James J.Q. Yu

James J.Q. Yu
Southern University of Science and Technology | SUSTech · Department of Computer Science and Engineering

Doctor of Philosophy
Looking for motivated postdocs and prospective graduate students with interests in intelligent transportation systems

About

90
Publications
22,791
Reads
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2,914
Citations
Citations since 2017
71 Research Items
2697 Citations
20172018201920202021202220230200400600800
20172018201920202021202220230200400600800
20172018201920202021202220230200400600800
20172018201920202021202220230200400600800
Introduction
Assistant professor at Southern University of Science and Technology, China. I'm most interested in smart cities development and deep learning techniques. I am constantly looking for motivated prospective graduate students and postdocs with interests in deep learning and intelligent transportation systems to join the group. Visiting scholars, research assistants and undergraduates are also very welcome.

Publications

Publications (90)
Article
Full-text available
The growing complexity of real-world problems has motivated computer scientists to search for efficient problem-solving methods. Metaheuristics based on evolutionary computation and swarm intelligence are outstanding examples of nature-inspired solution techniques. Inspired by the social spiders, we propose a novel Social Spider Algorithm to solve...
Article
Full-text available
The smart city embraces gradual adoption of autonomous vehicles (AVs) into the intelligent transportation system. Contributed by their full-fledged controllability, AVs can respond to instantaneous situations with high efficiency and flexibility. In this paper, we propose a novel AV logistic system (AVLS) to accommodate logistic demands for smart c...
Article
Real-time traffic speed estimation is an essential component of intelligent transportation system (ITS) technologies. It is the foundation of modern transportation control and management applications. However, the existing traffic speed acquisition systems can only provide real-time speed measurements of a small number of roads with stationary spee...
Article
Full-text available
Traffic speed prediction is among the foundations of advanced traffic management and the gradual deployment of internet of things sensors is empowering data-driven approaches for the prediction. Nonetheless, existing research studies mainly focus on short-term traffic prediction that covers up to one hour forecast into the future. Previous long-ter...
Article
Full-text available
Existing traffic flow forecasting approaches by deep learning models achieve excellent success based on a large volume of datasets gathered by governments and organizations. However, these datasets may contain lots of user’s private data, which is challenging the current prediction approaches as user privacy is calling for the public concern in rec...
Article
Estimated time of arrival (ETA) is one of the critical services offered by navigation and hailing providers. The majority of existing solutions approach ETA as a regression problem and leverage GPS trajectories for estimation. However, the travel time fluctuates greatly between different trips, making simple regression methods skewed. Additionally,...
Article
Full-text available
In practice, traffic data collection is often warned by missing data due to communication errors, sensor failures, storage loss, among other factors, leading to impaired data collection and hampering the effectiveness of downstream applications. However, existing imputation approaches focus exclusively on estimating the lost value from incomplete o...
Article
Full-text available
Autonomous vehicle (AV) integration poses a significant challenge for intelligent transportation systems (ITSs). The ability to automatically coordinate complex AV operations at scale is crucial for advancing the quality of core transportation services, such as ride-sharing and parcel delivery. However, existing studies have only considered either...
Article
Full-text available
Autonomous vehicles (AVs), as one of the cores in future intelligent transportation systems (ITSs), can facilitate reliable and safe traffic operations and services. The ability to automatically perform effective AV motion planning and deploy efficient perception systems is vital for advancing the quality of core transportation services. However, e...
Article
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Background. Precise and comprehensive characterizations from anterior segment optical coherence tomography (AS-OCT) are of great importance in facilitating the diagnosis of angle-closure glaucoma. Existing automated analysis methods focus on analyzing structural properties identified from the single AS-OCT image, which is limited to comprehensively...
Preprint
Pedestrian trajectory forecasting is a fundamental task in multiple utility areas, such as self-driving, autonomous robots, and surveillance systems. The future trajectory forecasting is multi-modal, influenced by physical interaction with scene contexts and intricate social interactions among pedestrians. The mainly existing literature learns repr...
Article
Graph learning-based algorithms are becoming the prevalent traffic prediction solutions due to their capability of exploiting non-Euclidean spatial-temporal traffic data correlation. However, current predictors primarily employ heuristically constructed static traffic graphs in forecasting, which may not describe the latent traffic dynamics well. E...
