ÖPNV-Flexi - Artificial intelligence to optimize public transport

The coronavirus pandemic posed major challenges for local public transport. In order to ensure safe and low-contact mobility, the team from the Software & Information Engineering department contributed its expertise in data-based transport planning. The aim of the ÖPNV-Flexi project was to analyze large amounts of data and use artificial intelligence to enable demand-oriented and flexible use of local public transport.

In the district of Passau, numerous data sets for various bus routes were evaluated. The evaluation of different AI models, in particular time series analyses, enabled precise predictions to be made about passenger volumes on various routes. These forecasts helped to plan peak times more efficiently, manage capacities better and make public transport more pandemic-friendly.

Mobility is a basic need and in many regions is still primarily covered by car. Local public transport suffers from disadvantages such as limited flexibility and a lack of comfort. This is where the ÖPNV-Flexi project came in to compensate for the central weaknesses of public transport compared to private transport.

By combining digital networking, real-time information and artificial intelligence, sustainable solutions have been developed to improve planning and user-friendliness.

Technological approaches for intelligent traffic control

The ÖPNV-Flexi project pursued two central approaches:

1. Flexibilization for passengers
   • Development of the "ImmerMobil" and "Wohin-Du-Willst" apps, which provide comprehensive real-time information on mobility options.
   • Provision of transfer information, live timetables and route recommendations.
   • Use of augmented reality (AR) to improve navigation by projecting navigation data directly into the passenger's field of vision.
2. Optimization for transport operators
   • Development of an AI-supported planning tool that takes weather conditions, events and other influencing factors into account.
   • Big data-supported capacity forecasts that make it possible to adapt vehicle availability to actual demand in real time.
   • Reduction of overloads and empty runs through automated fleet management and predictive deployment planning.

These measures have enabled more intelligent traffic management that is tailored to the needs of passengers and transport companies alike.

The solutions developed by ÖPNV-Flexi have significantly increased the user-friendliness and efficiency of local public transport. Initial analyses showed a better distribution of passengers, which reduced waiting times and optimized bus capacity utilization.

The combination of artificial intelligence and real-time data analysis has shown how digital technologies can help to make public transport more sustainable and attractive. In rural regions in particular, dynamic route planning could help to make local transport services more flexible and customer-oriented.

The project results provide a valuable basis for future smart city concepts and could contribute to the development of new mobility solutions for urban and rural areas.