Predicting the Pulse of a Multi-Million European Metropolis.
How a cross-functional task force engineered a massive public transport simulation pipeline to predict bottlenecks and model city-wide infrastructure changes.
5M+
Daily Trips Modeled
Every passenger an agent
100 GB
Data Per Run
Structured municipal data
95%
Metro Prediction Accuracy
Underground networks
85%
Ground Route Accuracy
Trams, buses, surface
The Data Chaos.
The core challenge was not just mathematical — it was a massive data engineering hurdle. The team had to continuously adapt to shifting municipal requirements while unifying fragmented data sources to build accurate road graphs, transit networks, and complex daily schedules from scratch.
Fragmented, Messy Data Sources
Municipal transit data arrived in dozens of inconsistent formats — GTFS feeds, CSV timetables, shapefiles, and ad-hoc exports — with gaps, duplicates, and conflicting schemas.
Building Road Graphs from Scratch
Accurate road graphs and transit networks had to be reverse-engineered from raw geospatial data. Every edge and node had to be validated against real-world topology.
Reconstructing Daily Schedules
Complex daily schedules for millions of agents required inferring travel demand from origin-destination surveys, ticketing data, and population density models.
Continuously Shifting Requirements
Municipal stakeholders regularly changed scope mid-pipeline. New station configurations, revised timetables, and updated projections had to be absorbed without breaking fidelity.
Transit Monitor — Line M2
Live SimClosure of Uprising Square station creates cascade overload on 2 downstream nodes. Predicted delay: +34 min average.
MATSim + Custom Data Pipelines.
A calibrated, production-grade instrument fed by a seven-stage data pipeline transforming raw municipal exports into a high-fidelity multi-agent simulation.
Simulation Pipeline
Raw Municipal Data
GTFS, CSV, Shapefiles
Data Cleansing
Dedup, schema unification
Road Graph Builder
OSM + custom topology
Schedule Assembler
OD matrix + timetables
MATSim Engine
Multi-agent simulation
OLAP Cube Layer
Analytical aggregation
Operator UI
Reports & scenario explorer
The Engine & The Pipeline
We used the open-source MATSim framework as the core simulation engine. The heavy lifting was engineering the massive data preparation pipelines required to feed 100 GB of structured municipal data into the engine for every single run.
Multi-Agent Complexity
Every passenger was modeled as an independent agent with a unique daily plan, calculating millions of possible route combinations across the entire network.
Actionable Intelligence
Raw simulation data is useless without interpretation. We built a custom analytical layer and OLAP cube reports, translating billions of data points into a clear UI for transport operators.
From Billions of Data Points to Operator Decisions.
A calibrated simulation with an analytical layer translating raw output into KPIs that transport operators can model, compare, and act on.
Passenger Flow — Actual vs. Simulated
Calibration Δ < 4%Operator KPIs
94.2%
Peak Load by Station
▲ Critical
4.1 min
Avg Transfer Time
▼ Improved
0.38
Bottleneck Score
▲ Critical
Iter 42
Iteration Convergence
— Stable
Technical Triumph.
Despite unpredictable municipal data and shifting requirements, the cross-functional team delivered a highly calibrated predictive instrument — equipping the city to safely model, analyze, and execute future urban transport reforms.
95%
Prediction Accuracy
Underground metro networks calibrated against real-world ridership data.
85%
Prediction Accuracy
Ground-level routes — trams, buses, and surface transit modeled with high fidelity.
100 GB
Per Simulation Run
Structured, validated municipal data feeding every modeled scenario.
5M+
Daily Trips Modeled
Every passenger an independent agent with a unique daily plan.
- Cross-functional 8-person task force delivered a production-grade simulation instrument.
- Calibrated against real-world ridership data to validate prediction fidelity.
- Operators can model station closures, new line introductions, and timetable changes before deployment.
- Simulation pipeline absorbs continuous data updates without loss of accuracy.
“A state-of-the-art capability to safely model, analyze, and execute future urban transport reforms — before a single worker picks up a tool.”
Roxosoft Engineering Team
Smart City Simulation Task Force
Work With Us
Facing a massive data engineering or simulation challenge?
Whether you need to model city-scale systems, tame fragmented data pipelines, or build predictive infrastructure — our technical team has done it before.
What Happens Next?
We Analyze Your Requirements
Our founders will personally review your project details and reach out to discuss your vision and goals.
NDA & Confidentiality
If needed, we sign a Non-Disclosure Agreement to ensure your ideas and data stay fully protected.
Project Proposal
We submit a comprehensive project proposal with timeline, milestones, and a transparent cost breakdown.
Why CTOs Choose Roxosoft
- Direct access to founders and tech leads — no middlemen
- 75%+ of our team are senior-level engineers
- Microsoft, AWS, and Google certified professionals
- Average client relationship: 5+ years
Offices in United States · Portugal · Spain · Senior data architects only.