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