Article
Network partitioning is recognized as an effective auxiliary approach for solving transportation tasks on large-scale traffic networks in a domain-decomposition manner. Most of the existing related partitioning algorithms are explicitly designed to traffic management problems and merely focus on the implied topology of the networks. In this paper,...
Article
Federated learning (FL) is widely adopted in traffic forecasting tasks involving large-scale IoT-enabled sensor data since its decentralization nature enables data providers’ privacy to be preserved. When employing state-of-the-art deep learning-based traffic predictors in FL systems, the existing FL frameworks confront overlarge communication ov...
Article
Full-text available
Privacy-preserving transportation mode identification (TMI) is among the key challenges toward future intelligent transportation systems. With recent developments in federated learning (FL), crowdsourcing has emerged as a promising cost-effective data source for training powerful TMI classifiers without compromising users’ data privacy. However, ex...
Article
Efficient collaboration between collaborative machine learning and wireless communication technology, forming a Federated Edge Learning (FEEL), has spawned a series of next-generation intelligent applications. However, due to the openness of network connections, the FEEL framework generally involves hundreds of remote devices (or clients), resultin...
Preprint
Full-text available
Efficient collaboration between collaborative machine learning and wireless communication technology, forming a Federated Edge Learning (FEEL), has spawned a series of next-generation intelligent applications. However, due to the openness of network connections, the FEEL framework generally involves hundreds of remote devices (or clients), resultin...
Conference Paper
Full-text available
Statistics on urban traffic speed flows are essential for thoughtful city planning. Recently, data-driven traffic prediction methods have become the state-of-the-art for a wide range of traffic forecasting tasks. However, many small cities have a limited amount of traffic data available for building data-driven models due to lack of data collection...
Chapter
Full-text available
Missing traffic data problem has a significant negative impact for data-driven applications in Intelligent Transportation Systems (ITS). However, existing models mainly focus on the imputation results under Missing Completely At Random (MCAR) task, and there is a considerable difference between MCAR with the situation encountered in real life. Furt...
Chapter
High-quality traffic data is crucial for intelligent transportation system and its data-driven applications. However, data missing is common in collecting real-world traffic datasets due to various factors. Thus, imputing missing values by extracting traffic characteristics becomes an essential task. By using conventional convolutional neural netwo...
Article
Full-text available
Electric vehicle (EV) dynamic wireless charging system has become an emerging application in the area of the intelligent transportation system (ITS). However, an integrated design of EV dynamic wireless charging system requires the considerations of both the economical and technical perspectives of a smart city. Specifically, most of the existing r...
Article
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Modern power systems are incorporated with distributed energy sources to be environmental-friendly and cost-effective. However, due to the uncertainties of the system integrated with renewable energy sources, effective strategies need to be adopted to stabilize the entire power systems. Hence, the system operators need accurate prediction tools to...
Article
Full-text available
GPS trajectories serve as a significant data source for travel mode identification along with the development of various GPS-enabled smart devices. However, such data directly integrate user private information, thus hindering users from sharing data with third parties. On the other hand, existing identification methods heavily depend on the respec...
Conference Paper
Full-text available
Intelligent transportation management requires not only statistical information on users' mobility patterns, but also knowledge of their corresponding transportation modes. While GPS trajectories can be readily obtained from GPS sensors found in modern smartphones and vehicles, these massive geospatial data are neither automatically annotated nor s...
Article
Intelligent transportation management requires not only statistical information on users' mobility patterns, but also knowledge of their corresponding transportation modes. While GPS trajectories can be readily obtained from GPS sensors found in modern smartphones and vehicles, these massive geospatial data are neither automatically annotated nor s...
Article
Full-text available
Digital humanities is an important subject because it enables developments in history, literature, and films. In this article, we perform an empirical study of a Chinese historical text, Records of the Three Kingdoms (Records), and a historical novel of the same story, Romance of the Three Kingdoms (Romance). We employ deep-learning-based natural l...
Article
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Accurate long-term origin-destination demand (OD) prediction can help understand traffic flow dynamics, which plays an essential role in urban transportation planning. However, the main challenge originates from the complex and dynamic spatial-temporal correlation of the time-varying traffic information. In response, a graph deep learning model for...
