Project overview:
Requirements definition
Creation of structural data and network models
Procurement of software and hardware
Calibration and verification
Test, acceptance and commissioning

Project partner:
City of Frankfurt / Main
N. N.

Project duration: 12/2019 – 12/2023

The project "With data for more planning security" is funded by the Federal Ministry of Transport and Digital Infrastructure (BMVI) as part of the immediate program "Clean Air 2017-2020" - funding measure "Digitization of municipal transport systems".

In order to meet the diverse challenges of urban traffic, innovative traffic planning and traffic control must on the one hand be holistic in terms of content, intermodal and environmentally oriented, and on the other must use intelligent and efficient methods.

Above all, this procedure requires diverse and high-quality input data. In order to gain this, the city of Frankfurt am Main is developing a “model for forecasting the impact of traffic management measures and their application based on traffic, environmental and weather data” with the UVM 7 project.

The forecast is based on traffic data (detectors, floating car data, traffic reports), traffic cells / traffic districts with the respective demographic information (number of inhabitants and individual mobility behavior), uses (work, supply or leisure facilities), choice of means of transport, and activated and deactivated ones Traffic management measures.

The data are thus placed in an overall context, which enables cause / effect analyzes of the operational traffic management through graphic, tabular and cartographic preparation. Among other things, a tool for proactive traffic control will be made available to the traffic planners and operators of the integrated overall traffic control center of the road traffic authority.

Adjustments and planning of traffic management measures based on this are aimed at the forecast results for the choice of means of transport, the control of traffic flows, to liquefy the traffic and to reduce traffic-related immissions.