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Real-Time Scooter Fleet Management Web Application Development

teacode.io

Automotive

GPS

Environmental Services

Challenges in Scooter Fleet Management

Hive faced operational inefficiencies due to manual scooter deployment tracking, lack of real-time data visibility, and difficulty scaling operations across multiple European cities. The absence of centralized parking spot optimization and vehicle status monitoring led to increased downtime and reduced scooter availability.

About the Client

Company specializing in electric scooter rentals for urban mobility, focusing on eco-friendly transportation solutions

Goals for the Web Application

Develop a real-time web platform for scooter fleet monitoring
Enhance operational efficiency through automated deployment tracking
Provide actionable insights for parking spot optimization
Support scalability across multiple geographic regions

Core System Functionalities

Interactive Mapbox-based dashboard with real-time scooter locations
Battery status and maintenance alerts for each vehicle
Advanced filtering by location, status, and usage metrics
Parking spot utilization analytics and recommendation engine
Multi-city deployment management interface

Technology Stack Requirements

Mapbox for interactive mapping

React.js for front-end interface

Node.js with WebSocket for real-time data

PostgreSQL for spatial data storage

AWS for cloud scalability

System Integration Needs

Existing mobile app API for user/scooter synchronization
GPS tracking hardware integration
Battery telemetry systems
Third-party analytics platforms

Performance Expectations

Support 10,000+ concurrent scooter connections
Map rendering performance under 2 seconds
99.9% system uptime SLA
Role-based access control for security
EU GDPR compliance for data handling

Expected Business Outcomes

The web application will reduce operational downtime by 40% through real-time monitoring, enable 200% faster fleet scaling across new cities, and improve scooter availability by 35% through data-driven parking spot optimization. The intuitive interface will decrease staff training time by 60% while maintaining system performance at scale.