INTR - HKUST Class Schedule & Quota

COURSE INFO

PREVIOUS CODE	INTR 6000G
DESCRIPTION	Emergent innovations in autonomy, connectivity and shared mobility are revolutionizing vehicular traffic systems. Developing comprehensive and systematic understandings of traffic dynamics is essential to drive these innovations to reinvent transportation systems. The course covers different aspects of vehicular traffic flow dynamics and how to describe and simulate them with mathematical models. This course starts with how to obtain and interpret traffic flow data, the basis of any quantitative traffic modeling. The second and main part of this course introduces different approaches and models to mathematically describe vehicular traffic flow, and their application in simulation from microscopic to macroscopic level. The last part of this course introduces major applications of traffic flow theory including traffic flow management schemes, mix-autonomy traffic flow modeling and advanced control and sensing strategies by connected and automated vehicles.

PREVIOUS CODE

INTR 6000G

DESCRIPTION

Emergent innovations in autonomy, connectivity and shared mobility are revolutionizing vehicular traffic systems. Developing comprehensive and systematic understandings of traffic dynamics is essential to drive these innovations to reinvent transportation systems. The course covers different aspects of vehicular traffic flow dynamics and how to describe and simulate them with mathematical models. This course starts with how to obtain and interpret traffic flow data, the basis of any quantitative traffic modeling. The second and main part of this course introduces different approaches and models to mathematically describe vehicular traffic flow, and their application in simulation from microscopic to macroscopic level. The last part of this course introduces major applications of traffic flow theory including traffic flow management schemes, mix-autonomy traffic flow modeling and advanced control and sensing strategies by connected and automated vehicles.

INTR 5100 - Traffic Flow Theory (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6230)	Tu 01:30PM - 04:20PM	Rm 201, W1	YU, Huan	20	10	10	0

COURSE INFO

PREVIOUS CODE	INTR 6000E
DESCRIPTION	This course will introduce traffic control system concepts, components, algorithms, and tools for evaluating their effectiveness. With the instruction, assignments, and projects in this course, students are expected to learn about traffic system control devices, working principles, and popular algorithms. Additionally, the VISSIM traffic simulation package will be introduced in greater detail so that students can use it for evaluating the performance of traffic operation plans.

PREVIOUS CODE

INTR 6000E

DESCRIPTION

This course will introduce traffic control system concepts, components, algorithms, and tools for evaluating their effectiveness. With the instruction, assignments, and projects in this course, students are expected to learn about traffic system control devices, working principles, and popular algorithms. Additionally, the VISSIM traffic simulation package will be introduced in greater detail so that students can use it for evaluating the performance of traffic operation plans.

INTR 5130 - Traffic Control and Simulation (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6231)	TBA	TBA	TBA	40	0	40	0

COURSE INFO

EXCLUSION	IOTA 5003
CO-LIST WITH	IOTA 5003
DESCRIPTION	This course aims to develop students’ fundamental understanding of the application scenarios, challenges, and solutions of wireless connectivity in various systems involving autonomous things, and under possible mobility. Topics covered include fundamentals of digital communications, future wireless connectivity requirements, and various solutions to the unique challenges such as dynamic propagation environment, scalability, complexity, and heterogeneity.

INTR 5220 - Wireless Connectivity for Mobile Autonomous Things (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6232)	Tu 09:00AM - 11:50AM	Rm 102, W1	YAN, Jia YANG, Liuqing	15	12	3	0

COURSE INFO

DESCRIPTION	This course will introduce modern concepts, algorithms, and tools for data-driven transportation modeling and optimization. By taking this course, students will have the chance to master emerging data-driven methods for transportation systems modeling and optimization.

INTR 5230 - Data-driven Methods in Transportation (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6233)	Mo 01:30PM - 04:20PM	Rm 202, E4	LIANG, Yuxuan	20	11	9	0

COURSE INFO

PREVIOUS CODE	INTR 6000F
DESCRIPTION	The course aims to help students master the basic concepts and research methods of Artificial Intelligence (AI) and machine learning, understand future development trends, and lay the foundation for further research in leveraging machine learning and AI in transportation research. Through the study of this course, students will understand and master the basic concepts, ideas and methods of AI and related machine learning techniques, and initially learn and master the ability to use those machine learning techniques to solve practical problems, especially in transportation context.

