BuiltWithHERE: How MOIA is Shaping the Future of Autonomous Ridepooling

Who is MOIA and What Do They Do?

Built with precision, simulated, and tested - this is how MOIA envisions the future of urban mobility. As cities around the world struggle with congestion, sustainability, and accessibility, the need for smarter transportation solutions has never been more urgent. Just when you start to wonder how we’ll tackle these challenges, MOIA steps in with a bold new approach: autonomous ridepooling. 

MOIA’s Mobility Consulting team equips cities with the tools to plan smarter, simulate better, and move more efficiently. Their in-house simulation platform, the “Mobility Impact Analyzer” (MIA), helps forecast urban mobility challenges and prepares cities for the unknowns of tomorrow. 

What is Ridepooling?

Ridepooling is a shared transportation service where multiple passengers traveling in similar directions are grouped into a single vehicle. Unlike traditional ride-hailing, ridepooling dynamically adjusts routes to efficiently pick up and drop off passengers. This reduces traffic congestion, lowers emissions, and makes urban mobility more sustainable and cost-effective. MOIA takes this concept further by preparing for autonomous ridepooling—where vehicles operate without drivers, unlocking even greater scalability.  

The Challenge 

Traffic is a global phenomenon. Good traffic is more the exception than the rule. As an a example, the Latvian city of Riga has less than 1 million inhabitants but traffic is still a major concern. As a resident of Riga, observationally a lot of cars are single occupants. This trend holds all over the world: most of the seats in vehicles are not utilized. This pattern brings with it a host of other challenges—traffic congestion, limited parking, and the environmental toll of so many vehicles on the road. It’s fair to say that urban mobility is at a crossroads worldwide.

The issues are strikingly similar: congestion, sustainability, and equitable access to transportation. If only there were fewer cars on the road, commuting would be so much easier. That’s where MOIA and ridepooling comes in. MOIA is tackling these issues head-on by developing ridepooling and autonomous mobility services that reduce the number of vehicles on the road while improving accessibility and efficiency. Let’s review how this is done. 

The Solution: MOIA’s Mobility Impact Analyzer (MIA) 

There are two sides to MOIA’s story: what you see on the streets and what happens behind the scenes. 

1. The magical Volkswagen minivans cruising through Hamburg, equipped with self-driving capabilities, are visible proof that MOIA’s vision is already in motion. Visit MOIAs website to Ride the Mobility of Tomorrow https://www.moia.io/en/passengers 

2. The Mobility Impact Analyzer (MIA), MOIA’s in-house simulation platform, helps cities plan for smarter mobility. Read more about it on https://www.moia.io/en/blog/mia-mobility-impact-analyzer 

Before ridepooling services hit the streets, MIA supports urban planners by modeling how the service would perform in real-world environments. It’s also a great example of open-source software powering a successful business—MIA runs on MATSim, an agent-based simulation framework. With MIA, cities can test different scenarios, evaluate the impact on traffic and emissions, and make informed, data-driven decisions.

MOIA video

HERE Technologies: The Data Backbone 

MOIA uses HERE’s high-resolution historic traffic data to calibrate simulations to real-world conditions. Unlike real-time data, historic speed profiles offer a consistent and reliable foundation for modeling typical traffic patterns—an essential component for long-term planning and scenario analysis. MOIA uses public data as well and, if applicable, uses specific data like inhabitants or workplace distributions from the respective municipality to better understand the traffic patterns and simulate how the service would improve the traffic in the city. 

Real-World Impact 

MOIA’s simulations achieve over 95% accuracy in replicating real-world traffic conditions. The accuracy is spectacular! This just shows we are closing in to fully autonomous driving and not only in one city. MIA platform has been used to evaluate ridepooling services in Hamburg and is now being expanded to other regions as well.  

As the self-service version of MIA is in development, in the future municipalities and non-technical users will be able to run their own simulations and explore the benefits of shared mobility in cities of their choice. 

Developer Insights 

MOIA’s tech stack includes Java, AWS, and MATSim, with a custom frontend for the MIA platform. MOIAs mobility transition map gives a clear overview of the traffic around the city and that is just the frontend. 

 The MATSim is the core technology which provides the analysis to be plotted on the map. 

The team emphasizes the importance of validating and calibrating simulation models using real-world data. HERE’s traffic data plays a crucial role in this process, ensuring that simulations reflect actual urban conditions and deliver actionable insights. 

Looking Ahead 

MOIA’s vision is to democratize access to advanced mobility simulation tools. Going forward, they aim to scale their solutions globally and empower more cities to embrace sustainable, data-driven transportation planning. Please visit this link to find their ridepool locations and also look at their business offerings.