NDA Project
Urban Mobile App with Smart Footfall Analytics
Our team developed a mobile application from scratch, designed to enhance city travel experiences. The app features weather and traffic tracking, a live heatmap, and occupancy prediction for places such as malls, cafés, shops, and restaurants. Additionally, it allows users to filter places by demographics (e.g., age, gender, income, and more) to find locations that match their crowd. With this app, users can forget common inconveniences like wasting time in crowded venues, feeling out of place, being misled by fake reviews, or ending up in the wrong social circles.
overview
About the Client
Our client is an entrepreneur who is a non-technical yet highly creative individual with bold and inspiring ideas. They wanted to develop a smart city and mobility solution for local citizens and travelers worldwide. The goal was to make everyday life in urban areas more convenient. Through an intuitive mobile app, users could easily plan their adventures.
- Front-End (Mobile)
- Back-End
- Cloud Services
Challenges
Building algorithms that could accurately predict venue occupancy and crowd patterns using dynamic data sources
Lack of required data at the project start
Ensuring the app could handle intensive data processing and visualization while maintaining smooth performance
Delivered data required reworking certain functionalities to match the actual structure and content
Designing an intuitive interface capable of displaying multiple data layers, requiring advanced UX design and technical architecture
Technologies
Front-End (Mobile)
React Native
Back-End
Nest JS
Cloud Hosting
AWS
Database
AWS RDS
PostgreSQL
Solution
1. Core Architecture and Functionality
Designed an intuitive yet advanced UX interface capable of displaying multiple data layers.
Implemented secure user authentication with Sign In / Sign Up features and personalized profile pages.
Developed an interactive heatmap to visualize popular and crowded areas in real time.
2. Engaging User Experience
Developed a leaderboard system to boost users’ engagement and competitiveness.
Enabled push notifications to deliver timely alerts and personalized recommendations.
3. Smart System and Data Performance
Integrated dynamic weather and traffic layers for smarter travel planning.
Engineered the app to handle intensive data processing while maintaining stable performance.
Project Management
Revised the project plan, reprioritized activities, and reallocated resources to keep the progress steady.
Refined and redesigned integrations once real data became available, ensuring alignment with actual content and minimizing delays.
Results the Client Got
Smart navigation
An interactive heatmap with a real-time view of weather and crowd density, helping users choose the best places to visit at any given moment
Predictive Analytics
The app analyzes provider data to forecast venue occupancy and anticipate future crowd trends
Smart Social Discovery
Users discover not only new places but also like-minded people, filtering venues by demographic metrics
Time-Based Planning
Every location can be viewed through three dimensions: past trends, current activity, and future predictions, enabling users to plan their journeys much better
FAQs
What problem does this case solve?
This application bridges the gap between static location listings and real-time city analytics by providing live crowd predictions and demographic data trends for urban travelers and citizens.
What technologies were used to build the platform?
The solution was developed entirely from scratch, using Node.js for the backend, React Native for cross-platform mobile development, and AWS Aurora for secure, high-performance data processing.
How does the system handle real-time data and predictions?
The system processes continuous data streams from external providers, applying analytical models to predict venue occupancy and display accurate crowd forecasts across multiple layers (traffic, weather, demographics).
How did the team ensure performance with heavy data visualization?
Our engineers optimized the data flow and created an advanced UX architecture to handle this high-volume visualization, ensuring the app maintains stable performance even under intensive processing loads.
How did the project adapt to changing requirements?
The development plan was restructured through agile reprioritization. Once the team was available to continue their work, they redesigned the integrations to align with actual provider formats while minimizing project delays.
What’s the business potential of such a solution?
The solution’s architecture can be adapted for smart city analytics, retail footfall prediction, or mobility planning, making it a scalable foundation for urban data-driven platforms.
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