Deep-tech perspectives on urban optimization, operational science, and decision intelligence.
What collective intelligence can teach us about public transport — and why exploring the solution space matters more than picking a single algorithm.
Series
From planning to data to operations — a three-part reading path. Each chapter sets up the next decision.
Quick Topics
How people perceive waiting, reliability, and trust — and why comfort is an operational cost.
Depot pull-outs, dead runs, and the fleet math that never shows up on the passenger map.
When GTFS looks clean on paper but the street tells a different story about service quality.
Research, models, and operations research — framed for people who approve the next timetable.
Small cities, fare-free experiments, and networks where budget and ridership collide.
What strategy games get right about transit — and what real cities must still prove.
Editor's PickWhat collective intelligence can teach us about public transport — and why exploring the solution space matters more than picking a single algorithm.
10 min read
A direct journey is always better — or is it? How splitting a corridor can reduce cost, improve frequency, and still serve passengers well.
Transit isn't optimizedby adding more buses.
The future of public transport won't be defined by more data—but by better-defined objectives.
A Nature Communications study shows optimal transit networks are not always symmetrical—and concentrating resources can outperform uniform coverage.
Cities already have the data. The real question is which evidence deserves to be trusted before optimization, AI, or digital twins begin.
Every scheduleis a hypothesis.
Denied passengers and latent demand: why non-boarders never appear in ridership data. Lessons from Eskişehir, Lisbon, and Abu Dhabi on measuring real transit demand.
OW Manifesto
Every route, headway, and depot pull-out is a decision. OW makes those decisions measurable, constraint-aware, and explainable.
Read our science approach
IPPR research applied to Turkey: passengers expect reliability, affordability, and safety. How OW GTFSHub™, CostLogic™, and FreqOpt™ help municipalities meet those expectations.
Public transit optimization in small cities is not a luxury—it is a critical lifeline to save tight municipal budgets. Practical insights from real cases.
Networks learn.When organizations do.
Does classic optimization reward dense cores while sidelining peripheral neighborhoods? OW Suite breaks the efficiency paradox with reinforcement learning and accessibility-based transit planning.
Fare-free transit sounds simple, but data shows mode choice is driven primarily by travel time and passenger signals like PCI—often triggering modal shift. Here is an OW data-driven roadmap for Türkiye’s cities.
Public transport efficiency isn't measured by vehicle kilometers alone. From eye-tracking to reinforcement learning, biophilic design to slope penalties, integrating human behavior into optimization models is the future of transit.
Optimization startslong before the first vehicle moves.
2053 net zero sounds far away, but it is just one generation ahead. Optimize your fleet to cut fuel costs and emissions—without buying new vehicles.
Related Modules
Used in this piece
Every morning, before a single passenger boards, a silent migration occurs in cities worldwide: thousands of buses travel from depots to starting points, completely empty. This "dead mileage" or "empty kilometers" is often dismissed as an unavoidable operational necessity. But in reality, it's one of public transport's most significant—and most overlooked—sources of waste.
Related Modules
Used in this piece
A common scene plays out daily on many city arteries: two or more buses with different line numbers travel the same corridor mere minutes apart, seemingly chasing the same passengers. This is not strategic service design; it's often the result of unplanned growth and historical legacy.
Every public transport system runs on two essential fuels: the diesel or electricity that powers its vehicles, and the data that powers its decisions. While the first is meticulously measured and managed, the second—specifically your GTFS data—often operates in the shadows.
Related Modules
Used in this piece
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