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INTR

Intelligent Transportation

INTR 5100
Traffic Flow Theory
[3-0-0:3]
Previous Course Code(s)
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.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Develop systematic knowledge of traffic flow modelling from microscopic Ordinary Differential Equation models to macroscopic Partial Differential Equations and their variations.
- 2.
  Use traffic flow modeling to describe, analyze and simulate classical traffic phenomena.
- 3.
  Have a data-based overview of the phenomenology of traffic flow dynamics and explore a set of data collection, processing and analytics methods.
- 4.
  Explore the novel applications of the methods and models of traffic flow dynamics that range from traffic-state estimation, emission models and ITS applications.
- 5.
  Grasp mathematical analysis skills, with a particular focus in traffic flow mathematical models.
- 6.
  Strengthen programming skills, with a particular focus in numerical simulations of traffic flow systems.
INTR 5110
Urban Transportation Network Modeling
[3-0-0:3]
Description
This course discusses formulations and algorithms of finding equilibrium flow patterns through transportation networks to assess the network performance and providing foresights into the future dynamics, then explores policies, designs, and strategies to improve the system performance. Emphasis is placed on the understanding of the paradigm of equilibrium analysis of transportation systems. Topics discussed in the class include static user equilibrium, system optimum, market- based instruments for congestion management, bi-level programming models, bottleneck models dynamic network modeling, and the implications of emerging technologies on the analyses of transportation networks.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Analyze the network traffic flow problem based on the user equilibrium concept.
- 2.
  Look at choice of travel modes, the distribution of trips among various possible destinations, and the choice of route between an origin and a destination in congested urban transportation networks.
- 3.
  Apply solution algorithms for traffic impact study in a transportation network through a course project.
- 4.
  Understand the formulation methods and common solution techniques, and interpret model results of network traffic flow problems.
INTR 5120
Optimization Methods for Transport and Logistics Management
[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.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Have an overview of important optimization problems arising from transport and logistics management.
- 2.
  Model and solve various network flow problems, such as transportation, shortest path, maximum flow, minimum cost flow, and multi-commodity flow problems.
- 3.
  Model and solve various routing and scheduling problems, such as traveling salesman, vehicle routing, and service network design problems.
- 4.
  Model and solve transport and logistics management problems with uncertain data.
- 5.
  Conduct preliminary research in the field of transport and logistics operations.
- 6.
  Professionally present a research project.
INTR 5130
Traffic Control and Simulation
[3-0-0:3]
Previous Course Code(s)
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.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Understand components of an event-driven simulation system.
- 2.
  Build simulation models using VISSIM or SUMO.
- 3.
  Design signal timing plans for pre-timed/actuated traffic control intersections.
- 4.
  Apply advanced traffic signal control technologies
- 5.
  Design and evaluate ramp metering strategies.
- 6.
  Be aware of emerging technologies that may affect traffic system operations.
INTR 5200
Emerging Mobility and Multimodal Freight Systems
[3-0-0:3]
Description
Intelligent Transportation Systems (ITS) apply a variety of technologies to monitor, evaluate, and manage transportation systems to enhance their efficiency, safety, and sustainability. This postgraduate-level course introduces the basic functional components in ITS and how they are designed and operated to manage multi-modal transportation systems., including both passenger and freight transportation systems and infrastructure. The course topics cover the following three main parts: (i) Emerging vehicle technologies and mobility services, data management, and institutional issues; (ii) Multi-modal freight operations and management in road, rail, maritime and inter-modal systems; (iii) Analytical methods including fundamentals of system analysis, risk and regression analysis, network flow optimization and algorithm design, etc.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Have an overview of the basic components, their functionalities, and applications of intelligent transportation systems (ITS).
- 2.
  Understand the increasing significance of ITS and emerging technologies in transportation planning and management.
- 3.
  Utilize microeconomics and optimization methods to conduct transportation systems analysis.
- 4.
  Develop basic knowledge to conduct revenue and data management in ITS projects.
- 5.
  Professionally present a research project, in the flow of introduction to the research question, the analysis method, solution approaches, outcomes and contributions.
INTR 5210
Game Theoretical Methods in Transportation
[3-0-0:3]
Previous Course Code(s)
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.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Learn the construction of games under different environments and representations.
- 2.
  Understand equilibrium concepts, such as Nash equilibrium, Bayesian Nash equilibrium, Subgame perfect equilibrium, perfect Bayesian equilibrium and sequential equilibrium, and their compatibility with game settings and representations.
- 3.
  Establish compatible game theoretical models and find equilibrium solutions for simple problems.
- 4.
  Know the basics of variational inequality, and its connection with optimization and equilibrium.
