Mountain highways are tough to navigate and even tougher to keep safe. The main issue is that it's hard to predict where crashes might happen. This is because there's not enough data and the data that does exist is tricky to analyze. There are too many zeros and too much variation. This makes it hard to figure out what causes crashes and how to stop them before they happen.

To tackle this problem, researchers gathered lots of data. They looked at road design, traffic flow, road surface quality, and weather conditions. They then created two new models to make sense of all this data. These models are called the Random Parameter Negative Binomial Lindley (RPNB-L) and the Random Parameter Negative Binomial Generalized Exponential (RPNB-GE). These models are pretty good at handling the zeros and the variation in the data.

The results showed that certain parts of the road, like tunnels and interchanges, are more dangerous. Also, the condition of the road surface and heavy rain play a big role in crash frequency. These findings are important because they can help in choosing the right safety measures for mountain highways.

However, there are a few things to consider. The models are good, but they're not perfect. They might miss some important factors that contribute to crashes. Also, the data used in this study is from China. So, the results might not apply to mountain highways in other countries.

Another thing to think about is that the models focus on crash frequency. They don't look at the severity of the crashes. This is important because a crash that causes a lot of damage or injuries is a bigger problem than a minor fender-bender.

In the end, this study is a step in the right direction. It provides valuable insights into what makes mountain highways dangerous. But there's still a lot of work to be done. Researchers need to keep digging into the data and coming up with new ways to keep these roads safe.

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