Loading [MathJax]/extensions/MathMenu.js

Fan Ding - IEEE Xplore Author Profile

Scheduled Maintenance on Monday 1/13/2025

Single article sales and account management will be unavailable from 5:00 AM - 7:00 PM ET (09:00 - 23:00 UTC). We apologize for the inconvenience.

IEEE.org
IEEE Xplore
IEEE SA
IEEE Spectrum
More Sites

- Donate
- Personal Sign In

Access provided by:

MIT Libraries

Access provided by:

MIT Libraries

ADVANCED SEARCH

Author details

Fan Ding

Publications

19

Citations

153

Publications by Year

20202025

Co-Authors:

Soyoung AhnA. S. M. BakibillahJun ChenWeiqi ChenYang Cheng

Show All Co-Authors (56)

Fan Ding

Affiliation

National Mobile Communications Research Laboratory

Southeast University

Nanjing, China

Publication Topics

Deep Learning,
Model Predictive Control,
Neural Network,
Traffic Conditions,
Recurrent Neural Network,
Traffic Flow,
Convolutional Neural Network,
Deep Reinforcement Learning,
Electric Vehicles,
Long Short-term Memory,
Reward Function,
Road Network

Biography

Fan Ding (Member, IEEE) received the B.S. degree in information engineering from Southeast University, Nanjing, China, in 2012, and the M.S. degree in computer science and the Ph.D. degree in civil engineering from the University of Wisconsin–Madison, Madison, WI, USA, in 2016 and 2017, respectively.,He is currently an Assistant Professor with the School of Transportation, Southeast University. His research interests include intelligent transportation systems (ITSs), mobile traffic sensor modeling, proactive traffic management, and transportation big data analytics.(Based on document published on 5 December 2023).

Publications

19

Citations

153

Publications by Year

20202025

Co-Authors:

Soyoung Ahn
A. S. M. Bakibillah
Jun Chen
Weiqi Chen
Yang Cheng

Show All Co-Authors (56)

Author's Published Works

Search History

Showing 1-19 of 19 results

Journals (11)

Conferences (5)

Early Access Articles (2)

Magazines (1)

Sort

Filter Results

Show

Subscribed Content

Open Access Only

Range
Single Year
Fan Ding(19)
Huachun Tan(9)
Hanxuan Dong(5)
Jiankun Peng(5)
Hailong Zhang(5)
School of Transportation, Southeast University, Nanjing, China(8)
Department of Civil and Environmental Engineering, University of Wisconsin–Madison, Madison, WI, USA(3)
Advanced Research Institute of Multidisciplinary Sciences, Beijing Institute of Technology, Beijing, China(2)
Department of Civil Engineering, McGill University, Montreal, Canada(2)
School of Transportation, Southeast University, Nanjing, Jiangsu, China(2)
IEEE Transactions on Intelligent Transportation Systems(5)
IEEE Transactions on Transportation Electrification(4)
2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)(3)
IEEE Access(2)
2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)(1)
IEEE(19)
Kuching, Malaysia(3)
Chongqing, China(1)
Macau, China(1)
Model Predictive Control(6)
Neural Network(6)
Deep Learning(5)
Traffic Conditions(5)
Traffic Flow(5)

Select All on Page

Sort By

Results

Ternary Message Passing Decoding of High-Rate Regular LDPC Codes

Qiushi Xu;Ming Jiang;Fan Ding;Yi Wang

IEEE Communications Letters

Year: 2025 | Early Access Article |

In this letter, we present an improved ternary message passing (TMP) decoding scheme designed for the high-rate regular low-density parity-check (LDPC) codes, which are widely used in storage systems. The decoders in this approach transmit information internally by only three states, and compared to traditional normalized min-sum decoding, it retains only sign multiplication operations, resulting ...Show More

Ternary Message Passing Decoding of High-Rate Regular LDPC Codes

Qiushi Xu;Ming Jiang;Fan Ding;Yi Wang

IEEE Communications Letters

Year: 2025 | Early Access Article |

Co-Evolving Traffic State Parameters Prediction Based on Mechanism-Data Blending Driven Deep Learning

Hanxuan Dong;Hailong Zhang;Fan Ding;Huachun Tan

IEEE Transactions on Intelligent Transportation Systems

Year: 2025 | Volume: 26, Issue: 3 | Journal Article |

HTML

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 multivari...Show More

Co-Evolving Traffic State Parameters Prediction Based on Mechanism-Data Blending Driven Deep Learning

