The main objectives of the MUST project are to directly and indirectly influence mobility behavior toward climate- and environmentally friendly traffic management. This is achieved through traffic avoidance, modal shift, and traffic improvement. For example, the project tests which combinations of information can effectively reach different target groups in order to achieve positive effects.
In Austria, traffic information is already available through radio, roadside displays, apps, and the multimodal route planner VAO (Verkehrsauskunft Österreich). These services provide a broad information base covering current traffic events. In recent years, multimodal route planning has gained importance, as transport modes have increasingly been combined. Services such as ÖBB Scotty, AnachB, Smartride, Wegfinder, or Wien Mobil already integrate data from individual motorized traffic (MIT), public transport, cycling, and walking. In addition, they combine modes such as park & ride routes or sharing options with rental bikes and scooters.
MUST, as a follow-up project to DOMINO (www.domino-ooe.at), develops new information channels and improves existing ones. Examples include monitors for carpooling information or an Outlook add-in for planning shared commutes. Usability, app functions, reward systems, and communication content are enhanced, while Austrian carpooling platforms are connected with VAO.
Furthermore, media partnerships ensure wide reach and enable rapid feedback from users. In pilot test corridors, new and existing information and communication channels are evaluated. These corridors include:
The results serve as a basis for evaluation, optimization, and further development of existing or newly conceptualized mobility services.
RISC Software GmbH leads the analysis of the information chain within the Upper Austrian pilot project. To evaluate the influence of information channels on mobility behavior, access figures from public transport services are compared with road usage data, counting points from Upper Austria and ASFINAG, and data from the Domino carpooling app. Weather data is also integrated into the evaluation.
Since the data is delivered in different formats and protocols, cleansing and harmonization are necessary. Therefore, a data model tailored to the planned queries is being designed. Due to the large and growing amount of data, the system must also ensure short response times and scalability.
Based on the processed data, RISC first analyzes historical usage behavior. This includes detecting long-term trends, seasonal patterns, and short-term fluctuations. To measure the impact of the information channels, selected reference circuits are evaluated for different impact periods. Depending on the findings, various time-series analysis methods are applied, such as correlation analyses, hypothesis testing, causality investigations, and change point detection.
Finally, iterative feedback loops with domain experts validate these effects through visual representations.
RISC has established strong expertise in intelligent transportation systems, big data engineering, and data science within the Smart Mobility & Analytics department. This expertise results from multiple large-scale research and commercial projects.
The project consortium consists of ASFINAG, ÖBB, ORF, VAO, VOR Verkehrsverbund Ost-Region, ÖAMTC, AlphaHapp GmbH, OÖ Verkehrsverbund, FH OÖ Forschungs-& Entwicklungs GmbH, Fluidtime Data Services GmbH, netwiss OG, tbw research, s.mobil, Salzburg Research, RISC Software GmbH and Wiener Lokalbahnen. Many high-reach information channels are located in the direct sphere of influence of this consortium – thus ensuring efficient and targeted processing of the project content and an effective pilot phase in the project.
This project is funded by the Austrian Research Promotion Agency (FFG) under project no. 902291.
Senior Data Engineer
