Problem 3: Shenendehowa Campus On the western edge of the network is the entrance to the Shenendehowa (Shen) Campus (Intersection A). It is a signalized, fully-actuated intersection. It has two lanes eastbound (left-through and exclusive right), two lanes westbound (left and through-right), two lanes northbound (left and through-right), and one lane southbound (left-through-right).
Large volumes exist on the eastbound, westbound, and northbound approaches. Exhibit 2-26 shows typical volumes for the AM peak hour. Traffic enteLevel of Servicend leaving the Shenendehowa campus uses the westbound left and eastbound right. Those flows are highly peaked. The volumes to and from the north are extremely small because a church is the only building generating traffic on that approach. On a typical weekday during the peak hours, there isn’t much traffic going into or out of the church. Analysis Plans We’re going to use this intersection to examine three issues: peak hour factor, heavy vehicles, and impact dilution. We’ll examine the first two issues in the context of the AM and PM peaks (AM Existing & PM Existing), while we’ll use the PM With condition for the third. Sub-problem 3a: AM & PM Peak Hour - Existing Conditions
Sub-problem 3b:
Discussion: |
Problem 3 Printable Version
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Sub-problem 3a: Shenendehowa Campus AM & PM peak - Existing ConditionsPeak
Hour Factor We can use the highly peaked flows at the entrance to the Shenendehowa campus to show how the peak hour factor works and the effect it has. Using data for this intersection will show how the typical method for applying the peak hour factor might or might not lead to the right assessment of the performance conditions in some situations. Discussion: Traffic
engineers hold different perspectives on the peak hour factor. Some compute
values for each clock hour (3-4, 4-5, etc.). Some consider each sequence of
four 15-minute time periods and use the sequence with the maximum total
volume for the peak hour factor. Some predicate PHF
calculation on the sequence of 15-minute time periods that has the maximum
flow for the movement, others, the maximum total intersecting volume for
the intersection. Still others focus on demand, not volume as the basis
for computing the PHF. What do you do? What do you think should be done if
the data were available? with Peak Hour Factor Analysis |
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Sub-problem 3a: Shenendehowa Campus AM & PM peak - Existing Conditions
The AM peak volumes by 15-minute interval
are presented in Exhibit 2-27. The peak hour is highlighted in yellow. As can
be seen, there is high variability in most of the flows. Only the
eastbound through is relatively consistent. For example, during the peak
hour, the eastbound right ranges from 54 to 111 vehicles in a 15 minute
period. The westbound left ranges from 106 to 45 (because of the school
traffic), while the northbound right ranges from 41 to 79.
If we do a standard peak hour analysis for the AM peak hour, we get an overall level of service C. Dataset 22 contains the complete input data for the AM peak hour analysis. Exhibit 2-28 shows the delays and levels of service for each of the movements. The largest delays are associated with the westbound left and the conflicting eastbound through. The westbound left movement clearly has the worst LOS (D).
with Peak Hour Factor Analysis |
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Sub-problem 3a: Shenendehowa Campus AM & PM peak - Existing ConditionsThe PM peak is similar. The 15-minute counts are shown in Exhibit 2-29, and the peak hour is highlighted in yellow. Comparing the PM peak to the AM peak in Exhibit 2-27, we can see that the PM peak eastbound rights and the westbound lefts are significantly less then in the AM peak. Also, the westbound through volume is much larger. Finally, there’s a major change in the percentage of heavy vehicles. In the AM peak, the percentages were 15% for the westbound left and 9% for the eastbound right. In the PM peak, they are 26% for the westbound left and 41% for the eastbound right.
Exhibit 2-30 compares the delays and levels of service for the AM and PM peaks. Dataset 23 contains the input data for the PM peak analysis. Overall, the delays in the PM peak are slightly smaller than the AM peak.
The question we want to raise is this: do either of these conditions shown in the table above occur? Are these good representations of the conditions in either peak hour? Are they pessimistic? Optimistic? We need to look at the individual 15-minute intervals to find the answer. We must be careful not to oversimplify this analysis, because there isn’t any real carryover in queues from one 15-minute interval to the next. If there were, we would need have to do a series of cascading analyses across sequential slices to capture the effects of queue spillover. with Peak Hour Factor Analysis |
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Sub-problem 3a: Shenendehowa Campus AM & PM peak - Existing ConditionsLet’s first look at the 15-minute intervals that makeup the AM peak. The volumes were shown in Exhibit 2-27. We have to create four datasets and get four separate results. Then we can compare those results with the original “peak hour” solutions we obtained, shown in Exhibit 2-30, to see where the differences are. We could also perform a pair of analyses in which the peak hour factors are different for every movement. That produces yet a third assessment of the intersection’s performance. Click here to see the input data for each of these analyses. The delays and levels
of service that we obtain for the AM peak are shown in Exhibit 2-31.
