Intro to Traffic Flow Modeling and Intelligent Transport Systems

Learn how to describe, model and control urban traffic congestion in simple ways and gain insight into advanced traffic management schemes that improve mobility in cities and highways.

Description

Travelers in large cities experience significant congestion during their everyday trips. Expanding roads and infrastructure is not a long-lasting remedy to urban congestion. This course will focus on understanding traffic congestion and will explore ways to improve mobility through advanced traffic management schemes. As we all experience, congestion is a highly complex process without a single explanation. An interesting question is: “Can we describe these complex interactions with simple and elegant ways? “
 
The course will introduce the fundamentals of traffic flow theory and will describe different traffic flow models in micro- and macroscopic level. Network-level aggregated modeling and control approaches are introduced based on the concept of the Macroscopic Fundamental Diagram (MFD). Advanced traffic management schemes (such as adaptive traffic signal control, ramp metering, variable speed limits) are discussed. User equilibrium analysis is introduced through applications of route and departure time choices. Relative reading material, exercises and real data sets are provided.

What you will learn

• Understand key concepts and the physics of the transport phenomena


• Familiarize with major elements of transportation systems


• Use simple and elegant models to identify the causes of congestion


• Propose traffic management strategies to alleviate congestion


• Apply the fundamentals of transportation engineering in real case studies

Prerequisites

Analysis & Linear Algebra (1st year BSc level)

Plan

Week 1: Traffic Flow Basics
Introduction of basic traffic variables that are necessary to describe congestion (flow, density and speed) and the relations among them under equilibrium, known as fundamental diagram. Description of graphical tools such as time-space diagramsand input-output diagrams.
 
Week 2: Continuum Models of Traffic Flow
Review of the different families of traffic models (micro, meso, macro, network). Description of LWR (Lighthill-Whitham-Richards) models representing the dynamics of traffic streams through the continuity equation and an assumed equilibrium flow-density fundamental diagram.
 
Week 3: Traffic Modeling and Control for Freeway Systems
Introduction to different car following models. Description of space and time discretization and the dynamic difference equations of the macroscopic Cell Transmission Model (CTM). Discussion of the concept of ramp metering in highway traffic applications.
 
Week 4: Macroscopic Fundamental Diagram (MFD)
Introduction to network level aggregated models. These models ignore small-scale information and describe how congestion changes over time and space in different zones of a city. Relations between traffic density and traffic flow emerge in a neat way, creating what is known as a MFD. Properties and dynamic characteristics of MFD models are presented.
 
Week 5: Network-level Traffic Management
Established control techniques are introduced, with the focus being on how they can be integrated with MFD models to provide large-scale traffic control for congested networks for single- and multi-region systems.
 
Week 6: Control of Traffic Signals
Introduction to the basics of traffic signal control. Description of methods to optimize traffic light settings. Generation of fixed-time signal plans for day-of-week and time-of-day implementation. Discussion of traffic responsive systems, the semi-actuated and fully-actuated control logic, and real-time adaptive strategies. Variable speed limits (VSL) are discussed as a measure to improve mobility in highway networks.
 
Week 7: Equilibria in Transportation
Introduction to the concept of User Equilibrium and the way it can be used to predict and steer behavioral adjustments following long lasting perturbations. The fundamental concepts and assumptions required for such analyses are explained and illustrated with two applications: route and departure time choice.