Hanxuan Dong;Hailong Zhang;Fan Ding;Huachun Tan

IEEE Transactions on Intelligent Transportation Systems

Year: 2025 | Volume: 26, Issue: 3 | Journal Article |

Energy-Efficient Hybrid Model Predictive Trajectory Planning for Autonomous Electric Vehicles

Fan Ding;Xuewen Luo;Gaoxuan Li;Hwa Hui Tew;Junn Yong Loo;Chor Wai Tong;A.S.M Bakibillah;Ziyuan Zhao;Zhiyu Tao

2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Year: 2024 | Conference Paper |

HTML

To tackle the twin challenges of limited battery life and lengthy charging durations in electric vehicles (EV s), this paper introduces an Energy-efficient Hybrid Model Predictive Planner (EHMPP), which employs an energy-saving optimization strategy. EHMPP focuses on refining the design of the motion planner to be seamlessly integrated with the existing automatic driving algorithms, without additi...Show More

Energy-Efficient Hybrid Model Predictive Trajectory Planning for Autonomous Electric Vehicles

Fan Ding;Xuewen Luo;Gaoxuan Li;Hwa Hui Tew;Junn Yong Loo;Chor Wai Tong;A.S.M Bakibillah;Ziyuan Zhao;Zhiyu Tao

2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Year: 2024 | Conference Paper |

FedBChain: A Blockchain-Enabled Federated Learning Framework for Improving DeepConvLSTM with Comparative Strategy Insights

Gaoxuan Li;Chern Hong Lim;Qiyao Ma;Xinyu Tang;Hwa Hui Tew;Fan Ding;Xuewen Luo

2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Year: 2024 | Conference Paper |

HTML

Recent research in the field of Human Activity Recognition has shown that an improvement in prediction performance can be achieved by reducing the number of LSTM layers. However, this kind of enhancement is only significant on monolithic architectures, and when it runs on large-scale distributed training, data security and privacy issues will be reconsidered, and its prediction performance is unkn...Show More

FedBChain: A Blockchain-Enabled Federated Learning Framework for Improving DeepConvLSTM with Comparative Strategy Insights

Gaoxuan Li;Chern Hong Lim;Qiyao Ma;Xinyu Tang;Hwa Hui Tew;Fan Ding;Xuewen Luo

2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Year: 2024 | Conference Paper |

KANS: Knowledge Discovery Graph Attention Network for Soft Sensing in Multivariate Industrial Processes

Hwa Hui Tew;Gaoxuan Li;Fan Ding;Xuewen Luo;Junn Yong Loo;Chee-Ming Ting;Ze Yang Ding;Chee Pin Tan

2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Year: 2024 | Conference Paper |

HTML

Soft sensing of hard-to-measure variables is often crucial in industrial processes. Current practices rely heavily on conventional modeling techniques that show success in improving accuracy. However, they overlook the non-linear nature, dynamics characteristics, and non-Euclidean dependencies between complex process variables. To tackle these challenges, we present a framework known as a Knowledg...Show More

KANS: Knowledge Discovery Graph Attention Network for Soft Sensing in Multivariate Industrial Processes

Hwa Hui Tew;Gaoxuan Li;Fan Ding;Xuewen Luo;Junn Yong Loo;Chee-Ming Ting;Ze Yang Ding;Chee Pin Tan

2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Year: 2024 | Conference Paper |

Graph RL-Based Route Planning Method for EVs with Synchronous Charge Station Selection

Yuhan Ma;Weiqi Chen;Fan Ding;Yi Fan;Jiankun Peng

2024 8th CAA International Conference on Vehicular Control and Intelligence (CVCI)

Year: 2024 | Conference Paper |

HTML

Electric vehicles (EV s) represent a crucial solution for energy conservation and emission reduction in response to global warming. However, widespread usage of EVs presents challenges to the optimization of both route planning and charging planning, due to the considerations of road network, distribution of charging stations, and vehicle interactions. This study proposes a synchronous method of r...Show More

Graph RL-Based Route Planning Method for EVs with Synchronous Charge Station Selection

Yuhan Ma;Weiqi Chen;Fan Ding;Yi Fan;Jiankun Peng

2024 8th CAA International Conference on Vehicular Control and Intelligence (CVCI)

Year: 2024 | Conference Paper |

A Fundamental-Diagram-Informed Spatial Partitioning Method for Heterogeneous Traffic Networks

