How many hours need to be sought on a K-Value vs. AADT graph?

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Multiple Choice

How many hours need to be sought on a K-Value vs. AADT graph?

Explanation:
The correct response indicates that 30 hours is the standard timeframe typically analyzed on a K-Value versus Annual Average Daily Traffic (AADT) graph. The K-Value is a representation of the proportion of daily traffic that occurs during the peak hour, and understanding this relationship is crucial in traffic engineering for capacity analysis and road design. In transportation and traffic studies, a time period of 30 hours of traffic data is commonly used to establish reliable averages and patterns. This timeframe allows for a comprehensive assessment of peak traffic conditions and helps to account for variations that may occur during weekends and weekdays. By analyzing 30 hours of data, traffic engineers can better determine the K-Value, which assists in forecasting and planning for future traffic needs effectively. The other choices suggest timeframes that do not align with common practices in traffic analysis. For instance, 20 hours might not sufficiently capture variability found in traffic patterns, whereas 40 and 50 hours could lead to unnecessary data that may complicate the analysis without adding significant value. Therefore, using a 30-hour interval strikes a good balance between capturing adequate data trends and maintaining efficiency in analysis.

The correct response indicates that 30 hours is the standard timeframe typically analyzed on a K-Value versus Annual Average Daily Traffic (AADT) graph. The K-Value is a representation of the proportion of daily traffic that occurs during the peak hour, and understanding this relationship is crucial in traffic engineering for capacity analysis and road design.

In transportation and traffic studies, a time period of 30 hours of traffic data is commonly used to establish reliable averages and patterns. This timeframe allows for a comprehensive assessment of peak traffic conditions and helps to account for variations that may occur during weekends and weekdays. By analyzing 30 hours of data, traffic engineers can better determine the K-Value, which assists in forecasting and planning for future traffic needs effectively.

The other choices suggest timeframes that do not align with common practices in traffic analysis. For instance, 20 hours might not sufficiently capture variability found in traffic patterns, whereas 40 and 50 hours could lead to unnecessary data that may complicate the analysis without adding significant value. Therefore, using a 30-hour interval strikes a good balance between capturing adequate data trends and maintaining efficiency in analysis.

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