Applications

Our automated pipeline quickly creates high-
resolution transportation simulations which can be
used for a versatile set of complex applications.

Group 123

02 Data-driven traffic prediction

Data sparsity is a thing of the
past. Today, we have access to
massive amounts of data from
multiple sources.

Advanced data analytics and AI
algorithms help us to analyze these data
to unveil the hidden patterns of complex
urban traffic systems and predict its
future state. While every city is unique,
the congestion propagation mechanism is
more or less similar. By integrating
knowledge, you can get the most out of
your data.

Group 126(1)

04 Design for future shared, electric and autonomous mobility

Within the past few years, cities have experienced emerging mobility services challenging their public policies, particularly bike- and scooter-sharing and ride hailing services. Hence, accurate design and simulation of such emerging mobility services are essential to apply certain regulations in order to guarantee sustainable development.

Reducing the investment risks in public EV
charging infrastructure to meet the future
demands taking into account the power
supply and smart grid

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01 Traffic simulation for cities of any scale

Traffic simulations are essential for traffic engineers for a wide range of applications:

evaluating the impact of traffic
management measures to traffic
prediction. You are able to simulate
almost any “what-if” scenario in your
city. Open-source or commercial, the
choice of the simulator is yours.

Group 125

03 Modeling road traffic emission

Air quality is a major concern for many European cities.

A large number of best practice examples are available and many cities of different sizes plan to employ a similar plan, i.e. low emission zones, promoting public transport and active mobility such as walking and biking, congestion pricing schemes, etc. In order to evaluate the effectiveness of any strategy, it is necessary to model the “is” situation and compare it to the “future” scenario. Using widely accepted European standards

Group 128

06 Digital asset management

Keep a digital record of your
physical assets in the city from
with a history of changes and
planned future changes to share
across departments.

history of changes and planned future changes to share across departments. Coupled with advanced data analytics you can use predictive maintenance to reduce the adverse impacts of maintenance work.

Group 129

05 Rethinking public
space allocation

Public space is a very rare and precious commodity that has been dominated by vehicular traffic.

Many cities around the world are redesigning their road space allocation with the aim of achieving a safer, cleaner and sustainable urban mobility. Closing inner-city roads to car traffic, converting car-lanes to bus-lanes, removing parking spaces to add bike-lanes etc. must be carefully analyzed in the context of urban mobility as a whole and not just as a local measure.

01 Traffic simulation for cities of any scale

Traffic simulations are essential for traffic engineers for a wide range of applications:

evaluating the impact of traffic
management measures to traffic
prediction. You are able to simulate
almost any “what-if” scenario in your
city. Open-source or commercial, the
choice of the simulator is yours.

02 Data-driven traffic prediction

Data sparsity is a thing of the past. Today, we have access to massive amounts of data from multiple sources.

Advanced data analytics and AI algorithms help us to analyze these data to unveil the hidden patterns of complex urban traffic systems and predict its future state. While every city is unique, the congestion propagation mechanism is more or less similar. By integrating knowledge, you can get the most out of your data.

03 Modeling road traffic emission

Air quality is a major concern for many European cities.

A large number of best practice examples are available and many cities of different sizes plan to employ a similar plan, i.e. low emission zones, promoting public transport and active mobility such as walking and biking, congestion pricing schemes, etc. In order to evaluate the effectiveness of any strategy, it is necessary to model the “is” situation and compare it to the “future” scenario. Using widely accepted European standards

04 Design for future shared, electric and autonomous mobility

Within the past few years, cities have experienced emerging mobility services challenging their public policies, particularly bike- and scooter-sharing and ride hailing services. Hence, accurate design and simulation of such emerging mobility services are essential to apply certain regulations in order to guarantee sustainable development.

Reducing the investment risks in public EV charging infrastructure to meet the future demands taking into account the power supply and smart grid

05 Rethinking public
space allocation

Public space is a very rare and precious commodity that has been dominated by vehicular traffic.

Many cities around the world are redesigning their road space allocation with the aim of achieving a safer, cleaner and sustainable urban mobility. Closing inner-city roads to car traffic, converting car-lanes to bus-lanes, removing parking spaces to add bike-lanes etc. must be carefully analyzed in the context of urban mobility as a whole and not just as a local measure.

06 Digital asset management

Keep a digital record of your
physical assets in the city from
with a history of changes and
planned future changes to share
across departments.

history of changes and planned future changes to share across departments. Coupled with advanced data analytics you can use predictive maintenance to reduce the adverse impacts of maintenance work.

01 Traffic simulation for cities of any scale

Traffic simulations are essential for traffic engineers for a wide range of applications:

evaluating the impact of traffic
management measures to traffic
prediction. You are able to simulate
almost any “what-if” scenario in your
city. Open-source or commercial, the
choice of the simulator is yours.

02 Data-driven traffic prediction

Data sparsity is a thing of the past. Today, we have access to massive amounts of data from multiple sources.

Advanced data analytics and AI algorithms help us to analyze these data to unveil the hidden patterns of complex urban traffic systems and predict its future state. While every city is unique, the congestion propagation mechanism is more or less similar. By integrating knowledge, you can get the most out of your data.

03 Modeling road traffic emission

Air quality is a major concern for many European cities.

A large number of best practice examples are available and many cities of different sizes plan to employ a similar plan, i.e. low emission zones, promoting public transport and active mobility such as walking and biking, congestion pricing schemes, etc. In order to evaluate the effectiveness of any strategy, it is necessary to model the “is” situation and compare it to the “future” scenario. Using widely accepted European standards

04 Design for future shared, electric and autonomous mobility

Within the past few years, cities have experienced emerging mobility services challenging their public policies, particularly bike- and scooter-sharing and ride hailing services. Hence, accurate design and simulation of such emerging mobility services are essential to apply certain regulations in order to guarantee sustainable development.

Reducing the investment risks in public EV charging infrastructure to meet the future demands taking into account the power supply and smart grid

05 Rethinking public
space allocation

Public space is a very rare and precious commodity that has been dominated by vehicular traffic.

Many cities around the world are redesigning their road space allocation with the aim of achieving a safer, cleaner and sustainable urban mobility. Closing inner-city roads to car traffic, converting car-lanes to bus-lanes, removing parking spaces to add bike-lanes etc. must be carefully analyzed in the context of urban mobility as a whole and not just as a local measure.

06 Digital asset management

Keep a digital record of your
physical assets in the city from
with a history of changes and
planned future changes to share
across departments.

history of changes and planned future changes to share across departments. Coupled with advanced data analytics you can use predictive maintenance to reduce the adverse impacts of maintenance work.

Our Customers and Partners

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