| ABSTRACT |
The Smart Road Safety Prediction System Using Multi-Modal ML Data is designed to anticipate and
minimize road accidents by examining a wide range of factors that shape traffic conditions. Instead of focusing on a
single parameter, the system brings together historical crash records, traffic density patterns, weather variations, and other influencing variables to pinpoint areas and timeframes with a higher likelihood of accidents. By applying machine learning techniques, the system identifies patterns in past incidents and translates them into predictive insights that can support both commuters and authorities in making safer decisions. The core aim of this project extends beyond predicting accident-prone zones; it emphasizes strengthening overall road safety through practical and data-driven
recommendations. The workflow involves gathering diverse datasets, cleaning and preparing them for analysis, training predictive models, and finally making the results available through a simple, interactive platform. When historical data is combined with real-time information, the system becomes capable of guiding proactive actions such as dispatching emergency teams earlier, adjusting traffic signal timings, or issuing timely alerts to the public. Beyond its technical contribution, the implementation of this system carries meaningful social and economic value. By reducing accident risks, it can help protect human lives, minimize financial losses from damages, and improve the flow of traffic in growing urban environments. This project demonstrates the potential of artificial intelligence and advanced data analytics in tackling everyday challenges, ultimately creating a safer and more efficient transportation network. |
