PROOF

Case Studies & Pilot Implementations

Real-world applications of OW Suite decision intelligence—Governance, Network, Finance, and Sustainability—with methodology grounded in GTFS validation, Mixed-Integer Programming (MIP), deadhead ratio control, and service coverage analytics.

Metrics are methodological example ranges; results depend on data quality and constraints.

OW Optimize the World — Decision Intelligence Studio scene

Every successful optimization begins with a measurable question.

Four domains. One decision model. Evidence you can defend.

DOMAINS

Four decision domains. One methodology.

01

Governance

Shared models across departments

Explore02

Network

Demand-aware headways & reliability

Explore03

Finance

Structural cost vs decision OPEX

Explore04

Sustainability

Carbon as an optimization outcome

Explore
Example Scenario — GovernanceOW Intelligence Hub™

Citywide Operations & Resource Coordination

System Question

“How can limited resources be allocated across the city in a transparent, effective, and coordinated way?”

How OW Supports

OW integrates fragmented operational data into a shared system model, enabling organizations to understand trade-offs and coordinate actions across governance layers.

The Challenge

Large transit agencies rarely fail because of a single bad timetable—they fail because planning, operations, finance, and maintenance optimize local KPIs that conflict at the system level. Bus, rail, and contracted services often ingest different versions of the truth: stale GTFS snapshots, partial AVL coverage, and reporting spreadsheets that cannot be reconciled with what vehicles actually did on the street.

The real issue isn't vehicle shortage. It is structural resource allocation.

OW's Decision Intelligence Approach

OW establishes a GTFS Validation and data-ingestion gate before any optimization run. Feeds are checked for shape integrity, stop sequencing, calendar coherence, and block continuity so that MIP formulations are not poisoned by silent data defects. Once baseline network behavior is trusted, agencies model cross-mode resource allocation as an MIP with explicit objectives: reduce aggregate deadhead ratio, improve punctuality-sensitive Service Coverage, and respect fleet, crew, and political minimums as hard constraints.

Scenario layers let governance teams stress-test coordinated policies—network restructuring, procurement deferrals, or inter-operator transfers—against the same objective function, so trade-offs are explainable rather than rhetorical.

Decision flow

  1. Departments
  2. Shared Model
  3. Decision

Key Metrics

17%

Deadhead ↓

+11

Coverage

<24h

Reporting

MetricBaselineOW optimizedImprovement
Deadhead ratio (system)26–30%17–22%8–12 pts ↓
Service Coverage (equity-weighted)72/10082–88/100+10–16
Cross-mode reporting latency5–10 days<24 hNear real time
Coordinated scenario consensus time8–12 weeks2–4 weeksFaster cycles

View related decision context on Use Cases →

OW Optimize the World — operations coordinator reviewing a digital transit dashboard
ProductOW Intelligence Hub™
OW Intelligence Hub™

Governance & data fusion — GTFS validation → shared MIP core

Behind every better transit system is a better decision model.

Governance turns fragmented KPIs into a shared objective.

Example Scenario — NetworkOW FreqOpt™

Public Transport Network Performance

System Question

“Why does the transport network underperform — even when planned supply is in place?”

How OW Supports

OW replaces static schedules with adaptive, demand-aware optimization models that improve reliability, efficiency, and service quality.

The Challenge

Planners publish timetables that look adequate on paper, yet passengers experience chronic unreliability, crowding pockets, and surprise gaps. The root cause is rarely a lack of vehicles—it is a structural mismatch between where and when demand concentrates and how supply is committed through blocks, interlining, and depot return constraints.

Empty kilometers are not noise. They are the cost of a mismatched supply model.

OW's Decision Intelligence Approach

OW models the network as a capacitated, time-expanded flow problem. Mixed-Integer Programming ties trip-building, vehicle circulation, and crew-feasible rotations to minimizing dead mileage while honoring headway, load, and OTP targets. GTFS Validation ensures route geometry and stop times align with observed speed profiles so deadhead and running times in the MIP reflect operational physics, not wishful scheduling.

Demand-aware frequency and micro-adjustments use smart-card or APC-informed load curves to reposition slack where it prevents crowding instead of where tradition places it.