Article
Full-text available
Federated learning has been applied to various tasks in intelligent transportation systems to protect data privacy through decentralized training schemes. The majority of the state-of-the-art models in ITS are graph neural networks (GNN)-based for spatial information learning. When applying federated learning to the ITS tasks with GNN-based models,...
Conference Paper
Full-text available
GPS trajectory is one of the most significant data sources in intelligent transportation systems (ITS). A simple application is to use these data sources to help companies or organizations identify users’ travel behavior. However, since GPS trajectory is directly related to private data (e.g., location) of users, citizens are unwilling to share the...
Article
Full-text available
Crowdsourced navigation is becoming the prevalent automobile navigation solution with the widespread adoption of smartphones over the past decade, which supports a plethora of intelligent transportation system services. However, it is subjected to Sybil attacks that inject carefully designed adversarial GPS trajectories to compromise the data aggre...
Article
Accurate traffic speed prediction is critical to modern internet of things-based intelligent transportation systems. It serves as the foundation of advanced traffic management systems and travel services. Nonetheless, the large number of roads and sensors impose great computational burden to existing forecast approaches, most of which can only hand...
Conference Paper
Full-text available
Traffic speed prediction is among the key problems in intelligent transportation system (ITS). Traffic patterns with complex spatial dependency make accurate prediction on traffic networks a challenging task. Recently, a deep learning approach named Spatio-Temporal Graph Convolutional Networks (STGCN) has achieved state-of-the-art results in traffi...
Article
Full-text available
With increasing public demands for timely and accurate air pollution reporting, more air quality monitoring stations have been deployed by the governments in urban metropolises to increase the coverage of urban air pollution monitoring. However, due to systematic or accidental failures, some air pollution measurements obtained from these stations a...
Preprint
Full-text available
Existing traffic flow forecasting by deep learning models achieves excellent success based on a large volume of datasets gathered by governments and organizations. However, privacy and security issues are challenging the current prediction approaches. To address these challenges, we introduce a privacy-preserving machine learning technique based on...
Preprint
Full-text available
Existing traffic flow forecasting technologies achieve great success based on deep learning models on a large number of datasets gathered by organizations. However, there are two critical challenges. One is that data exists in the form of "isolated islands". The other is the data privacy and security issue, which is becoming more significant than e...
Article
Full-text available
Travel mode identification is among the key problems in transportation research. With the gradual and rapid adoption of GPS-enabled smart devices in modern society, this task greatly benefits from the massive volume of GPS trajectories generated. However, existing identification approaches heavily rely on manual annotation of these trajectories wit...
Article
Full-text available
Accurate identification in public travel modes is an essential task in intelligent transportation systems. In recent years, GPS-based identification is gradually replacing the conventional survey-based information-gathering process due to the more detailed and precise data on individual's travel patterns. Nonetheless, existing research suffers from...
Conference Paper
Full-text available
Generative Adversarial Network (GAN) and its variants serve as a perfect representation of the data generation model, providing researchers with a large amount of high-quality generated data. They illustrate a promising direction for research with limited data availability. When GAN learns the semantic-rich data distribution from a dataset, the den...
Preprint
Generative Adversarial Network (GAN) and its variants serve as a perfect representation of the data generation model, providing researchers with a large amount of high-quality generated data. They illustrate a promising direction for research with limited data availability. When GAN learns the semantic-rich data distribution from a dataset, the den...
Article
Online vehicle routing is an important task of the modern transportation service provider. Contributed by the ever-increasing real-time demand on the transportation system, especially small-parcel last-mile delivery requests, vehicle route generation is becoming more computationally complex than before. The existing routing algorithms are mostly ba...
Article
Data integrity of power system states is critical to modern power grid operation and control. Due to communication latency, state measurements are not immediately available at the control center, rendering slow responses of time-sensitive applications. In this paper, a new graph-based deep learning approach is proposed to recover and predict the st...
Article
Full-text available
Traffic speed prediction, as one of the most important topics in Intelligent Transport Systems (ITS), has been investigated thoroughly in the literature. Nonetheless, traditional methods show their limitation in coping with complexity and high nonlinearity of traffic data as well as learning spatial-temporal dependencies. Particularly, they often n...
Article
Transient stability assessment is critical for power system operation and control. Existing related research makes a strong assumption that the data transmission time for system variable measurements to arrive at the control center is negligible, which is unrealistic. In this paper, we focus on investigating the impact of data transmission latency...
