References

Our research-driven approach has attracted top
cities to contract us for high-resolution traffic
modeling.

London

Changes to public spaces, e.g. new bike lanes require detailed know-how
about what will happen to traffic demand and congestion.

Problem statement & data

Transport for London is the government
body for most of London’s transport
network.

During the COVID-19 pandemics, London saw
changes in traffic performance between 2019 and
2020. In collaboration with TU Munich, we examined a
high-resolution data set of more than 11’000 loop
detectors and thousands of traffic cameras covering a
period of 2 years.

The image shows the vehicle
flow (every 3min) for a lane in
London. Our data analytics
revealed a decrease in flows
from 2019 to 2020 can be
observed.

Approach

Our automated pipeline generated a
digital road network and directly
matched the available data as well
as the changes in speed policy, lane
management, etc.

We then estimated average traffic demands for 2019 and 2020. Thanks to our endogenous demand estimation module we only required loop detector measurements to predict the origin-destination matrix.

Origin-Destination estimation
for different regions in the
greater London area. We can
estimate these relations using
loop detector counts only.

Result

Our approach disentangled the main drivers
in the complex interplay between
transportation demand and supply.

This allowed us to control for the different changes in
the network and demand and discuss the traffic
performance in Greater London.

Zurich

We evaluate the short- and long-term effects of construction sites.

Problem statement & data

According to several international rankings
the city of Zurich is at the forefront of
becoming a smart city.

Our ongoing project with the city consists of an analysis
for construction sites in the North of the city. The city has
roughly 1500 loop detectors which offered high
resolution insights over a 4-year period.

Visualization of our simulation
output

Approach

Our automated pipeline generated a
large-scale city-wide traffic
simulation including public
transportation, bicycles, as well as
car traffic.

We focus on an extended morning peak between 05:00-09:00. Transportation demand was estimated using our endogenous demand estimation which only requires loop detector measurements to predict the origin-destination matrix.

Comparison of the average of
empirically measured flows in
6 districts in the city of Zurich
and our simulation
(comparison every 3min).

Result

Our simulation offers very high accuracy,
with less than 5% deviation in the measured
flows (over 15min periods), and 85% of all
monitored roads showed a very good value
of below 3.8 for the international standard
GEH statistic.

We are now working on the real-time prediction of
congestion, so that our simulation truly transforms into
a digital twin.

References

London

Changes to public spaces, e.g. new bike lanes require detailed know-how
about what will happen to traffic demand and congestion.

Problem statement & data

Transport for London is the government
body for most of London’s transport
network.

Approach

Our automated pipeline generated a
digital road network and directly
matched the available data as well
as the changes in speed policy, lane
management, etc.

We then estimated average traffic demands for 2019 and 2020. Thanks to our endogenous demand estimation module we only required loop detector measurements to predict the origin-destination matrix.

Result

Our approach disentangled the main drivers
in the complex interplay between
transportation demand and supply.

Zurich

We evaluate the short- and long-term effects of construction sites.

Problem statement & data

According to several international rankings
the city of Zurich is at the forefront of
becoming a smart city.

Approach

Our automated pipeline generated a
large-scale city-wide traffic
simulation including public
transportation, bicycles, as well as
car traffic.

We focus on an extended morning peak between 05:00-09:00. Transportation demand was estimated using our endogenous demand estimation which only requires loop detector measurements to predict the origin-destination matrix.

Result

Our simulation offers very high accuracy,
with less than 5% deviation in the measured
flows (over 15min periods), and 85% of all
monitored roads showed a very good value
of below 3.8 for the international standard
GEH statistic.

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References

London

Changes to public spaces, e.g. new bike lanes require detailed know-how about what will happen to traffic demand and congestion.

Problem statement & data

Transport for London is the government body for most of London’s transport network.

Approach

Our automated pipeline generated a digital road network and directly matched the available data as well as the changes in speed policy, lane management, etc.

We then estimated average traffic demands for 2019 and 2020. Thanks to our endogenous demand estimation module we only required loop detector measurements to predict the origin-destination matrix.

Result

Our approach disentangled the main drivers in the complex interplay between transportation demand and supply.

Zurich

We evaluate the short- and long-term effects of construction sites.

Problem statement & data

According to several international rankings the city of Zurich is at the forefront of becoming a smart city.

Approach

Our automated pipeline generated a large-scale city-wide traffic simulation including public transportation, bicycles, as well as car traffic.

We focus on an extended morning peak between 05:00-09:00. Transportation demand was estimated using our endogenous demand estimation which only requires loop detector measurements to predict the origin-destination matrix.

Result

Our simulation offers very high accuracy, with less than 5% deviation in the measured flows (over 15min periods), and 85% of all monitored roads showed a very good value of below 3.8 for the international standard GEH statistic.

Follow Us

Follow Us

Site Map

Get in Touch

Follow Us

Site Map

Changes to public spaces, e.g. new bike lanes require detailed know-how
about what will happen to traffic demand and congestion.

Transport for London is the government
body for most of London’s transport
network.

Our automated pipeline generated a
digital road network and directly
matched the available data as well
as the changes in speed policy, lane
management, etc.

Our approach disentangled the main drivers
in the complex interplay between
transportation demand and supply.

According to several international rankings
the city of Zurich is at the forefront of
becoming a smart city.

Our automated pipeline generated a
large-scale city-wide traffic
simulation including public
transportation, bicycles, as well as
car traffic.

Our simulation offers very high accuracy,
with less than 5% deviation in the measured
flows (over 15min periods), and 85% of all
monitored roads showed a very good value
of below 3.8 for the international standard
GEH statistic.