COURSE INFO
VECTOR[3-0-0:3]
PREVIOUS CODEINTR 6000C
DESCRIPTIONThis 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.

INTR 5210 - Game Theoretical Methods in Transportation (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6121) We 03:00PM - 05:50PMRm 150, E1SUN, Xiaotong3011190 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONThis 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)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6116) Tu 09:00AM - 11:50AMRm 149, E1ZHU, Meixin3016140 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONIntelligent 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.

INTR 5240 - The Principle and Application of Intelligent Connected Vehicle (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6115) 22-JAN-2024 - 14-MAR-2024
Mo 09:00AM - 11:50AM		Rm 102, E1ZHENG, Xinhu405350 
15-MAR-2024 - 10-MAY-2024
Mo 09:00AM - 11:50AM		Rm 105, E3ZHENG, Xinhu
COURSE INFO
VECTOR[3-0-0:3]
EXCLUSIONROAS 5910
CO-LIST WITHROAS 5910
PREVIOUS CODEINTR 6000B
DESCRIPTIONThe 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.

INTR 5260 - Engineering Psychology and Transportation Applications (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6119) We 09:00AM - 11:50AMRm 134, E1HE, Dengbo3012180 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONThis course introduces methods for analysis and control design of nonlinear systems, which have a wide range of engineering applications including transportation, robotics, biology, energy, and manufacturing systems. The course includes: 1) Mathematical models of nonlinear systems, and fundamental differences between the behavior of linear and nonlinear systems, equilibrium, limit cycles and general invariant sets. 2) Phase plane analysis, Lyapunov stability, Input-to-state stability, Input-output stability, and approximation methods. 3) Feedback linearization and nonlinear control design tools, including Lyapunov-based control and Backstepping. From learning the nonlinear phenomena to understanding the mathematical properties and then analyzing system behaviors, students will be able to grasp the fundamental concepts and advanced tools that are useful in the analysis of nonlinear systems. The control design tools for nonlinear systems from feedback linearization to advanced backstepping control are covered in this course. Students will be proficient in skills of independently assessing the advantages and disadvantages of different nonlinear methods, make a qualified choice of method for analysis and design of nonlinear control systems that arise from various research areas.

INTR 5300 - Nonlinear Control Systems (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6117) 22-JAN-2024 - 07-APR-2024
Mo 03:00PM - 05:50PM		Rm 148, E1YU, Huan308220 
08-APR-2024 - 10-MAY-2024
Mo 03:00PM - 05:50PM		Rm 201, W2YU, Huan
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONThis postgraduate level course aims to introduce practical modeling methods based on theories and principles in applied mathematics, operations research, and management science for solving the planning, design and evaluation of complex transportation systems, including both passenger logistics and freight distribution systems. This course will cover the three major aspects: inventory management, network design and flow optimization, as well as facility location in a logistical system. It introduces fundamental concepts, model formulation, optimization techniques, as well as solution algorithms (including stochastic process, network/graphic representation, classic OR problems, formulation of optimization problems, exact solution methods, meta heuristics, continuous approximation, etc.). It will also cover practical solution approaches that reduce cumbersome details of transportation systems into models with a manageable number of parameters and decision variables. A variety of perspectives and techniques to both classic problems and recent advances will be presented along with ways to compare their performance.

INTR 5400 - Logistics Systems Analysis: Modeling, Optimization and Algorithms (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6120) 22-JAN-2024 - 03-APR-2024
Th 09:00AM - 11:50AM		Rm 148, E1BAI, Yun407330 
04-APR-2024 - 10-MAY-2024
Th 09:00AM - 11:50AM		Rm 201, E3BAI, Yun
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONAs 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 graduate 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, transporation, 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 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.

INTR 6000J - Spatio-Temporal Data Mining and Analytics (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6118) Tu 06:00PM - 08:50PMRm 102, W1LIANG, Yuxuan308220 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONDriven 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.

INTR 6000K - Introduction to Mobile Edge Intelligence: The Communication and Computing Perspectives (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6180) Tu 03:00PM - 05:50PMRm 150, E1YAN, Jia3010200 
COURSE INFO
VECTOR[0-1-0:0]
DESCRIPTIONSeminar 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 6800G - Seminar in Intelligent Transportation (0 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
T01 (6189) We 01:30PM - 02:20PMRm 134, E1ZHU, Meixin7058120 
COURSE INFO
VECTOR[1-3 credit(s)]
DESCRIPTIONAn independent study on selected topics carried out under the supervision of a faculty member.

INTR 6900A - Independent Study (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
R01 (6370) TBATBATBA10190 
COURSE INFO
DESCRIPTIONMaster'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)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
R01 (6122) TBATBATBA409310 
COURSE INFO
DESCRIPTIONOriginal 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)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
R01 (6123) TBATBATBA6029310