Article
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With the technological advancements, the autonomous vehicle (AV) is expected to play an active role in the future transportation system. An AV-based public transportation system was recently proposed to unleash the full capability of AVs to provide effective and flexible transportation services. It establishes a new public transportation market tha...
Article
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Autonomous vehicles are expected to play an important role in handling the last mile logistics in intelligent transportation systems thanks to their unmanned nature and full-fledged controllability. Recently, an autonomous vehicle logistic system (AVLS) was proposed, which employs autonomous vehicles to serve logistic requests and utilize the exces...
Article
Full-text available
This paper presents a novel delay aware synchrophasor recovery and prediction framework to address the problem of missing power system state variables due to the existence of communication latency. This capability is particularly essential for dynamic power system scenarios where fast remedial control actions are required due to system events or fa...
Article
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State estimation is critical to the operation and control of modern power systems.However, many cyber-attacks, such as false data injection attacks, can circumvent conventional detection methods and interfere the normal operation of grids.While there exists research focusing on detecting such attacks for DC state estimation, attack detection in AC...
Article
Full-text available
The Autonomous Vehicle (AV) is expected to be an important "building blocks" of the future smart city. Recently, an AV-based public transportation system has been successfully developed to provide precise, effective, and intelligent public transportation services. For better quality of service, the system encourages market competition by accommodat...
Article
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Fault detection is essential in microgrid control and operation, as it enables the system to perform fast fault isolation and recovery. The adoption of inverter-interfaced distributed generation in microgrids makes traditional fault detection schemes inappropriate due to their dependence on significant fault currents. In this paper, we devise an in...
Article
Full-text available
Wide area measurement system (WAMS) is one of the essential components in the future power system. To make WAMS construction plans, practical models of the power network observability, reliability, and underlying communication infrastructures need to be considered. To address this challenging problem, in this paper we propose a unified framework fo...
Article
Full-text available
Transient stability assessment is a critical tool for power system design and operation. With the emerging advanced synchrophasor measurement techniques, machine learning methods are playing an increasingly important role in power system stability assessment. However, most existing research makes a strong assumption that the measurement data transm...
Conference Paper
The electric vehicle (EV) will become one of the major forms of conveyance for ground transportation in the near future. Due to its intrinsic properties, EV seamlessly bridges the energy and mobility aspects of the smart city. Recently, the vehicular energy network (VEN) has been developed and it is capable of conveying energy over a road network b...
Article
Full-text available
Autonomous vehicles (AVs) will revolutionarize ground transport and take a substantial role in the future transportation system. Most AVs are likely to be electric vehicles (EVs) and they can participate in the vehicle-to-grid (V2G) system to support various V2G services. Although it is generally infeasible for EVs to dictate their routes, we can d...
Conference Paper
Full-text available
Due to the increasing concern for greenhouse gas emissions and fossil fuel security, electric vehicles (EVs) have attracted much attention in recent years. EVs can aggregate together constituting the vehicle-to-grid system. Coordination of EVs is beneficial to the power system in many ways. In this paper, we formulate a novel large-scale EV chargin...
Conference Paper
Full-text available
Thanks to the various advantages over conventional cars, autonomous vehicles (AVs) will take a more important role in the future transportation system. Since AVs are typically electric vehicles (EVs), they can contribute to vehicle-to-grid (V2G) services. While it is generally not feasible to dictate EV routes, we can design AV travel plans to fulf...
Article
Full-text available
Online identification of post-contingency transient stability is essential in power system control, as it facilitates the grid operator to decide and coordinate system failure correction control actions. Utilizing machine learning methods with synchrophasor measurements for transient stability assessment has received much attention recently with th...
Conference Paper
Full-text available
Due to the increasing concern on greenhouse gas emmissions and fossil fuel security, Electric Vehicles (EVs) have attracted much attention in recent years. However, the increasing popularity of EVs may cause stability issues to the power grid if their charging behaviors are uncoordinated. In order to address this problem, we propose a novel coordin...
Article
Full-text available
Economic Load Dispatch (ELD) is one of the essential components in power system control and operation. Although conventional ELD formulation can be solved using mathematical programming techniques, modern power system introduces new models of the power units which are non-convex, non-differentiable, and sometimes non-continuous. In order to solve s...