 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This course will introduce important optimization problems arising from transport and logistics management, including network flow problems, routing and scheduling problems, and problems involving uncertainty. It will focus on modeling techniques and solution methodology for problem solving. Theoretical and operational insights into the problems will also be discussed. The goal of the course is to train students to a level of technical competency to formulate and solve related optimization problems that they may encounter in both research and real-life. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6140) | Fr 01:30PM - 04:20PM | Rm 101, E4 | JIA, Shuai | 30 | 8 | 22 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| PREVIOUS CODE | INTR 6000C |
| DESCRIPTION | This postgraduate-level course introduces how game-theoretical methods are used to model strategic behaviors and to support decision making in transportation systems. Fundamental knowledge in game theory and mechanism design, including different game representations, equilibrium concepts and information asymmetry will first be covered. Variational inequality will then be introduced, with an emphasize of its importance in determining equilibrium solutions for transportation network models. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6141) | Th 01:30PM - 04:20PM | Rm 202, W4 | SUN, Xiaotong | 40 | 9 | 31 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | Intelligent connected vehicles (ICVs) are believed to change people’s life in the near future by making the transportation safer, cleaner and more comfortable. Although many prototypes of ICVs have been developed to prove the concept of autonomous driving and the feasibility of improving traffic efficiency, there still exists a significant gap before achieving mass production of high-level ICVs. This course aims to present an overview of both the state of the art and future perspectives of key technologies that are needed for future ICVs. Through the study of this course, students will understand and master the basic concepts, key technologies and applications of ICV, and initially learn and master the ability to use that knowledge to solve practical problems, especially in cross-disciplinary communication and transportation context. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6142) | Mo 09:00AM - 11:50AM | Rm 102, E1 | ZHENG, Xinhu | 30 | 30 | 0 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| EXCLUSION | ROAS 5910 |
| CO-LIST WITH | ROAS 5910 |
| PREVIOUS CODE | INTR 6000B |
| DESCRIPTION | The course will cover a wide range of engineering psychology topics as well as how the research in these directions can affect policies and regulations in vehicle design and surface transportation. The students will gain an understanding of the characteristics and limitations of human beings from engineering psychology perspectives of view and how the design of traffic control devices, the roadway, the in-vehicle devices, regulations and traffic rules can be affected by the research in these directions. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6143) | We 09:00AM - 11:50AM | Room 521H VR Room, W1 | HE, Dengbo | 15 | 6 | 9 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| PREVIOUS CODE | INTR 6000J |
| DESCRIPTION | As urbanization continues to grow, there is an increasing demand for data-driven solutions to address urban challenges, such as transportation, environment, climate, and public health. This interdisciplinary course aims to provide postgraduate students with a comprehensive understanding of spatio-temporal data mining concepts, techniques, and their applications in urban computing scenarios. The course will cover topics including data sources, modeling techniques, analytics, and advanced topics, with hands-on exercises using real-world datasets. Targeting students from various disciplines like computer science, transportation, urban planning, geography, and environmental science, the course will promote collaboration and knowledge exchange. By equipping students with the necessary skills to analyze, model, and visualize urban spatio-temporal data, this course will contribute to the development of a new generation of professionals adept at harnessing the power of spatio-temporal data to create smarter, more sustainable cities and foster interdisciplinary research in smart cities and beyond. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6146) | TBA | TBA | TBA | 20 | 0 | 20 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This course introduces the principles and algorithms of Simultaneous Localization and Mapping (SLAM), which is widely used in intelligent transportation and robotics. Students will learn how to reconstruct 3D maps of environments and track the positions of mobile agents simultaneously. Practical applications of SLAM in autonomous vehicles, drones, and smart manufacturing will be explored through hands-on projects. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6147) | Mo 07:00PM - 09:50PM | Rm 202, E3 | LI, Haoang | 25 | 25 | 0 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | Harness the power of mobile devices to revolutionize transportation with this postgraduate course. As mobile sensing and computing become increasingly integral to our understanding of the physical world, this postgraduate-level course provides a comprehensive look at their transformative impact on urban transportation. Students will gain a solid grasp of the fundamental concepts and cutting-edge techniques in mobile sensing and computing, enabling the development of intelligent transportation systems and applications. Key topics include mobile device sensing, computing, and communication fundamentals, as well as advanced discussions on mobile computing enabled transportation management, localization and tracking, mobile crowdsensing paradigm, social sensing, vehicular mobile systems, and mobile intelligence. This course is designed for students and professionals seeking to leverage mobile technology for innovative solutions in transportation. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6148) | Tu 03:00PM - 05:50PM | Rm 134, E1 | LIU, Zhidan | 30 | 5 | 25 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This course explores how emerging technologies—such as AI, IoT, robotics, and data analytics—are transforming modern production and operations management. Students will learn to design, analyze, and optimize intelligent production systems that enhance efficiency, flexibility, and sustainability. Topics include smart manufacturing systems, digital twins, human–robot collaboration, supply chain integration, and data-driven decision-making. Through case studies and practical projects, students gain both theoretical foundations and hands-on experience in managing operations within Industry 4.0 environments. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6151) | Tu 07:00PM - 09:50PM | Rm 150, E1 | MA, Jun | 30 | 4 | 26 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | Autonomous vehicles (AVs) integrate perception, planning, and control to enable intelligent driving. This course introduces fundamental models and testing methodologies for evaluating AV systems. The first part covers traditional modular architectures, including sensing, perception, trajectory planning, and control algorithms. The second part focuses on emerging end-to-end learning frameworks that directly map sensor inputs to driving actions, emphasizing data processing, training, and safety evaluation. The course concludes with an overview of connected and automated vehicle (CAV) extensions that enhance perception and coordination through communication technologies. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6152) | Th 09:00AM - 11:50AM | Rm 102, W1 | MA, Ke | 30 | 8 | 22 | 0 |
| VECTOR | [0-1-0:0] |
|---|---|
| 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. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| T01 (6149) | Th 05:00PM - 05:50PM | Rm 101, E1 | MA, Jun | 80 | 55 | 25 | 0 |
| 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. |
|---|
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| R01 (6013) | TBA | No room required | TBA | 80 | 11 | 69 | 0 |
| 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. |
|---|
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| R01 (6015) | TBA | No room required | TBA | 120 | 68 | 52 | 0 |