Passenger Flow Using Spatial-Temporal Hybrid Model of Deep Learning . | JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS , 2022 , 148 (4) .
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Prediction of Public Bus Passenger Flow Using Spatial-Temporal Hybrid Model of Deep Learning EI
期刊论文 | 2022 , 148 (4) | Journal of Transportation Engineering Part A: Systems
Chen, Tao | Fang, Jie | Xu, Mengyun | Tong, Yingfang | Chen, Wentian Unfold
Short-term Forecasting of Travel Time Utilizing Deep Learning Approach EI
会议论文 | 2022 , 12285 | 2022 International Conference on Advanced Algorithms and Neural Networks, AANN 2022
Tong, YingFang | Fang, Jie | Liu, ZhiJia | Xiao, PingHui Unfold
Abstract&Keyword Cite

Abstract ：

Travel time prediction is a fundamental part of traffic analysis. Meanwhile it affected by spatial correlations, temporal dependencies, external conditions (e.g. weather, meta data, traffic conditions). In this paper, we propose a deep learning framework that integrates CNN and Bi-LSTM to learn spatial-temporal feature representations of travel time prediction. The short-term (5 minutes interval) historical traffic data which fully utilize to capture the patterns and trend of the travel time. Our paper sorted the feature into two categories: time-varying attributes, non-time-varying attributes. The proposed models called MV-FCL were evaluated on a network in the City of Zhangzhou, China. The results demonstrate that the proposed MV-FCL model outperform state-of-art baselines. © 2022 SPIE.

Keyword ：

Bismuth compounds Bismuth compounds Forecasting Forecasting Long short-term memory Long short-term memory Travel time Travel time

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GB/T 7714 Tong, YingFang , Fang, Jie , Liu, ZhiJia et al. Short-term Forecasting of Travel Time Utilizing Deep Learning Approach [C] . 2022 .
MLA Tong, YingFang et al. "Short-term Forecasting of Travel Time Utilizing Deep Learning Approach" . (2022) .
APA Tong, YingFang , Fang, Jie , Liu, ZhiJia , Xiao, PingHui . Short-term Forecasting of Travel Time Utilizing Deep Learning Approach . (2022) .
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A Map-Matching Algorithm With Extraction of Multigroup Information for Low-Frequency Data SCIE
期刊论文 | 2022 | IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE
Fang, Jie | Wu, Xiongwei | Lin, Dianchao | Xu, Mengyun | Wu, Huahua | Wu, Xuesong | Bi, Ting Unfold
Abstract&Keyword Cite Version(2)

Abstract ：

The growing use of probe vehicles generates a huge number of global navigation satellite systems (GNSS) data. Limited by satellite positioning technology, further improving the accuracy of map matching (MM) is challenging work, especially for low-frequency trajectories. When matching a trajectory, the ego vehicle's spatial-temporal information of the present trip is most useful with the least amount of data. In addition, there is a large number of other data, e.g., other vehicles' state and past prediction results, but it is hard to extract useful information for matching maps and inferring paths. Most of the MM studies have used only the ego vehicle's data and ignored other vehicles' data. Based on those, this article designs a new MM method to make full use of "big data." We first sort all the data into four groups according to their spatial and temporal distance from the present matching probe, which allows us to sort for their usefulness. Then we design three different methods to extract valuable information (scores) from them: a score for speed and bearing, one for historical usage, and another for traffic state using a spectral graph Markov neural network. Finally, we use a modified top-K shortest-path method to search the candidate paths within an ellipse region and then use the fused score to infer the path (projected location). We test the proposed method against baseline algorithms using a real-world dataset in China. The results show that all scoring methods can enhance MM accuracy. Furthermore, our method outperforms the others, especially when the GNSS probing frequency is <= 0.01Hz.

Keyword ：

Artificial neural networks Artificial neural networks Collaboration Collaboration Data mining Data mining Global navigation satellite system Global navigation satellite system Probes Probes Satellites Satellites Trajectory Trajectory

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GB/T 7714 Fang, Jie , Wu, Xiongwei , Lin, Dianchao et al. A Map-Matching Algorithm With Extraction of Multigroup Information for Low-Frequency Data [J]. | IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE , 2022 .
MLA Fang, Jie et al. "A Map-Matching Algorithm With Extraction of Multigroup Information for Low-Frequency Data" . | IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE (2022) .
APA Fang, Jie , Wu, Xiongwei , Lin, Dianchao , Xu, Mengyun , Wu, Huahua , Wu, Xuesong et al. A Map-Matching Algorithm With Extraction of Multigroup Information for Low-Frequency Data . | IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE , 2022 .
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A Map-Matching Algorithm With Extraction of Multi-Group Information for Low-Frequency Data Scopus
期刊论文 | 2023 , 15 (2) , 238-250 | IEEE Intelligent Transportation Systems Magazine
Fang, J. | Wu, X. | Lin, D. | Xu, M. | Wu, H. | Bi, T. Unfold
A Map-Matching Algorithm With Extraction of Multi-Group Information for Low-Frequency Data EI
期刊论文 | 2023 , 15 (2) , 238-250 | IEEE Intelligent Transportation Systems Magazine
Fang, Jie | Wu, Xiongwei | Lin, Dianchao | Xu, Mengyun | Wu, Huahua | Wu, Xuesong | Bi, Ting Unfold
Proactive highway traffic control with intelligent multi-objective optimisation algorithm SCIE
期刊论文 | 2022 , 175 (2) , 65-75 | PROCEEDINGS OF THE INSTITUTION OF CIVIL ENGINEERS-TRANSPORT
Xie, Huahui | Tu, Lili | Fang, Jie | Easa, Said M. Unfold
WoS CC Cited Count： 2
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Abstract ：

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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Proactive highway traffic control with intelligent multi-objective optimisation algorithm EI
期刊论文 | 2022 , 175 (2) , 65-75 | Proceedings of the Institution of Civil Engineers: Transport
Xie, Huahui | Tu, Lili | Fang, Jie | Easa, Said M. Unfold
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