International Research Journal of Education and Technology

Road traffic accident analysis and the creation of a prediction model are the goals of a study. Data is gathered from a variety of police documents as well as the regional transport agency. To find patterns and connections between the parameters and the risk of an accident occurring, the data is evaluated using statistical methods. An analysis-based prediction model is created that uses the inputted data to forecast the probability of an accident occurring.

Estimating and assessing road traffic noise entails measuring the noise levels generated by vehicles at a specific location and using the data to assess the effects on the environment. Sound level meters and other specialized tools can be used to accomplish this. The average noise level, as well as the highest and minimum levels, are then calculated and analyzed using statistical techniques after the data has been collected. The gathered data is contrasted with pertinent noise standards or recommendations, such as those made by the World Health Organization or municipal laws. The results of these analyses can be used to develop strategies for mitigating the effects ofroad traffic noise, such as through the use of noise barriers or the implementation of quieter road surfaces.

To comprehend the effects of traffic on the environment and to establish strategies for emission reduction, it is critical to estimate and evaluate this carbon footprint. The type of cars being used, the distance being driven, the fuel efficiency of the vehicles, and the type of fuel being used must all be considered when estimating the carbon footprint from road traffic. These variables can be discovered by data collection and analysis, and they can be used to the IPCC Guidelines to quantify the total carbon emissions from vehicle traffic.

Keywords: Traffic Accident, Severity, Prediction model, Traffic noise, Intersection, Idling emissions, Carbon footprint.