 |
| 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 (6112) | Tu 09:00AM - 11:50AM | Rm 101, W4 | SUN, Xiaotong | 40 | 10 | 30 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| 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. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6114) | Mo 06:00PM - 08:50PM | Rm 202, W4 | LIANG, Yuxuan | 40 | 14 | 26 | 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 (6123) | Mo 09:00AM - 11:50AM | Rm 223, W1 | ZHENG, Xinhu | 30 | 16 | 14 | 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 (6125) | Fr 09:00AM - 11:50AM | Rm 222, W1 | HE, Dengbo | 15 | 3 | 12 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| PREVIOUS CODE | INTR 6000K |
| DESCRIPTION | Driven by recent advancements in artificial intelligence (AI) and mobile edge computing (MEC), edge intelligence has emerged as a promising paradigm to support AI model training and inference over wireless networks. This postgraduate-level course aims to equip students with an understanding of edge intelligence from both communication and computing perspectives. The course begins by laying the groundwork with the fundamentals of wireless communications and mobile networks, followed by an exploration of the concepts and key technologies underpinning MEC. Building on this foundation, federated edge learning will be introduced, including its system modeling, convergence analysis, differential privacy mechanisms, and the integration of communication and learning design. Lastly, the course delves into device-edge co-inference techniques and the applications of edge intelligence in transportation, such as infrastructure-vehicle cooperative autonomous driving. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6127) | Tu 03:00PM - 05:50PM | Rm 101, W4 | YAN, Jia | 30 | 7 | 23 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This course introduces students to System Dynamics and its real-world applications. System Dynamics studies the relationships between different parts of a system and how these relationships influence system behavior over time based on modeling and simulations. System Dynamics has been used globally to develop business and government policies and strategies based on an understanding of a system's internal structure and how the system responds to interventions. In this course, students learn how to use simulation software Vensim for System Dynamics modeling. This course also discusses state-of-art applications of System Dynamics in new technology diffusion (such as alternative-fuel vehicles) , policy-making (such as climate change mitigation and environmental sustainability policies), technology management (such as energy management), and other real-world contexts. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6136) | Tu 06:00PM - 08:50PM | Rm 202, W1 | WEI, Wei | 20 | 6 | 14 | 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 (6137) | Th 09:00AM - 11:50AM | Rm 223, W1 | LIU, Zhidan | 30 | 8 | 22 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | As research related to the low-altitude economy becomes popular, the high-precision and low-cost localization of drones has garnered widespread attention. This course will systematically introduce drone localization methods based on visual and laser information, and analyze the differences and connections between various schemes. By taking this course, students will be equipped with the ability to develop a complete drone localization system and apply it in various scenarios such as logistics and inspection. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6536) | Fr 03:00PM - 05:50PM | Rm 101, W2 | HE, Jinhao LI, Haoang | 20 | 11 | 9 | 0 | Only open to the students conducting research related to low-altitude economy and drones |
| 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 (6138) | Mo 01:30PM - 02:20PM | Rm 102, W4 | LIANG, Yuxuan | 80 | 54 | 26 | 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 (6132) | TBA | No room required | TBA | 60 | 27 | 33 | 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 (6133) | TBA | No room required | TBA | 70 | 48 | 22 | 0 |