ModelGPT – more than just an April Fools’ joke?

AI-powered chatbots like ChatGPT have brought the topic of artificial intelligence to the center of public discussion. For our April Fools’ joke we imagined how MobilityGPT would look like – a generative AI system that can build transportation models within seconds. But is this idea really nothing more than a joke? ?Without a doubt, AI and machine learning (ML) already play an important role for us at PTV Group as well as for the entire mobility industry.

First of all, we already have a super-fast technology to automatize the building of standardized transport models: With Model2Go, basic transportation models can be set up for any given city or region worldwide within just a few days - a process that takes months manually and requires lots of know-how and resources. Since summer 2022, when we launched this automated technology, numerous customers are reaching out to try and work with it. What we learn is, that this technology brings a significant change to their work. ?It drastically reduces the time, effort, and cost of model-building, making model-based decisions viable even for small projects.

Model2Go is empowered by Machine Learning (ML) and Big Data sources. We are constantly further developing our technology here. For example, we offer customers the possibility to predict structural data as population or workplaces based on publicly available OpenStreetMap data.

AI also comes into play in numerous areas at PTV. ?For example, in the field of recognizing and reconstructing the geographical distribution of travel demand, or in recognizing the mode of transport based on GPS data or trajectories. Depending on the data source, it can be hard to get details on the mode of travel.?For mobile device location-based services (LBS), we can tell that people have moved from A to B, but not how. This information is crucial, however, e.g., in modeling changing travel behavior on sustainable modes. AI classification methods help improve these valuable data sets by synthesizing missing and additional characteristics.?

Intelligent traffic management is another important application area: Our real-time traffic management solution PTV Optima combines machine learning techniques with dynamic traffic modeling. The ML algorithms are used for forecasting calculations and improving the accuracy of the model. Today’s mobility systems are becoming increasingly complex with all the different transport modes, new technologies and services. It is not only about getting a coherent and detailed picture of the current traffic situation, but also about being able to act in a predictive way. Our software not only provides live information on the current traffic situation, but also enables detailed traffic forecasts up to 60 minutes in advance. This enables traffic managers to react appropriately to both planned and unplanned events.

Here, too, we are constantly working on further developments – for example using ML to suggest traffic operators’ specific area to monitor and for automatic generation of meaningful alerts aimed to prevent dangerous congested situation and increase safety and smooth operation of road networks.

In transit networks, we work on ML techniques to propose mitigation actions when service disruptions occur due to vehicle breakdowns, unexpected congestion, and similar situations.

In conclusion, I would like to mention an exciting and very concrete project, #transmove in Hamburg. In close cooperation with the Free and Hanseatic City of Hamburg (namely the Department of Roads, Bridges and Waters), Karlsruher Institut für Technologie (KIT) and the industrial partner @Workplace Solutions GmbH (WPS) we are implementing an AI-supported software that aims to enable smart and sustainable mobility forecasts (short and long term) Hamburg strives to use the holistic approach not only to improve city-wide mobility, but also to reduce emissions. The exciting thing about it: The plan is to make this smart system available not only to mobility planners such as traffic control centers and thus to city/road/bike path planners, but also to citizens.

AI and ML are powerful tools that can help us to create a better transportation system that is safer, more efficient, and more sustainable. I am convinced that the future of mobility lies in these innovative technologies and the intelligent use of data.