Managing highway safety needs accurate and detailed info. These can always be obtained from a range of sources. They might be used to discover safety focal points, problems, and risks. Understanding the data helps you to select suitable treatments and strategies for improvement.
The quality of these types of data may be measured using several criteria. Some of them contain completeness, order, regularity, and timeliness. Other factors contain ease of access and integration.
One of the most common types of essential safety data can be crash data. This can be collected by in-field observers and analyzed to distinguish potential problems. It can also be gathered through stationary cameras. Yet, these are expensive to get.
Another type of data is naturalistic traveling data. It really is recorded frame-by-frame to capture information regarding basic road safety. Aside from recording a driver’s facial area, this data is a strong source of regarding road safe practices.
In addition to being expensive to acquire, these kinds of data can even be challenging to code. To overcome these types of challenges, agencies may choose to use predictive types. Predictive models are systems that can analyze historical and current information to forecast potential crashes.
Crash data can also find more information be connected to traffic volume and street characteristics data. Relating the two can offer an accurate evaluation of the roadway and help transportation officials to determine high-risk areas. Safety pros can then target those locations with the the majority of potential for safe practices improvements.
Surrogate measures of safety may also be collected to name safety concerns before an actual crash develops. They are commonly observed through dashboard-mounted video cameras or in-field observers.