Decision flow

  1. AVL
  2. Demand
  3. Optimization
  4. New Headways

Key Metrics

15%

Deadhead ↓

+8pts

OTP

−28%

Crowding

MetricBaselineOW optimizedImprovement
Deadhead ratio (peak/off-peak)24–32%15–20%9–12 pts ↓
Headway regularity (cv)0.38–0.450.22–0.30Stabilized
Crowding exceedance hours100% (baseline)65–78%22–35% ↓
OTP / on-time performance78–82%86–92%+6–10 pts

View related decision context on Use Cases →

OW Optimize the World — commuter waiting at a city bus stop
ProductOW FreqOpt™
OW FreqOpt™

Network flow — demand signals → MIP trip graph → dead mileage minimization

Deadhead ↓ · Coverage ↑ · OTP ↑

When the network model is trusted, every lever becomes measurable.

Example Scenario — FinanceOW CostLogic™

Budget & Cost Dynamics

System Question

“Which costs are structural, and which are driven by operational choices?”

How OW Supports

OW shifts budget discussions from totals to decision impact, helping organizations understand how choices translate into costs.

The Challenge

Finance teams see fuel, labor, and maintenance as line items; operations sees them as outcomes of scheduling decisions. Without a causal bridge, budget cuts become blunt (fewer trips everywhere) instead of surgical (remove structural dead mileage while preserving Service Coverage).

Budgets debate totals. Optimization debates trade-offs.

OW's Decision Intelligence Approach

OW CostLogic™ and optimization layers separate baseline structural commitments (minimum service rules, collective agreements, fleet ownership) from decision-derived OPEX: deadhead hours, peak vehicle requirement, fuel from empty repositioning, and maintenance cycles triggered by marginal kilometers. Every scenario run produces a consistent cost attribution tied to GTFS-validated vehicle trajectories, not inferred averages.

MIP shadow prices on tight constraints translate into finance-ready narratives: which euro of savings is robust under demand uncertainty versus which requires risky service degradation.

Decision flow

  1. GTFS
  2. Vehicle
  3. Fuel
  4. CostLogic

Key Metrics

ROI

+14%

−6pts

Dead OPEX

−5%

Peak fleet

MetricBaselineOW optimizedImprovement
OPEX attributable to dead mileage9–14%5–8%4–6 pts ↓
Peak vehicle requirement100%92–97%3–8% ↓
Cost per revenue hour100%88–94%6–12% ↓
Scenario ROI clarityQualitativeQuantified (MIP)Auditable

View related decision context on Use Cases →

OW Optimize the World — transit staff reviewing operational data on a tablet
ProductOW CostLogic™
OW CostLogic™

Cost attribution — validated vehicle traces → OPEX decomposition

From totals to trade-offs.

CostLogic™ attributes spend to the decisions that created it.

OW CostLogic™
Example Scenario — SustainabilityOW FleetOpt™

Environmental Performance & Carbon Reduction

System Question

“How do operational choices influence environmental outcomes?”

How OW Supports

OW aligns operational optimization with climate and sustainability objectives by making environmental impact a measurable system outcome.

The Challenge

Cities publish climate targets, but transit emissions are embedded in operational mechanics: unnecessary dead mileage, poor load factors, and diesel peaks that could be smoothed with better frequency carving. Without coupling emissions models to optimization, sustainability becomes a reporting exercise detached from dispatch and planning levers.

Climate targets fail in the garage, not in the annual report.

OW's Decision Intelligence Approach

OW attaches emission factors and energy-use curves to MIP decision variables: vehicle type, trip length, idle time, and depot pull-outs. Objectives can be scalarized or solved as multi-objective problems, Pareto-fronting the trade-off between crowding relief and CO₂ per passenger-kilometer.

GTFS Validation prevents optimistic routing that understates VKT; AVL reconciliation grounds electrification scenarios in observed elevation and stop-go patterns—so each accepted scenario documents OTP, Service Coverage, and forecasted emission ranges together.

Decision flow

  1. Operations
  2. Energy
  3. Carbon
  4. FleetOpt

Key Metrics

−10%

CO₂ / km

−22%

Excess VKT

+12%

Energy eff.

MetricBaselineOW optimizedImprovement
CO₂ per revenue vehicle-km100%88–93%7–12% ↓
Excess VKT from deadhead100%72–85%15–28% ↓
Passenger-km per energy unit100%108–118%Efficiency ↑
Climate scenario audit trailAd hocGTFS-linkedTraceable

View related decision context on Use Cases →

OW Optimize the World — parent and child boarding a public bus
ProductOW FleetOpt™
OW FleetOpt™

Carbon-aware optimization — energy curves inside the MIP objective

Every successful optimization begins with a measurable question.

Pilot implementations turn methodology into evidence—before the budget cycle closes.

Ready to measure your next decision?