Download our free white paper and learn how AI & real-time data can support the evolution of shared mobility!
Shared mobility is growing fast. How can data in motion foster such growth?
Shared mobility has proven to be an effective solution for reducing costs, minimizing CO2 emissions, and providing flexible city commuting options. To further enhance shared mobility, the combination of event stream processing and Artificial Intelligence offers significant benefits, i.e. cost savings, additional revenues, and brand loyalty improvement through personalized customer experiences. As per Statista’s projections, the global shared mobility market is expected to grow at a CAGR of 3,90% between 2023 and 2027, reaching a global value of $1,79tn by 2027.
Our free whitepaper presents a use case about real-time machine learning approach that improves decision-making for car-sharing operators. The aim of the project is to forecast vehicle demand and distribution over different time periods and areas. Predictions are based on real-time data such as weather conditions and disruptive events (accidents, road closures) altering the ordinary fleet distribution.
The decision support system enabled by our MLOps platform, Helicon, offers actionable insights for car sharing operators, enabling proactive adjustments in vehicle distribution, pricing, and incentives based on traffic patterns and occurrences.
What you'll find in the white paper
Reshaping Shared Mobility with Decision Intelligence offers actionable insights to data professionals such Data Scientists, ML Engineers, Data Engineers, CTOs & CIOs and covers the following topics:
Introduction to Shared Mobility, overview & benefits
The Data in Motion & Real-time Machine Learning opportunities
How Helicon enables AI-powered, real-time prediction of car fleet distribution
Key benefits offered by Helicon for the shared mobility industry