The first line shows our original AM peak hour analysis. The next
shows the results if we use movement-specific peak hour factors, and the
last four lines show the results for each 15-minute interval during the PM
peak. (Where a movement has zero flow, no LOS has been computed.)
In the original analysis, the average delay per vehicle is 25.9 seconds. When we use movement-specific PHF values, the average delay is 38 seconds, or 47% higher. Is this realistic? We’ll see. The average delays on a 15-minute basis range from 17.8 to 29.2 seconds per vehicle. So the 38.0 seconds is clearly too high. The original analysis underestimates the delays during the peak 15 minutes where it is 13% higher.
Discussion: with Peak Hour Factor Analysis |
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Sub-problem 3a: Shenendehowa Campus AM & PM peak - Existing ConditionsA detailed look at the individual 15-minute intervals from Exhibit 2-31 is also instructive. The eastbound through-and-right delays range from 20.8 to 34.9 seconds; the northbound approach delays range from 19.6 to 32.6 seconds; and the westbound lefts range from 28.4 seconds all the way to 51.6 seconds per vehicle. For the rest of the movements, the delays are more consistent. Notice that in all of the interval cases and in the base case, each of the delays is lower than those produced by the analysis done using PHF by movement. When comparing the original peak hour analyses to each of the 15-minute interval analyses, it is obvious that the actual intersection performance levels are not consistent with the predicted AM or PM peak hour conditions. In the worst 15-minute interval (8:00 to 8:15), the overall LOS is C. For this interval, there are significant increases from the PM peak hour results for the eastbound right, the westbound left, and all three of the southbound movements. Looking at the best performing 15-minute interval (8:30 to 8:45), the overall LOS is B. For this interval there are significant decreases (from the base case) in the delays for the eastbound though-left, the westbound left, and all three northbound movements. with Sub-problem 3a |
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Sub-problem 3a: Shenendehowa Campus AM & PM peak - Existing ConditionsExhibit 2-32 shows the results of our sensitivity analysis for the PM Existing condition. The overall delay doesn’t change very much. It ranges from 19.4 sec/veh up to 24.1. The delays for the individual movements are similar except for the eastbound left-through. It varies from 20.2 to 31.1 sec/veh. That’s a 50% change.
Discussion: with Sub-problem 3a |
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Sub-problem 3a: Shenendehowa Campus AM & PM peak - Existing ConditionsHeavy
Vehicles
Exhibit 2-33 demonstrates
the differences in delay that will be obtained by neglecting the percent heavy
vehicle correction. In both the AM and PM conditions, the delays are smaller when the
correction factors are omitted. The differences in the AM peak are
slightly larger than they are during the PM peak. This is due to the
slightly higher volumes that occur during the AM peak.
Discussion: to Sub-problem 3b |
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Sub-problem 3b: Shenendehowa Campus PM peak - With ConditionsTraffic
Growth and Sensitivity The
volumes that will be used to analyze the 2004 PM With and PM
Without conditions are shown in Exhibit 2-34.
There is a small estimated growth on the eastbound
through movement, and the westbound through movement and the rest of the
movement volumes are unaffected by the site development.
For
the input
datasets
click
here.
Exhibit 2-35 shows what we find from analyzing each of these three conditions. Comparing the with and without conditions, the changes in overall delay are quite small. To check the robustness of this comment (i.e., the sensitivity to uncertainty in the site development volumes), we looked at an additional with condition with 30% more site-generated traffic. The delays still have not changed much. This tells us that this intersection is not significantly affected by the site development at Maxwell Drive.
to Discussion |
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Problem 3: Shenendehowa Campus
Discussion We’ve also seen that it is important to pay attention to the heavy vehicle percentages. This may be of particular importance in a situation like this, where the Shenendehowa intersection serves a lot of school buses. We might not initially realize the importance of accounting for their presence in the traffic stream, but doing so changes the delays considerably. Lastly, we’ve seen that there are ways to check for impacts from site-generated traffic. We were relatively formal about that, doing the performance assessment with and without the site-generated traffic, looking at the resulting changes in delay, and deciding that the impact was insignificant. Sometimes, for expedience, analysts make a decision based on the percentage increase in intersecting traffic that results from the site-generated traffic.
Discussion: to Problem 4 |