- 5.
  Understand the consistency of Nash equilibrium and User equilibrium in static traffic assignment.
- 6.
  Solve Nash equilibrium for network games using variational inequality.
- 7.
  Construct suitable mechanisms or allocation rules for simple mechanism design problems in transportation.
- 8.
  Identify the game theoretical models used in transportation research papersand evaluate their suitability for solving the transportation problems.
INTR 5220
Wireless Connectivity for Mobile Autonomous Things
[3-0-0:3]
Co-list with
IOTA 5003
Exclusion(s)
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.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Grasp the fundamentals of digital communications.
- 2.
  Understand the requirements of connected autonomous things.
- 3.
  Learn the dynamics associated with mobile autonomous things.
- 4.
  Analyze and evaluate wireless connectivity solutions under dynamics.
- 5.
  Learn the scalability and complexity issues associated with mobile autonomous things.
- 6.
  Analyze and evaluate wireless connectivity solutions addressing scalability and complexity issues.
- 7.
  Learn the heterogeneity associated with mobile autonomous things.
- 8.
  Analyze and evaluate wireless connectivity solutions accommodating heterogeneity.
INTR 5230
Data-driven Methods in Transportation
[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.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Understand the foundations of data-driven methods.
- 2.
  Develop data design, data cleaning, data processing, and data analysis capabilities.
- 3.
  Apply optimization, supervised learning, and reinforcement learning methods for traffic modeling.
- 4.
  Code, apply and solve the data-driven transportation problems using Python.
- 5.
  Interpret and visualize data-driven models and make connections to applications.
INTR 5240
The Principle and Application of Intelligent Connected Vehicle
[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.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Have an overview of intelligent connected vehicle (ICV) and its related concepts.
- 2.
  Understand fundamental frameworks and methodologies for vehicular communications.
- 3.
  Address the key challenges and limitations of V2V, V2I, V2X.
- 4.
  Understand ICV applications in transportation.
- 5.
  Professionally present a research project, in the flow of introduction to the research question, the analysis method, solution approaches, outcomes and contributions.
INTR 5250
Artificial Intelligence in Transportation
[3-0-0:3]
Previous Course Code(s)
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.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Have an overview of Artificial Intelligence (AI) and its related concepts.
- 2.
  Understand critical fundamental algorithms for AI applications, including problem solving and uncertain reasoning.
- 3.
  Explore a set of machine learning techniques related to AI, including Reinforcement Learning, Bayesian Networks, Naïve Bayes, Neural Networks.
- 4.
  Understand AI implementation in transportation.
- 5.
  Develop skills to apply AI techniques in transportation scenarios.
- 6.
  Professionally present a research project, in the flow of introduction to the research question, the analysis method, solution approaches, outcomes and contributions.
INTR 5260
Engineering Psychology and Transportation Applications
[3-0-0:3]
Previous Course Code(s)
INTR 6000B
Co-list with
ROAS 5910
Exclusion(s)
ROAS 5910
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.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Understand basic characteristics and limitations of human beings.
- 2.
  Acquire techniques in analyzing road/traffic control design and machine design from human factors point of view.
- 3.
  Understand common collision types on road and corresponding commonly used countermeasures.
- 4.
  Understand characteristics and causes of driver impairments and countermeasures.
- 5.
  Understanding the human factors challenges in the context of driving automation and connected vehicles technologies.
- 6.
  Design and conduct experiments to collect data from human subjects and analyze the data.
- 7.
  Write written reports and orally present the findings in the experiments.
INTR 5300
Nonlinear Control Systems
[3-0-0:3]
Description
This 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.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Develop an overview knowledge of theory and methods for nonlinear dynamical systems.
- 2.
  Grasp mathematical analysis skills to describe, model and interpret nonlinear phenomenon that arise in various engineering applications.
- 3.
  Apply advanced analysis and control design tools for nonlinear dynamical systems.
- 4.
  Master skills of independently assessing the advantages and disadvantages of different nonlinear methods.
- 5.
  Cultivate a rigorous way of research methodology that ensures valid and reliable solutions to engineering problems.
- 6.
  Strengthen their ability to contribute innovative thinking in various research areas.
INTR 5310
Theory and Application of Linear Optimization
[3-0-0:3]
Previous Course Code(s)
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.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Develop a solid foundation of linear programming theory.
- 2.
  Apply linear programming to model decision optimization problems.
- 3.
  Have a basic understanding of the principles, concepts, and techniques of integer programming.
- 4.
  Apply integer programming to model optimization problems with discrete decisions.
- 5.
  Conduct preliminary research in the field of linear and integer programming.
- 6.
