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Smart City Passenger Traffic Analytics Platform Development
  1. case
  2. Smart City Passenger Traffic Analytics Platform Development

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Smart City Passenger Traffic Analytics Platform Development

embiq.com
Transportation
Smart City
Information technology

Challenges in Passenger Traffic Monitoring

Inability to accurately track real-time passenger density across transport networks, lack of actionable insights for route optimization, and high infrastructure costs associated with traditional traffic monitoring systems. Challenges include spatial database implementation, device parameter management, and multi-platform integration.

About the Client

Public transportation operator focused on optimizing urban mobility through data-driven infrastructure management

Project Objectives for Smart City Analytics

  • Develop real-time passenger traffic density monitoring system
  • Create customizable analytics dashboard for transport planning
  • Implement cross-platform compatibility for device management
  • Reduce infrastructure costs through wireless sensor network

Core System Functionalities

  • WiFi/Bluetooth signal-based passenger tracking
  • Crowd density heatmaps with temporal analysis
  • Multi-bus line comparative analytics
  • Device parameter monitoring dashboard
  • OpenWRT WLAN device integration

Technology Stack Requirements

Django
MongoDB
MySQL
WebSocket
iBeacon
LTE
OCKOTech
DSDi Suite
Cloud Tools (Jenkins, Jira, Confluence)

System Integration Needs

  • Existing transport infrastructure systems
  • Third-party traffic analytics platforms
  • Public transport scheduling software

Operational Requirements

  • Scalability for expanding transport networks
  • Data security compliance (GDPR/CCPA)
  • Real-time processing latency <500ms
  • 99.9% system availability SLA

Business Impact Projections

Enables data-driven route optimization reducing operational costs by 30%, improves passenger satisfaction through better resource allocation, and provides scalable infrastructure with 50% lower upfront investment compared to traditional systems. Facilitates predictive maintenance and dynamic timetable adjustments based on verified usage patterns.

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