Weather plays a critical role in the frequency and severity of traffic accidents, creating significant economic and societal impacts worldwide.

The effect is substantially amplified in regions with harsh weather conditions, making it critical to have predictive models for traffic and accident rates associated with a particular weather pattern. Private insurance companies are one of the stakeholders who are highly motivated to predict and prepare accident data to optimize their portfolios.

Hence, in this study, we will discuss the predictive model and its performance issues, from a private insurance perspective, to estimate the impact of bad weather on traffic and lesions.

The Relationship Between Accidents, Traffic, and Weather

The relationship between traffic accidents and weather is complex and rooted in the interaction between infrastructure, vehicle, driver behavior, and environmental factors.

According to the National Highway Traffic Safety Administration (NHTSA), weather-related accidents account for nearly 20% of total annual car crashes in the USA. Accidents increase during periods of adverse or extreme weather, such as heavy rainfall, snow, and ice storms.

One of the crucial factors responsible for the high accident rates, during extreme or adverse weather conditions, is visibility. Precipitation in the form of ice, snow, rain, and hail reduce visibility, thereby increasing the chance of accidents.

The second most critical factor is wetness on the road surface, caused by rain or snow, leading to the reduction in friction between the tire rubber and pavement. Reduced friction increases driver response time to critical situations, leading to more accidents.

The third factor responsible for the high incidence of accidents during bad weather is reduced vehicle stability, caused primarily by slippery road surfaces.

A wet or icy road makes it difficult for drivers to maintain traction, control, and stability of their vehicle, especially while turning or braking suddenly. Reduced vehicle stability is known to be a leading cause of rollover accidents and collision.

As demonstrated, weather plays a significant role in the number and severity of traffic accidents. The development of predictive models for weather-related accidents becomes essential in mitigating the economic and societal impacts of bad weather.

These models can be used to identify areas with the highest weather-related accident risks, and prepare and optimize portfolios for insurance companies, among other benefits.

Private insurance companies require accurate models to predict the number and severity of accidents in their portfolio, and a predictive model for weather-related accidents could be used to help mitigate economic and social impacts of accidents.

Collecting historical and current accident data, visibility, wetness, and stability, parameters like temperature, precipitation, and wind speed, we can use machine learning algorithms for predictive modeling. In addition, combining accident data with alternative data sources like major events, road works, or infrastructure changes, can further improve the performance and accuracy of predictive models.

Predictive Modeling Challenges

Developing predictive models for weather-related accidents come with significant challenges, some of which are:.

Data Quality and Availability

Data quality and quantity of past and current accident data are crucial drivers of model performance. Without sufficient historical and current data, models will fail to generate reliable and accurate predictions.

Also, the quality and the consistency of accident reports are crucial for model performance. In some cases, events are over or underestimated, and data recording is subjective.

Diversity of Accident Scenarios

The variability of accident scenarios (e.g., severity and type of injury, vehicle involved, etc.,) poses a significant challenge for developing predictive models.

Considering all potential scenarios and coincidences can create a model that is too complex and unreliable. However, building models that are too simple will fail to capture all the necessary information needed to make accurate predictions.

Seasonal Variability

Seasonal variability and the impact of different weather patterns, across the different phases of the year, is another critical driver of accident risk.

For instance, sunny weather may increase the number of motorcycles and bicycles on the road, increasing the risk of accidents. Alternatively, snow and ice storms may reduce the number of vehicles on the road, reducing the risk of accidents.

Realtime Data Processing and Update

Real-time data processing and update are crucial for the implementation of weather-related accident models. To achieve this, model developers need support from a reliable data storage infrastructure and efficient algorithms.

Predictive models will require constant update and maintenance to remain relevant, reflecting new trends, incoming data, and patterns indicative of accidents.

Conclusion

The ability to predict and prepare for accidents due to bad weather is crucial for minimizing economic and societal impacts.

The above challenges are opportunities to refine our develop predictive models that insurance companies and other stakeholders can use in their operational and strategic decision-making.