Smart City · Public Transport

Low fare collection, no visibility into where revenue leaks, no single control point for the network

› Real-time audit dashboard + 30% revenue uplift

Excess fleet, inefficient schedules, high operational cost and poor on-time performance

› 15–25% fleet cost reduction in 6 months

Fragmented vendors, no unified data layer, slow procurement cycle

› End-to-end stack, single contract

It is happening all the time, and you need to know which routes, which shifts, which districts. Smart city maps it and sends inspectors exactly where it's highest

Vehicles are dispatched based on legacy timetables. Smart city reroutes using live demand — cutting excess fleet use by 15–25%

Origin-destination data shows exactly which routes are underperforming. After identifying them, the Smart city team models the fix, and helps you deploy it

With cities’ constant growth, some buses get into traffic jams, slowing to 10–15 km/h on key corridors. Do the network speed analysis with the Smart City team and plan targeted interventions — dedicated lanes, junction fixes

Additional fare revenue/year

Revenue processed in 2025

Increase in fare collection

Fleet cost reduction

To production rollout

Users on Yango infrastructure

The city network suffered from inefficient route planning, undetected fare evasion, and excess fleet deployment. We deployed integrated analytics, real-time dispatching, and onboard equipment. Inspector routes were optimized using live payment-to-passenger ratios. Vehicle classes and service intervals were redesigned based on actual origin–destination demand

Network speed analysis identified specific corridors where low operating speeds caused unreliable service. Data-driven recommendations — dedicated bus lanes, junction reorganization — were targeted precisely at those bottlenecks. The speed map became the city's core diagnostic tool for corridor-level investment decisions

Smart City platform integrates passenger flow and payment data into a single dashboard for real-time fleet control and automated capacity optimization. Dispatchers monitor live vehicle conditions while inspectors receive daily, AI-generated routes targeting areas with the highest fare evasion. To eliminate revenue loss, the system uses origin-destination matrices to redesign underperforming schedules into demand-matched intervals. Every operation relies on precise sensor data and localized deployment, ensuring the city maintains full data sovereignty

Active users on Yango infrastructure

Software + hardware + support — single contract, one vendor

Revenue processed through platform in 2024

Typical time from contract to Phase 1 production

Commercial model tied to your results — not license fees

Localized control, data residency, operational independence