Traffic Digitalization

  Graphic: City view with street, houses and vehicles Copyright: © ISAC

The research group “traffic digitalization” is focused on the collection and analysis of traffic data, communication within an intelligent traffic system, and the integration of simulations into traffic safety considerations.


Photo of Serge Lamberty © Copyright: ISAC


Serge Lamberty

Head of Digitalization Department


+49 241 80-25800



Research Focus in Digitalization

Data Collection

Current forms of traffic data collection are predominantly based on macroscopic approaches. Technological foundations for this include norm-compliant systems, floating vehicle data and induction loops.

Here, the research group is treading the path of a paradigm shift, towards microscopic data acquisition. The focus is on individual vehicle data, collected independently of the vehicle itself using sensors as an integral part of the infrastructure.

In this context, traffic data acquisition with cameras and the associated image processing are of particular interest - or rather the demands placed on the software used in this regard. The goal is to classify and digitally map each vehicle in order to analyze traffic behavior down to the smallest detail.

Data Analysis

The mapped traffic data provides valuable information about traffic behavior of road users and the interaction between them and with infrastructure elementse: An undisturbed flow of traffic becomes just as recognizable as road sections where heavy braking maneuvers accumulate. Based on these findings, traffic safety can be preventively evaluated and, if necessary, positively influenced.

The analysis of conflicts in road traffic makes it possible to identify dangerous spots before real dangerous situations occur or road users come to harm. The use of microscopic traffic data is particularly useful here.


The traffic of the future will be intelligent, and the foundations for this are already being laid today. At the center of research is what is known as shared intelligence.

On the one hand, intelligent and self-learning vehicles collect information and thus independently recognize danger spots. On the other hand, the infrastructure also generates data on usage behavior by vehicles and in this way also identifies accident black spots. Communication between the two entities, vehicle and infrastructure, is the basis of shared intelligence; together they close existing data gaps. These gaps are unavoidable because not all areas of trafficwill be covered by cameras or other sensor technologies.

Moreover, a basic standardization of data collection cannot be universally implemented for all road users: some vehicles may not have the necessary technical equipment, as well as vulnerable road users. Therefore, any shared intelligence is primarily based on communication, or information exchange.


Simulations open up additional perspectives beyond pure data collection. They are essential to cover areas that cannot be captured for various reasons.

Applied to an existing road section, a simulation illustrates the potential impact of interventions in the current infrastructure, such as a change in traffic light control or the redesignof traffic signs. In this way, it is possible to assess in advance whether a targeted measure will have the desired effect and contribute to an improvement in traffic safety.

Simulations are also of fundamental importance in the planning of new road sections because, since they do not yet exist, it is not possible to draw on a suitable data set. Regardless, an appropriately calibrated simulation enables accurate forecasts of future traffic flow. The same applies to potential danger spots, which can thus be identified and prevented within the planning process before they even arise.