Cover Image for When Transport Networks Think: Real-Time AI Decision Making at Scale

When Transport Networks Think: Real-Time AI Decision Making at Scale

Alex Sterling
February 21, 2026

The end of the schedule is coming.

For a century, transportation has been built on the static schedule. The bus leaves at 8:05. The delivery window is 2:00 PM to 4:00 PM. The parking rate is $5/hour, regardless of whether the lot is empty or full.

These aren't features; they're constraints. They exist because humans can't re-plan an entire city's worth of logistics every 30 seconds.

But AI can.

From Reactive to Cognitive

Most "smart city" projects are just dashboards. They show you traffic jams in high definition, but they don't fix them. True intelligence isn't about visualization; it's about decision-making.

We are moving from reactive systems (something breaks, a human fixes it) to cognitive systems (the network anticipates demand and adjusts autonomously).

Imagine a bus network that doesn't just follow a pre-printed PDF. Instead, it reallocates capacity based on real-time demand prediction models that ingest weather data, local event schedules, and live passenger counts. If a concert lets out early, the network knows before the crowd hits the curb.

The Engine: TidyCalls

At MaaS Solutions, we build the infrastructure for this transformation. Our core product, TidyCalls, isn't just an AI phone agent—it's the orchestration layer for physical operations.

TidyCalls acts as the "nerve center," connecting user intent (voice, chat, app) directly to physical execution (dispatch, access control, booking). It operates in three distinct layers:

  1. Prediction (The "What Will Happen"):
    Deep learning models that predict demand with granular precision. Not just "downtown will be busy," but "Stop #402 will need 40% more capacity at 18:15."

  2. Optimization (The "What Should We Do"):
    Real-time solvers that balance competing goals. How do we minimize wait times while maximizing fleet efficiency and adhering to driver break schedules? This is a massive combinatorial problem that AI solves in milliseconds.

  3. Agentic Execution (The "Do It"):
    This is the new frontier. Autonomous agents that negotiate with each other. A parking garage agent bidding for EV charging load from the grid agent. A ride-hailing fleet agent coordinating with the public transit agent to cover last-mile gaps during a disruption.

Shippable Intelligence: Autonomous Dispatch

We aren't just theorizing. We are building deployable modules today. Take our Autonomous Dispatch Engine:

  • For Fleet Operators: Dynamic routing that adjusts in real-time based on traffic and demand spikes.
  • For Property Managers: Automated amenity booking and maintenance dispatch that predicts equipment failure before tenants complain.
  • For Cities: Demand-responsive transport that fills the gaps in fixed-route networks.

This is software we can ship. It's not a generic LLM; it's a specialized, vertical AI engine trained on logistics and operational data.

Why Now?

Two things have converged: Latency and Reasoning.

5G and edge computing have brought latency down to where real-time control is safe. And Large Action Models (LAMs) have given systems the ability to reason about complex, unstructured inputs—like a text message from a driver or a vague maintenance report—and turn them into structured operational decisions.

The future isn't just self-driving cars. It's self-optimizing networks. The vehicle is just a node; the intelligence is in the graph.

And for the first time in history, the graph can think.