Fan Ding;Yu Zhao;Huachun Tan;Zhao Liu;Ziyuan Pu

IEEE Internet of Things Journal

Year: 2024 | Early Access Article |

Spatially partitioning heterogeneous traffic networks into multiple sub-networks 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 wi...Show More

A Fundamental-Diagram-Informed Spatial Partitioning Method for Heterogeneous Traffic Networks

Fan Ding;Yu Zhao;Huachun Tan;Zhao Liu;Ziyuan Pu

IEEE Internet of Things Journal

Year: 2024 | Early Access Article |

A Deep Long Short-Term Memory Network Embedded Model Predictive Control Strategies for Car-Following Control of Connected Automated Vehicles in Mixed Traffic

Yang Zhou;Zhen Zhang;Fan Ding;Soyoung Ahn;Keshu Wu;Bin Ran

IEEE Transactions on Intelligent Transportation Systems

Year: 2024 | Volume: 25, Issue: 7 | Journal Article |

Cited by: Papers (1)

HTML

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 traj...Show More

A Deep Long Short-Term Memory Network Embedded Model Predictive Control Strategies for Car-Following Control of Connected Automated Vehicles in Mixed Traffic

Yang Zhou;Zhen Zhang;Fan Ding;Soyoung Ahn;Keshu Wu;Bin Ran

IEEE Transactions on Intelligent Transportation Systems

Year: 2024 | Volume: 25, Issue: 7 | Journal Article |

Multiple Electric Components Health-Aware Eco-Driving Strategy for Fuel Cell Hybrid Electric Vehicle Based on Soft Actor-Critic Algorithm

Jiankun Peng;Jiaxuan Zhou;Jun Chen;Dawei Pi;Jingda Wu;Hongliang Wang;Fan Ding

IEEE Transactions on Transportation Electrification

Year: 2024 | Volume: 10, Issue: 3 | Journal Article |

Cited by: Papers (5)

HTML

The eco-driving strategy based on deep reinforcement learning holds significant potential for achieving energy efficiency, longevity, and safety of fuel cell hybrid electric vehicle (FCHEV). This article proposes a health-aware eco-driving strategy for FCHEV based on the soft actor-critic (SAC) algorithm. Building upon health awareness of multiple electric components including power battery, fuel ...Show More

Multiple Electric Components Health-Aware Eco-Driving Strategy for Fuel Cell Hybrid Electric Vehicle Based on Soft Actor-Critic Algorithm

Jiankun Peng;Jiaxuan Zhou;Jun Chen;Dawei Pi;Jingda Wu;Hongliang Wang;Fan Ding

IEEE Transactions on Transportation Electrification

Year: 2024 | Volume: 10, Issue: 3 | Journal Article |

Integrated Velocity Optimization and Energy Management Strategy for Hybrid Electric Vehicle Platoon: A Multiagent Reinforcement Learning Approach

Hailong Zhang;Jiankun Peng;Hanxuan Dong;Fan Ding;Huachun Tan

IEEE Transactions on Transportation Electrification

Year: 2024 | Volume: 10, Issue: 2 | Journal Article |

Cited by: Papers (6)

HTML

Coordinating a platoon of connected hybrid electric vehicles (HEVs) poses challenges due to the intricacy of their powertrains and the diverse driving scenarios encountered. The existing mainstream framework uses a hierarchical control scheme, simplifying the unified optimization problem into two separate series control processes: the powertrain level and the vehicle level. However, this approach ...Show More

Integrated Velocity Optimization and Energy Management Strategy for Hybrid Electric Vehicle Platoon: A Multiagent Reinforcement Learning Approach

Hailong Zhang;Jiankun Peng;Hanxuan Dong;Fan Ding;Huachun Tan

IEEE Transactions on Transportation Electrification

Year: 2024 | Volume: 10, Issue: 2 | Journal Article |

Two-stream LSTM Network with Hybrid Attention for Vehicle Trajectory Prediction

Chao Li;Zhanwen Liu;Jiaying Zhang;Yang Wang;Fan Ding;Xiangmo Zhao

2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

Year: 2022 | Conference Paper |

Cited by: Papers (1)

HTML

Trajectory prediction aims to estimate future location by exploring driving behavior and historical trajectory, which is essential for driving decision-making and local motion planning of smart vehicles. However, affected by the multiple complex interaction in the traffic scene, predicting future trajectory precisely is a challenging task. Most existing models simply fuse the inter-vehicle interac...Show More