PREVIOUS CODE

INTR 6000F

DESCRIPTION

The course aims to help students master the basic concepts and research methods of Artificial Intelligence (AI) and machine learning, understand future development trends, and lay the foundation for further research in leveraging machine learning and AI in transportation research. Through the study of this course, students will understand and master the basic concepts, ideas and methods of AI and related machine learning techniques, and initially learn and master the ability to use those machine learning techniques to solve practical problems, especially in transportation context.

INTR 5250 - Artificial Intelligence in Transportation (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6234)	Mo 09:00AM - 11:50AM	Rm 205, C7 Library	ZHENG, Xinhu	30	29	1	0

COURSE INFO

PREVIOUS CODE	INTR 6000D
DESCRIPTION	This course will explore the fundamental theories and methodologies of linear optimization and demonstrate how these techniques can be used to solve practical optimization problems. Two typical linear optimization techniques, linear programming and integer programming, will be introduced and discussed. The first part, linear programming, explores the simplex algorithm and the duality theory that act as the cornerstones of modern linear optimization solvers. The second part, integer programming, covers a broader range of topics in both methodology and applications, including problem modeling, model analysis, and decomposition- and relaxation-based solution methods. Implementation issues and industry cases will also be discussed.

PREVIOUS CODE

INTR 6000D

DESCRIPTION

This course will explore the fundamental theories and methodologies of linear optimization and demonstrate how these techniques can be used to solve practical optimization problems. Two typical linear optimization techniques, linear programming and integer programming, will be introduced and discussed. The first part, linear programming, explores the simplex algorithm and the duality theory that act as the cornerstones of modern linear optimization solvers. The second part, integer programming, covers a broader range of topics in both methodology and applications, including problem modeling, model analysis, and decomposition- and relaxation-based solution methods. Implementation issues and industry cases will also be discussed.

INTR 5310 - Theory and Application of Linear Optimization (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6235)	Fr 01:30PM - 04:20PM	Rm 222, W1	JIA, Shuai MA, Jun	30	21	9	0

COURSE INFO

ATTRIBUTES	[BLD] Blended learning
EXCLUSION	ROAS 5900
CO-LIST WITH	ROAS 5900
PREVIOUS CODE	INTR 5600
DESCRIPTION	The course will cover a wide range of analytical methods used in human factors research domain. The students will gain an understanding of the procedures, objectives and limitations of different research methods. The course will also include four case studies so that students would gain first-hand experience in applying the methods in real projects. These contents are required for research investigating users’ behaviors.

INTR 5330 - Analytical Methods in Human Factors Research (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6236)	Th 09:00AM - 11:50AM	Rm 237, E1	HE, Dengbo	10	0	10	0	> The class is delivered in a blended learning mode.

COURSE INFO

DESCRIPTION	Navigation is a core capability for intelligent vehicles, enabling environment perception, localization, and decision-making. This course provides a comprehensive understanding of vision-based navigation for unmanned systems, focusing on mobile robots and self-driving vehicles. It covers a wide range of topics, including multiview geometry, visual/-inertial state estimation, simultaneous localization and mapping (SLAM), place recognition, scene perception, and recent advances in embodied AI, equipping students with the theoretical and practical skills to design advanced navigation systems. Through hands-on projects using real-world datasets, students will gain experience in implementing and evaluating visual navigation solutions. Ideal for those interested in intelligent systems, computer vision, and AI, this course bridges theory and practice for autonomous systems development.

DESCRIPTION

Navigation is a core capability for intelligent vehicles, enabling environment perception, localization, and decision-making. This course provides a comprehensive understanding of vision-based navigation for unmanned systems, focusing on mobile robots and self-driving vehicles. It covers a wide range of topics, including multiview geometry, visual/-inertial state estimation, simultaneous localization and mapping (SLAM), place recognition, scene perception, and recent advances in embodied AI, equipping students with the theoretical and practical skills to design advanced navigation systems. Through hands-on projects using real-world datasets, students will gain experience in implementing and evaluating visual navigation solutions. Ideal for those interested in intelligent systems, computer vision, and AI, this course bridges theory and practice for autonomous systems development.

