Status: Ongoing (Showcased as a current project)
Associated with: Thynk360
As cities become more congested and commuters spend increasing time on the road, intelligent routing systems are becoming a necessity. To address this, I am developing a Traffic Route Optimizer, a data-driven solution designed to recommend optimal travel paths based on real-time traffic conditions. This AI project, under Thynk360, leverages geospatial analysis, live data APIs, and visualization to enhance decision-making for everyday travelers.
Tools & Technologies Used
- Programming Language: Python
- APIs & Services: Google Maps API, OpenStreetMap
- Libraries: Pandas, NumPy, Matplotlib, Folium, Geopy
- Data Types: Real-time traffic data, route coordinates, ETA
- Visualization Tools: Matplotlib, Folium for interactive maps
Description
The core functionality of the Traffic Route Optimizer is to take a starting point and destination as input and return the most time-efficient route, factoring in live traffic updates. The project started by integrating the Google Maps Directions and Traffic API, which provides detailed route information, including alternative paths, estimated travel times, and congestion levels.
Using this data, I built a Python-based system that fetches multiple route options and evaluates them based on parameters such as distance, ETA, and congestion indicators. I also visualized route comparisons using Folium maps layered with color-coded paths and traffic overlays. The final interface displays these routes with annotated markers, travel time breakdowns, and even road alerts (e.g., construction zones or accidents, when available).
Key Highlights
- Integrated Google Maps API to fetch and process real-time routing data.
- Used Pandas and Geopy for geospatial calculations, such as measuring detours and calculating time differences across routes.
- Built interactive visualizations showing route options using Folium with custom color layers and traffic markers.
- Developed a modular routing engine with adjustable priority factors (e.g., shortest time vs. shortest distance).
What You Learned / Achieved
This project significantly enhanced my understanding of geospatial data analysis, API integration, and real-time decision modeling. By working with GPS coordinates, traffic feeds, and route matrices, I learned how to interpret and process live environmental data in the context of optimization.
I also gained practical skills in using third-party APIs like Google Maps—learning how to authenticate, parse route objects, and manage request quotas. Working with tools like Folium and Geopy introduced me to spatial visualization, a critical aspect when translating data into user-friendly interfaces.
One of the biggest insights from this project was the importance of multi-criteria decision-making. I experimented with weighted scoring systems that factor in user preferences—such as avoiding toll roads, prioritizing shorter distances, or reducing time spent in heavy traffic.
Future Plans
The next milestone is to evolve this into a mobile-friendly application that users can interact with via voice or text inputs. I’m also exploring integration with public transit APIs to provide multimodal routing options (e.g., combining car + train).
Another planned enhancement is to include predictive traffic modeling, using historical data and machine learning to estimate future traffic patterns based on time of day and location. Additionally, I’m aiming to add route recommendation explanations, helping users understand why a specific path is preferred—building transparency and trust in the system.
This project represents my commitment to building smart, user-centered, and AI-powered mobility solutions under the Thynk360 initiative.
