U.S. Traffic Accident ML Predictor

Using weather and traffic accident data analytics + machine learning to predict the likeliness of a traffic accident

RoadRisk Predictor: Leveraging Big Data & ML for Proactive Traffic Accident Prevention

Problem

Traffic accidents are a pervasive and devastating issue, with millions occurring annually across the U.S. While many factors are known, there's a critical need to move beyond general statistics and identify subtle weather-related correlations that contribute to accidents. More importantly, the challenge was to develop a personalized, real-time predictive tool that could assess the probability of an accident for a specific commute, empowering individuals to make safer travel decisions.

Solution

Our team developed RoadRisk Predictor, a data-driven project that analyzed vast datasets of U.S. traffic accidents and corresponding weather conditions over multiple years. We leveraged Tableau for correlation analysis and built a machine learning model that, when integrated with real-time weather and location data via APIs, provides users with a percentage chance of encountering an accident on their chosen route.

My Role

Lead Frontend UI/UX & API Integration Specialist:

  • Primary Frontend Developer: Designed and implemented the user interface for the predictive model, allowing users to select locations on a map and view real-time risk assessments.
  • API Integration Lead: Spearheaded the integration and management of both the Google Maps API (for location input and route visualization) and a real-time Weather API (for current conditions).
  • Data Sourcing Lead: Independently identified and acquired the extensive multi-year traffic accident and weather datasets for all 50 states, crucial for the project's foundation.
  • Team Contributor: Actively participated in overall project ideation and research, including reviewing academic papers on accident prediction and contributing to the understanding of key risk factors (e.g., black ice, motorcycle accidents, hotspot analysis).

Process & Key Features

Our project followed a robust data science and machine learning pipeline:

  • Big Data Acquisition: Sourced and managed two massive, multi-year datasets: U.S. traffic accidents and comprehensive weather data across all 50 states.
  • Data Cleaning & Integration: Performed extensive data cleaning and matching (likely using Python/Pandas, given the scale) to merge the disparate accident and weather datasets into a unified, analyzable format.
  • Correlation Analysis (Tableau): Utilized Tableau to visually explore and identify correlations between various weather conditions (temperature, precipitation, visibility, wind) and accident frequency/severity, moving beyond obvious links.
  • Machine Learning Model Development: Collaborated with the team to develop a predictive model using two distinct machine learning algorithms (e.g., Logistic Regression for baseline, Gradient Boosting or Random Forest for enhanced accuracy). The model was trained on the integrated historical data.
  • API-Driven Real-time Prediction: Designed and implemented the frontend interface that consumes real-time weather data (via API key integration) and user-selected location (via Google Maps API) to feed into the trained ML model, providing an on-demand accident probability.
  • Research-Backed Approach: Incorporated insights from academic research on accident prediction, black ice impact, and hotspot identification to inform model features and interpretation.

Impact & Results

  • Personalized Risk Assessment: Delivered a functional prototype capable of providing users with a personalized, real-time percentage chance of an accident on their commute, a novel application of traffic data.
  • Actionable Insights: Uncovered nuanced correlations between weather patterns and accident occurrences, contributing to a deeper understanding of road safety risks.
  • Advanced Data Handling: Successfully managed, cleaned, and integrated large, complex datasets, demonstrating proficiency in big data manipulation.
  • Full-Stack Integration: Showcased strong capabilities in integrating external APIs (Google Maps, Weather) with a custom frontend and a machine learning backend.
  • Proactive Safety Tool: Developed a tool with the potential to empower individuals to make safer travel choices and potentially reduce accident rates.

Key Learnings

This project provided extensive experience in big data processing, advanced data visualization (Tableau), machine learning model development, and complex API integration. It underscored the power of combining diverse data sources and cutting-edge technologies to create practical, impactful solutions for real-world safety challenges. My role specifically honed my skills in frontend development and the critical art of API management.