INTR 6000P - Visual Navigation for Intelligent Vehicles (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6239)	Mo 01:30PM - 04:20PM	Rm 205, C7 Library	CHEN, Changhao	30	30	0	0

COURSE INFO

DESCRIPTION	This course provides a comprehensive introduction to deep generative modeling with a focus on applications in intelligent transportation systems (ITS). It covers the theoretical foundations and practical implementations of modern generative models, including autoregressive models and transformer-based architectures, variational autoencoders (VAEs), generative adversarial networks (GANs), diffusion models, and flow-based models. Throughout the course, emphasis is placed on how generative models can be applied to real-world transportation problems, including mobility trajectory generation, travel demand synthesis, traffic state simulation, and scenario generation for planning and control. Students will engage in hands-on programming assignments and a semester-long project, where they develop and evaluate generative models for transportation-related datasets. By the end of the course, students will gain both theoretical understanding and practical experience in designing, implementing, and critically evaluating generative models, as well as insights into their potential for advancing data-driven intelligent transportation systems.

DESCRIPTION

This course provides a comprehensive introduction to deep generative modeling with a focus on applications in intelligent transportation systems (ITS). It covers the theoretical foundations and practical implementations of modern generative models, including autoregressive models and transformer-based architectures, variational autoencoders (VAEs), generative adversarial networks (GANs), diffusion models, and flow-based models. Throughout the course, emphasis is placed on how generative models can be applied to real-world transportation problems, including mobility trajectory generation, travel demand synthesis, traffic state simulation, and scenario generation for planning and control. Students will engage in hands-on programming assignments and a semester-long project, where they develop and evaluate generative models for transportation-related datasets. By the end of the course, students will gain both theoretical understanding and practical experience in designing, implementing, and critically evaluating generative models, as well as insights into their potential for advancing data-driven intelligent transportation systems.

INTR 6000S - Deep Generative Modeling for Intelligent Transportation Systems (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6240)	TBA	TBA	TBA	30	0	30	0	Instructor Consent Required

COURSE INFO

DESCRIPTION	With the rapid development of artificial intelligence and Internet of Things technology, swarm intelligence has become one of the fastest-growing fields in artificial intelligence. The aim of this course is to enable students to grasp the basic concepts and methods of swarm intelligence by explaining its fundamental concepts, basic methods, and the latest progress in applications. This will lay a solid foundation for students to further engage in learning and research related to artificial intelligence. The main content of the course includes the basic concepts of swarm intelligence, classic algorithms, reinforcement learning, multi-agent reinforcement learning methods, as well as examples of swarm intelligence algorithms applied in high-mobility and heterogeneous intelligent transportation systems and low-altitude networks.

DESCRIPTION

With the rapid development of artificial intelligence and Internet of Things technology, swarm intelligence has become one of the fastest-growing fields in artificial intelligence. The aim of this course is to enable students to grasp the basic concepts and methods of swarm intelligence by explaining its fundamental concepts, basic methods, and the latest progress in applications. This will lay a solid foundation for students to further engage in learning and research related to artificial intelligence. The main content of the course includes the basic concepts of swarm intelligence, classic algorithms, reinforcement learning, multi-agent reinforcement learning methods, as well as examples of swarm intelligence algorithms applied in high-mobility and heterogeneous intelligent transportation systems and low-altitude networks.

INTR 6000T - Swarm Intelligence (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6241)	Th 01:30PM - 04:20PM	Rm 102, W1	ZHANG, Rongqing	30	30	0	0

COURSE INFO

DESCRIPTION	Seminar topics presented by students, faculty and guest speakers. Students are expected to attend regularly and demonstrate proficiency in presentation in accordance with the program requirements. Graded P or F.

INTR 6800L - Seminar in Intelligent Transportation (0 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
T01 (6242)	Tu 05:00PM - 05:50PM	Rm 228, E2	CHEN, Changhao	80	73	7	0

DESCRIPTION	An independent study on selected topics carried out under the supervision of a faculty member.

INTR 6900B - Independent Study (1 unit)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
R01 (6921)	TBA	TBA	TBA	50	1	49	0	Instructor Consent Required

COURSE INFO

DESCRIPTION	Master's thesis research supervised by co-advisors from different disciplines. A successful defense of the thesis leads to the grade Pass. No course credit is assigned.

INTR 6990 - MPhil Thesis Research (0 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
R01 (6237)	TBA	No room required	TBA	80	9	71	0

COURSE INFO

DESCRIPTION	Original and independent doctoral thesis research supervised by co-advisors from different disciplines. A successful defense of the thesis leads to the grade Pass. No course credit is assigned.

INTR 7990 - Doctoral Thesis Research (0 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
R01 (6238)	TBA	No room required	TBA	120	88	32	0