High-level Road Traffic Management App to Monitor Roads and Reduce Congestion

Solutions We Offered

Capital Numbers pooled its best-in-class DSA experts to do the job. Our experts started by integrating high-level data structures and algorithms into the app dashboard.

We incorporated data structures, like hash tables and trees, into the Angular-powered app dashboard. This allowed us to quickly access and process information, such as vehicle counts and incident reports, essential for real-time road traffic management.

We embedded complex algorithms for analyzing traffic patterns, predicting congestion, and optimizing traffic flows. For example, shortest path algorithms helped us determine the quickest routes for emergency vehicles, and sophisticated algorithms helped us predict traffic trends based on historical data.

Data structures like queues and hash maps helped us manage and organize information such as vehicle types and traffic signal lights. Graph algorithms helped us model road networks to find and detect congestion points, while optimization algorithms helped us dynamically adjust signal timings based on current traffic conditions.

Additionally, linear interpolation formulas helped us smoothly estimate traffic density at specific road intersections. Advanced image processing algorithms helped us detect accidents, stalled vehicles, and parking spaces.

In addition to the above, we incorporated color-coded polylines on Bing maps to differentiate between different types of cables and their specific functions, reducing the risk of errors during roadside repairs. We made this visual distinction to help technicians quickly locate and identify different cable pathways that may run close to each other, preventing accidental damage during maintenance work.

A lot of graphical representations were done using Kendo UI for high-quality visualizations. We also used TypeScript to adjust and fine-tune various frontend elements.

It took us around three years to develop and sync all the above algorithms, data structures, and visualizations with the .NET-powered app backend. We worked on the project using Azure DevOps deployment pipeline to ensure every integration is automatically tested and deployed to the Visual Studio Community source control, reducing configuration drift and deployment issues.