Built at Simform

Liftango

Transforming urban mobility with AI-powered route optimization and sustainable transport solutions

Overview

Liftango is a sophisticated smart mobility platform that reimagines urban transportation through intelligent route optimization, real-time coordination, and environmental sustainability. Designed for cities, universities, and enterprise campuses, the platform optimizes shared transport by dynamically matching riders, calculating efficient routes, and reducing carbon footprints.

Built with React and Redux, the application features advanced Google Maps integration with real-time vehicle tracking, predictive arrival times, and interactive route visualization. The platform serves transportation coordinators with powerful admin tools and provides riders with intuitive mobile booking experiences. The system processes thousands of daily rides while continuously optimizing for efficiency, cost, and environmental impact.

This project demanded expertise in complex geospatial algorithms, real-time data synchronization, scalable state management, and building performant interfaces that handle constantly updating location data from multiple vehicles simultaneously.

Role: Lead Developer · Simform
Duration: 12 months
Team: Cross-functional team of 3 frontend engineers, 4 backend engineers, 1 data scientist, 2 designers, and 1 product manager

The Challenge

Urban transportation systems face inefficiency, high costs, and environmental impacts from underutilized vehicles and poor route planning. The challenge was to build an intelligent platform that could dynamically optimize routes in real-time while accounting for traffic conditions, vehicle capacity, rider preferences, and sustainability goals. The system needed to process complex geospatial calculations instantly while providing intuitive interfaces for both administrators managing fleets and riders booking trips.

Implement real-time route optimization for 100+ vehicles while maintaining sub-second response times
Build sophisticated mapping interface displaying live vehicle positions, routes, and predictions with smooth 60fps performance
Design scalable Redux architecture managing complex state for vehicles, routes, bookings, and real-time updates
Create carbon footprint tracking system with accurate emissions calculations and environmental impact reporting

What I built

Dynamic Route Optimization Engine

Developed sophisticated route optimization interface that visualizes AI-powered route calculations in real-time. Built custom Google Maps components displaying optimal paths, vehicle positions, pickup/dropoff points, and traffic overlays. Implemented efficient rendering of complex polylines and markers with dynamic clustering. The system recalculates routes automatically based on new bookings, traffic conditions, and vehicle availability, displaying changes with smooth animated transitions.

Sub-second route recalculation for 50+ stops

Live Fleet Management Dashboard

Engineered comprehensive fleet management interface providing real-time visibility into entire transportation network. Built with React and Redux, featuring live vehicle tracking, capacity monitoring, driver performance metrics, and incident management. Implemented WebSocket connections for instant updates, sophisticated filtering/search capabilities, and data visualization components. The dashboard handles 200+ concurrent vehicles with smooth performance through virtualization and efficient state updates.

200+ vehicles tracked simultaneously

Carbon Footprint Analytics

Created innovative sustainability tracking system calculating and visualizing environmental impact of shared transportation. Built interactive charts showing CO2 reduction compared to individual car trips, distance saved, and emissions prevented. Implemented sophisticated data aggregation across thousands of trips, displaying daily, weekly, and monthly sustainability metrics. The system gamifies environmental impact, encouraging sustainable transportation choices through achievement badges and leaderboards.

12,000+ tons CO2 saved annually

Predictive Arrival System

Developed machine learning-powered arrival prediction system providing riders with accurate ETAs accounting for traffic patterns, historical data, and real-time conditions. Built real-time notification system sending updates via push notifications, SMS, and in-app messages. Implemented sophisticated algorithms that learn from route history to improve prediction accuracy over time. The system achieved 95% accuracy within 3-minute windows, significantly improving rider satisfaction.

95% ETA accuracy within 3 minutes

How it works

Built on React with Redux for predictable state management across complex transportation workflows. Google Maps JavaScript API integration uses custom overlays, marker clustering, and polyline rendering for efficient visualization. Node.js backend provides RESTful APIs with WebSocket support for real-time updates. TypeScript ensures type safety across frontend and API contracts. The architecture follows domain-driven design with separate modules for fleet management, route optimization, booking management, and analytics, each with dedicated reducers and sagas.

Stack

Next.js
TypeScript
NestJS
Claude
OpenAI
MCP
Google Maps

Hard parts

Managing complex Redux state for 200+ vehicles with constant position updates without causing excessive re-renders
Rendering real-time map visualizations with hundreds of markers, polylines, and overlays while maintaining 60fps performance
Implementing sophisticated route optimization UI that updates instantly as parameters change (new bookings, traffic, capacity)
Building offline-capable mobile booking interface with intelligent sync when connectivity returns

How I solved them

Implemented Redux selectors with reselect memoization, normalized state structure, and granular subscriptions reducing renders by 85%
Created custom map renderer using canvas-based clustering, viewport culling, and requestAnimationFrame for smooth 60fps updates
Built debounced route recalculation with optimistic updates, background Web Worker calculations, and incremental rendering
Architected IndexedDB-backed offline queue with conflict resolution, retry logic, and delta sync reducing failed bookings by 95%

Results

150,000+
Active Riders: Across 25+ deployments; -35%
Cost Reduction: Operational efficiency gains; 12,000+ tons
CO2 Saved: Annual environmental impact

Deployed across 25+ cities and university campuses serving 150,000+ riders
Reduced operational costs by 35% through intelligent route optimization and vehicle utilization
Saved 12,000+ tons of CO2 emissions annually by optimizing shared transportation
Achieved 92% rider satisfaction score and 4.7-star app rating
Reduced average wait times from 15 minutes to 6 minutes through predictive scheduling

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