The Hidden Intelligence Behind Multi-Modal Journey Planning

Your journey planner is lying to you.

It tells you the "fastest" route is the 8:15 bus. It doesn't tell you that the bus is overcrowded, the walk to the stop is freezing, and you'll likely miss the connection anyway. It doesn't know you.

We are building the engine that knows the truth—and knows what you actually want.

Most people think of a journey planner as a simple utility: you type in where you are, where you want to go, and it spits out a route. It feels instantaneous. It feels simple. But under the hood, modern multi-modal journey planning is one of the most computationally complex challenges in urban technology. It’s about orchestrating a chaotic, living system of buses, trains, taxis, scooters, bikes, and walking paths—each with their own schedules, real-time delays, pricing models, and availability constraints.

And now, we are asking it to do something even harder: be intelligent.

The Old Way vs. The Intelligent Way

Traditional routing engines (like the early iterations of OpenTripPlanner) were brilliant at solving the "shortest path" problem. They could tell you the mathematically fastest way to get from A to B.

But the fastest route isn't always the best route.

Maybe the fastest route involves a 15-minute walk in driving rain. Maybe it requires three transfers with tight connections that you’re likely to miss. Maybe it costs €50 in a taxi when a €3 metro ride is only 4 minutes slower.

This is where AI enters the equation.

Optimization Beyond Speed

The next generation of journey planners doesn't just minimize time. It optimizes for a multi-dimensional set of user preferences and city goals.

At MaaS Solutions, we are building engines that weigh factors dynamically:

• Cost vs. Convenience: "I’m willing to pay up to €10 to save 20 minutes, but not more."
• Sustainability: "Show me the greenest route, even if it takes 5 minutes longer."
• Accessibility: "I need step-free access and minimal walking."
• Extreme Conditions: "It’s freezing outside, so absolutely minimize waiting at exposed stops."

An AI agent can look at these constraints and "prune" the search space in real-time, offering options that a traditional algorithm would discard because they weren't strictly the "fastest."

Real-Time Adaptation

The true test of intelligence is how a system handles disruption.

In a standard app, if a bus is delayed, you might see a red "delayed" label. But the route remains the same. You stand at the stop, waiting.

An intelligent planner actively monitors the network state. If it detects that your connecting bus is 10 minutes late—making you miss your train—it shouldn't just tell you. It should reroute you.

Perhaps it suggests walking to a different station to catch a parallel tram. Or maybe it recommends taking a scooter to the next express bus stop. This is proactive, dynamic rerouting, powered by predictive models that understand how delays cascade through a network.

Context is Everything

We are seeing this play out in extreme environments. In scorching desert climates, "walkable distance" is a completely different concept in July than it is in January. An intelligent router knows this. It actively minimizes uncovered walking segments and prioritizes air-conditioned transfer points when the temperature spikes.

Conversely, in Nordic winters, the system needs to account for icy conditions that might make a bike path unusable, or prioritize routes with heated shelters.

This level of nuance requires:

• Micro-mobility integration: Seamlessly connecting scooters/bikes for the first/last mile only when conditions are right.
• Taxi/Ride-hailing orchestration: Booking a ride as a leg of a larger public transit journey to bridge gaps in comfort or safety.
• Predictive arrival times: Using machine learning to refine ETA predictions based on historical traffic patterns and weather impacts.

From Intent to Anticipation

We are moving away from the paradigm where a user has to explicitly state their intent every time ("I want to go to work now"). That is friction.

The future is predictive. An intelligent mobility agent anticipates your needs based on routine, calendar, and context. It knows you usually leave for the office at 8:30 AM. It checks the traffic, the weather, and the train status before you even open the app.

When you are ready to leave, it doesn't ask "Where to?"; it presents the best options for right now: "Heavy traffic on the bridge today. The 8:42 train is on time and gets you there 10 minutes earlier."

It’s complex engineering. It requires heavy lifting on data integration and serious algorithmic power. But for the user? It should feel like magic.