[MUSIC PLAYING]
NIKOLAS GEROLIMINIS: Welcome.
Travelers experience significant delays when they perform their everyday trips
in large cities.
This course will focus on understanding urban congestion
and developing ways to improve mobility through advanced traffic management
skills.
Can we develop simple and elegant ways to explain and understand
these interactions?
The first part of this course will start from the basics
and introduce important traffic variables,
like flow, density, and speed, to describe traffic congestion.
Afterwards, we will develop elegant models to study congestion evolution.
Models are abstractions of the real systems
that relate key variables with mathematical equations
and physical intuition.
They range from very detailed ones to aggregated representations
of traffic in cities.
DIMITRIOS TSITSOKAS: Microscopic models, for example,
describe how drivers react to the actions of the driver ahead
and how they change lanes in order to improve
their path towards their destination.
ANASTASIOS KOUVELAS: Macroscopic models describe how flow and density in a road
change dynamically with the conditions that occur--
for example, a traffic signal that turns from green to red.
A queue of vehicles is developed when demand is higher than capacity.
And these queues grow with finite build, creating
what is called a traffic shock wave.
NIKOLAS GEROLIMINIS: Network-level models
is-- ignore small-scale information and describe
how congestion changes over time and space in different zones of a city.
Aggregated relations between traffic density and traffic flow
emerge in a neat way, creating what is known
as a macroscopic fundamental diagram.
RAPHAEL LAMOTTE: As new monitoring techniques emerge,
cities are gaining access to a very loud and diverse data set.
Beside the traditional load detectors that are installed in the pavement,
cities now use data from cameras, from GPS, cellphone traces, Bluetooth,
Wi-Fi, whatever.
All together, this provides a very detailed image
of the traffic evolution of a time.
ANASTASIOS KOUVELAS: If we have a good understanding of how congestion
propagates in time and space, we can design
smart control algorithms that increase the supply of our transport systems.
For example, a driver can wait for some time
on the ramp at the entrance of a highway in order
to prevent the blockades at the main line of the highway.
Similarly, adaptive traffic lights in urban environments
can manage congestion in a smart way by identifying the critical bottlenecks
of the infrastructure and forming the queues of vehicles in optimal locations
so as to maximize throughput.
NIKOLAS GEROLIMINIS: At the network level,
after partitioning a city in homogeneous regions,
we can control the intertransfers among regions, known as perimeter control,
by adopting some key traffic lights.
RAPHAEL LAMOTTE: We should also understand
why people behave the way they do--
why they chose a route, their departure time, or their mode.
We will see how individual decisions interact
and how we can use simple tools, or perhaps more complicated schemes,
to ensure that people take the decisions that
are the best for society as a whole.
DIMITRIOS TSITSOKAS: This course consists of lectures,
reading material, exercises, but also data from different case studies, that
help to understand better the different types of congestion.
NIKOLAS GEROLIMINIS: If you follow this course,
you will learn how to use simple models to explain
the causes of urban congestion.
And you will also learn how to propose elegant traffic management
schemes to alleviate congestion and improve mobility in urban networks.
ALL: Enjoy the course!