Two-stream LSTM Network with Hybrid Attention for Vehicle Trajectory Prediction

Chao Li;Zhanwen Liu;Jiaying Zhang;Yang Wang;Fan Ding;Xiangmo Zhao

2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

Year: 2022 | Conference Paper |

Battery-Aware Cooperative Merging Strategy of Connected Electric Vehicles Based on Reinforcement Learning With Hindsight Experience Replay

Hanxuan Dong;Hailong Zhang;Fan Ding;Huachun Tan;Jiankun Peng

IEEE Transactions on Transportation Electrification

Year: 2022 | Volume: 8, Issue: 3 | Journal Article |

Cited by: Papers (4)

HTML

Cooperative control of connected and automated electric vehicles (CAEVs) offers a great potential for a safe, high-efficient, sustainable transportation system. Among them, coordinated on-ramp merging based on an on-ramp merging vehicle and a mainline facilitating is a hot spot to lighten the impact of shockwave on highway junctions. Reinforcement learning (RL) is a promising solution to address t...Show More

Battery-Aware Cooperative Merging Strategy of Connected Electric Vehicles Based on Reinforcement Learning With Hindsight Experience Replay

Hanxuan Dong;Hailong Zhang;Fan Ding;Huachun Tan;Jiankun Peng

IEEE Transactions on Transportation Electrification

Year: 2022 | Volume: 8, Issue: 3 | Journal Article |

Platoon Trajectories Generation: A Unidirectional Interconnected LSTM-Based Car-Following Model

Yangxin Lin;Ping Wang;Yang Zhou;Fan Ding;Chen Wang;Huachun Tan

IEEE Transactions on Intelligent Transportation Systems

Year: 2022 | Volume: 23, Issue: 3 | Journal Article |

Cited by: Papers (38)

HTML

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 ...Show More

Platoon Trajectories Generation: A Unidirectional Interconnected LSTM-Based Car-Following Model

Yangxin Lin;Ping Wang;Yang Zhou;Fan Ding;Chen Wang;Huachun Tan

IEEE Transactions on Intelligent Transportation Systems

Year: 2022 | Volume: 23, Issue: 3 | Journal Article |

Understanding and Modeling Urban Mobility Dynamics via Disentangled Representation Learning

Hailong Zhang;Yuankai Wu;Huachun Tan;Hanxuan Dong;Fan Ding;Bin Ran

IEEE Transactions on Intelligent Transportation Systems

Year: 2022 | Volume: 23, Issue: 3 | Journal Article |

Cited by: Papers (13)

HTML

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 we...Show More

Understanding and Modeling Urban Mobility Dynamics via Disentangled Representation Learning

Hailong Zhang;Yuankai Wu;Huachun Tan;Hanxuan Dong;Fan Ding;Bin Ran

IEEE Transactions on Intelligent Transportation Systems

Year: 2022 | Volume: 23, Issue: 3 | Journal Article |

A Feature-Based Approach to Large-Scale Freeway Congestion Detection Using Full Cellular Activity Data

Shen Li;Yang Cheng;Peter Jin;Fan Ding;Qing Li;Bin Ran

IEEE Transactions on Intelligent Transportation Systems

Year: 2022 | Volume: 23, Issue: 2 | Journal Article |

Cited by: Papers (4)

HTML

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 fea...Show More

A Feature-Based Approach to Large-Scale Freeway Congestion Detection Using Full Cellular Activity Data

Shen Li;Yang Cheng;Peter Jin;Fan Ding;Qing Li;Bin Ran

IEEE Transactions on Intelligent Transportation Systems

Year: 2022 | Volume: 23, Issue: 2 | Journal Article |

Predicted Network Equilibrium Model of Electric Vehicles With Stationary and Dynamic Charging Infrastructure on the Road Network

Xiasen Wang;Don MacKenzie;Yang Zhou;Fan Ding

IEEE Intelligent Transportation Systems Magazine

Year: 2022 | Volume: 14, Issue: 2 | Magazine Article |

Cited by: Papers (2)

HTML

With the rapid development of charging-while-driving technology, the deployments of charging lanes and direct current fast chargers (dcFCs) inevitably affect the charging and route choices behavior of battery electric vehicle (BEV) drivers. This article describes the first attempt to develop a predicted user equilibrium model on the road network considering both dcFC and charging lanes charging ch...Show More