  Professionally present a research project.
INTR 5320
Incremental Learning and Adaptive Signal Processing
[3-0-0:3]
Co-list with
IOTA 5108
Exclusion(s)
IOTA 5108
Description
This course aims to develop students’ fundamental understanding of the theory and application of incremental learning and adaptive signal processing. Topics covered in this course include Wiener filter, least mean squares (LMS), recursive least squares (RLS), the Kalman filter, classification, parameter learning, neural network and deep learning.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Understand the basic concepts and procedures of incremental learning.
- 2.
  Learn how to derive the optimal linear solutions for many applications such as channel equalization, beamforming, etc.
- 3.
  Understand common incremental algorithms such as Kalman filter, least mean squares (LMS), recursive least squares (RLS), etc.
- 4.
  Learn how to solve classification problems with incremental learning techniques.
- 5.
  Learn non-linear learning techniques such as neural networks.
- 6.
  Understand the parameter learning process and learn typical algorithms for parameter learning.
INTR 5330
Analytical Methods in Human Factors Research
[3-0-0:3]
Previous Course Code(s)
INTR 5600
Co-list with
ROAS 5900
Exclusion(s)
ROAS 5900
Background
Previous coursework in Probability and Statistics, including knowledge of estimation, confidence intervals, and hypothesis testing and its use in at least one and two sample problems. Some familiarity with Calculus and Linear Algebra.
Mode of Delivery
[BLD] Blended learning
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.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Understand commonly used models of human beings.
- 2.
  Acquire techniques in analyzing humans’ behaviors.
- 3.
  Understand commonly used statistical models for analyzing experimental data.
- 4.
  Gain first-hand experience in designing experiments in human factors area and applying the techniques in research projects.
- 5.
  Understand how to present research processes and results (either orally or in writing) in a scientific way.
INTR 5400
Logistics Systems Analysis: Modeling, Optimization and Algorithms
[3-0-0:3]
Description
This 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.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Understand essential concepts and philosophy of mathematical modeling and optimization of transportation systems.
- 2.
  Grasp the methodology and techniques to formulating and solving a transportation problem.
- 3.
  Get familiar with mathematical optimization techniques and the state-of-the-art research topics.
- 4.
  Gain research experience through term projects by identifying and solving scientific problems.
- 5.
  Practice skills for team collaboration and presentation through term projects.
INTR 5500
Multi-modal Freight Transportation System and Infrastructure
[3-0-0:3]
Description
This course aims to introduce multi-modal (rail, road, waterway, etc.) freight transportation operations and infrastructure systems. It comprises four inter-connected parts: 1) introduce basic modal-specific concepts and industry development; 2) explain widely used modeling techniques used in the multi-modal and inter-modal freight systems; 3) introduce transportation infrastructure management for different modes; and 4) apply the methodologies to emerging high-profile transportation research topics (e.g., resilience planning) through a term project.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Understand essential concepts and engineering knowledge of multi-modal transportation systems (operation and infrastructure).
- 2.
  Grasp the methodology and techniques to formulating and solving a multi-modal transportation problem.
- 3.
  Get familiar with high profile topics and issues in the industry techniques and the state-of-the-art research.
- 4.
  Gain research experience through term projects by identifying and solving scientific problems.
- 5.
  Practice skills for team collaboration and presentation through term projects.
INTR 6000
Special Topics in Intelligent Transportation
[3-0-0:3]
Description
Selected topics in intelligent transportation of current interest in emerging areas and not covered by existing courses. May be repeated for credit if different topics are covered.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Identify the knowledge of the selected topics in the field of intelligent transportation.
INTR 6800
Seminar in Intelligent Transportation
[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.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Demonstrate effective presentation skills in seminar presentations.
- 2.
  Understand current research trends and applications.
- 3.
  Engage argumentative and critical thinking skills.
INTR 6900
Independent Study
[1-3 credit(s)]
Description
An independent study on selected topics carried out under the supervision of a faculty member.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Demonstrate mastery of the knowledge and skills in the selected topics related to Intelligent Transportation.
- 2.
  Apply an interdisciplinary approach in examining the selected topics.
- 3.
  Critically evaluate different aspects of the selected topics.
- 4.
  Communicate findings effectively in written reports.
INTR 6990
MPhil Thesis Research
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.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Design, develop and conduct cross-disciplinary research in Intelligent Transportation.
- 2.
  Communicate research findings effectively in written and oral presentations.
INTR 7990
Doctoral Thesis Research
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.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Design, develop and conduct crossdisciplinary research in Intelligent
  Transportation.
- 2.
  Communicate research findings effectively in written and oral
  presentations.

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