 ElaborateCourse: Transport System Analysis

I. Course Introduction (including teaching goals and requirements):

This course focuses on how to model transportation systems based on quantitative analysisof transportation problems. The optimal traffic decision-making schemes areobtained based on data analysis by establishing, testing and solvingmathematical model. Traffic engineering thoughts and management thoughts arecombined and systematic, scientific and mathematical analysis are applied during this process.

This course aims to develop students’ understanding of the models used to support decisionsabout the planning or operation of the transportation system. The emphasis ison strategic network models which are used for longer term network planning andmicrosimulation models which focus on operational considerations. The traditional four step models of trip generation, mode choice and traffic assignment are considered in detail based on some optimization methods. Traffic simulation is also an important component of decision support for transport management in this course.

II. Teaching Syllabus (including the content of chapters and sections. A sheet can beattached):

Introduction:

Course introduction

Background of transport system analysis

Connotation of the four-step method

Complexity of public transport system

Function of traffic analytical models and trafficsimulation

Trip Generation:

Growth factor method

Regression analysis

Category analysis

Trip Distribution:

Uniform growth factor model: single/doubleconstrained growth factor model

Gravity model: classical version of gravity model

Mode Choice:

Influencing Factors and characteristics

Aggregate Mode Choice: Entropy-MaximizationApproach

Disaggregate Mode Choice: Discrete Choice Model

Discrete Choice Model:

Random utility theory

Binary choice model: linear probability model,binary probit model, binary logit model

Nested logit model

Multinomial choice theory: multinomial logit model,multinomial probit model

Activity-Based Models:

State-of-the-art

Development of activity-based models

Case study

Mathematical Tools:

Nonlinear programming: convex programming,Karush-Kuhn-Tucker conditions

Solution algorithms: line search methods, gradientdescent method, Frank-Wolfe method

Traffic Assignment:

User equilibrium, Beckmann’s transformation,all-or-nothing traffic Assignment, shortest path algorithms

System optimum, marginal-cost pricing principle

Stochastic user equilibrium, probit-based SUE,demand elasticity

Data Driven Analysis:

Data acquisition: focus on transport systemoptimization

Statistics theory and applications in transportsystem analysis

Frequently-used algorithm in big data analysis

Public Transport:

Transit network and modelling process

Operations and service planning

Transport Management:

Structure and management of transport organization

Transport economics

Traffic Simulation:

Basics in simulation

Procedures of simulation

Research Frontiers:

Network reliability

Travel time uncertainty

Applications of GIS:

Concept of GIS

Conduct projects using ArcGIS

III. TeachingSchedule:

 Week Course Content Teaching Method 1 Course  Introduction Lecture 2 Four  Step Models:  Trip Generation Lecture 3 Four  Step Models: Trip Distribution Lecture 4 Four  Step Models: Mode Choice Lecture 5 Discrete  Choice Model Lecture 6 Activity-Based  Models Lecture 7 Licensing and when  are they employed Lecture 8 Four  Step Models: Traffic Assignment I Lecture 9 Mathematical  Tools and Solution Algorithm Lecture 10 Four  Step Models: Traffic Assignment II Lecture 11 Modelling  for Network Design and Management Lecture 12 Data-driven  Analysis Lecture 13 Public  Transport Lecture 14 Transport  Management Lecture 15 Traffic  Simulation Lecture 16 Research  Frontiers Lecture 17 Applications  of GIS Lecture & Seminar 18 Project  Presentation Exam 