Predicted Network Equilibrium Model of Electric Vehicles With Stationary and Dynamic Charging Infrastructure on the Road Network

Xiasen Wang;Don MacKenzie;Yang Zhou;Fan Ding

IEEE Intelligent Transportation Systems Magazine

Year: 2022 | Volume: 14, Issue: 2 | Magazine Article |

A Deep Reinforcement Learning-Based Energy Management Framework With Lagrangian Relaxation for Plug-In Hybrid Electric Vehicle

Hailong Zhang;Jiankun Peng;Huachun Tan;Hanxuan Dong;Fan Ding

IEEE Transactions on Transportation Electrification

Year: 2021 | Volume: 7, Issue: 3 | Journal Article |

Cited by: Papers (46)

HTML

Reinforcement learning (RL)-based energy management is one of the current hot spots of hybrid electric vehicles. Recent advances in RL-based energy management focus on energy-saving performance but less considers the constrained setting for training safety. This article proposes an RL framework named coach-actor-double-critic (CADC) for the optimization of energy management considered as the const...Show More

A Deep Reinforcement Learning-Based Energy Management Framework With Lagrangian Relaxation for Plug-In Hybrid Electric Vehicle

Hailong Zhang;Jiankun Peng;Huachun Tan;Hanxuan Dong;Fan Ding

IEEE Transactions on Transportation Electrification

Year: 2021 | Volume: 7, Issue: 3 | Journal Article |

A Personalized Human Drivers’ Risk Sensitive Characteristics Depicting Stochastic Optimal Control Algorithm for Adaptive Cruise Control

Jiwan Jiang;Fan Ding;Yang Zhou;Jiaming Wu;Huachun Tan

Year: 2020 | Volume: 8 | Journal Article |

Cited by: Papers (10)

HTML

This paper presents a personalized stochastic optimal adaptive cruise control (ACC) algorithm for automated vehicles (AVs) incorporating human drivers' risk-sensitivity under system and measurement uncertainties. The proposed controller is designed as a linear exponential-of-quadratic Gaussian (LEQG) problem, which utilizes the stochastic optimal control mechanism to feedback the deviation from th...Show More

A Personalized Human Drivers’ Risk Sensitive Characteristics Depicting Stochastic Optimal Control Algorithm for Adaptive Cruise Control

Jiwan Jiang;Fan Ding;Yang Zhou;Jiaming Wu;Huachun Tan

Year: 2020 | Volume: 8 | Journal Article |

Traffic Flow Prediction With Missing Data Imputed by Tensor Completion Methods

Qin Li;Huachun Tan;Yuankai Wu;Linhui Ye;Fan Ding

Year: 2020 | Volume: 8 | Journal Article |

Cited by: Papers (23)

HTML

Missing data is inevitable and ubiquitous in intelligent transportation systems (ITSs). A handful of completion methods have been proposed, among which the tensor-based models have been shown to be the most advantageous for missing traffic data imputation. Despite their superior imputation accuracies, the adoption of these imputed data is not uniform in modern ITSs applications. The primary goal o...Show More

Traffic Flow Prediction With Missing Data Imputed by Tensor Completion Methods

Qin Li;Huachun Tan;Yuankai Wu;Linhui Ye;Fan Ding

Year: 2020 | Volume: 8 | Journal Article |

IEEE Personal Account

Change username/password

Purchase Details

Payment Options
View Purchased Documents

Profile Information

Communications Preferences
Profession and Education
Technical interests

Need Help?

US & Canada: +1 800 678 4333
Worldwide: +1 732 981 0060
Contact & Support

Follow

About IEEE Xplore | Contact Us | Help | Accessibility | Terms of Use | Nondiscrimination Policy | IEEE Ethics Reporting | Sitemap | IEEE Privacy Policy

A public charity, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.

© Copyright 2025 IEEE - All rights reserved, including rights for text and data mining and training of artificial intelligence and similar technologies.

IEEE Account

Change Username/Password
Update Address

Purchase Details

Payment Options
Order History
View Purchased Documents

Profile Information

Communications Preferences
Profession and Education
Technical Interests

Need Help?

US & Canada: +1 800 678 4333
Worldwide: +1 732 981 0060
Contact & Support

About IEEE Xplore
Contact Us
Help
Accessibility
Terms of Use
Nondiscrimination Policy
Sitemap
Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

Test Whats new message.