Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Figure 5.7. Mean TTI on the I-210 with adjustment to peak capacity and hourly distribution. 5.4 Results of the I-5 Scenario Testing The team tested a variety of scenarios using two facilities in Southern California with low levels of reliability. The first of these facilities tested is the I-5 facility in Orange County. As shown in Figure 5.8, I-5 is a 45-mile facility with four to six mixed-flow lanes and one to two barrier- separated high-occupancy vehicle lanes in each direction. There are also auxiliary lanes throughout the facility. Congestion is heaviest in the northbound direction during the p.m. peak period. Congestion in the southbound direction is similar for both a.m. and p.m. peak periods. Queuing can extend 12 to 15 miles and last for 4 to 6 hours. 80
Figure 5.8. Map of I-5 facility in Orange County. Selected Test Segment The geometry of the I-5 facility varies greatly along its 45-mile stretch. In an effort to produce results more comparable to real-world conditions, the study team selected a 6.5-mile segment with consistent characteristics to test using the C11 tool. The selected segment has five lanes in each direction from Jeffrey Road interchange to north of the SR-55 interchange. This segment (shown in Figure 5.9) was also selected because it is one of the most congested segments within the entire facility. 81
Figure 5.9. I-5 northbound test segment. The reliability results for the 6.5-mile segment are shown in Figure 5.10. The study team adjusted the default peak capacity of the tool until the TTI figures matched the PeMS baseline. In addition, the study team entered the actual hourly demand distributions to achieve hourly results better calibrated to the baseline conditions reported in PeMS. As can be seen in Figure 5.10, these adjustments resulted in TTI estimates very close to the actual baseline conditions. Unlike on the I-210 facility, the timing of the unreliability in the p.m. peak mirrored the actual TTI data. However, the onset of the unreliability in the p.m. occurred earlier than estimated by the C11 tool. 82
Figure 5.10. Mean TTI on I-5 for 6.5-mile segment Scenario Test #1: Incident Management Once the baseline conditions were calibrated, the study team tested various project scenarios previously identified as part of the I-5 CSMP (Caltrans 2012). The first scenario tested the effects of incident management (IM) on the facility. In this scenario, it is assumed that improved incident management leads to a 33 percent reduction in duration of a major collision from 45 minutes to 30 minutes. As shown in Figure 5.11, the C11 Reliability Analysis Tool allows for the input of a percent reduction in incident duration, so the percent reduction figures were used. The C11 tool estimated a noticeable improvement in reliability due to the reduction in incident duration. These results are shown in Figures 5.12 and 5.13. 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 6: 00 7: 00 8: 00 9: 00 10 :0 0 11 :0 0 12 :0 0 13 :0 0 14 :0 0 15 :0 0 16 :0 0 17 :0 0 18 :0 0 19 :0 0 C11 PeMS Capacity Data: Peak Capacity: 2,100 vphpl Hourly Distribution of Demand: Actual values 83
Figure 5.11. Incident management scenario input. Figure 5.12. Mean TTI on I-5 for incident management test. 84
Figure 5.13. Comparison of mean TTI and percent reduction on I-5 for incident management test. Scenario Test #2: Operational Improvements In the second scenario, the study team tested a combination of operational improvements along the facility. These improvements include constructing an auxiliary lane, widening an off-ramp, and creating a left turn lane to access an on-ramp. Unlike the incident management strategy previously tested, the C11 tool does not provide any automatic methods for modeling the effect of operational improvements. The study team decided to simulate the improvements in the C11 tool as an increase in capacity along the facility. One method to estimate the capacity improvement would be to use rules of thumb from case studies. In the case of the I-5 facility, the study team had available the results of microsimulation modeling for the I-5 CSMP. The microsimulation modeling showed (using changes in VMT) that the facility was able to handle more traffic after the operational improvements are made. As a result, the study team increased the capacity in the C11 tool by 2.3 percent (the percent VMT change estimated in the microsimulation) to 10,742 passenger cars per hour (pcph) as shown in Figure 5.14. This is a small change in capacity, but it reflects the significant congestion along the facility that operational improvements alone are unable to resolve. Figures 5.15 and 5.16 show the results of this change. Figure 5.16 shows that the scenario slightly improves reliability, with the biggest improvement observed at 4:00 p.m. An examination of the benefit-cost results suggests that the reliability improvement will be a small contributor to the overall project benefits. These benefit-cost impacts are discussed in more detail in Chapter 8 of this report. 85
Figure 5.14. Operational improvement scenario input. Figure 5.15. Mean TTI on I-5 for operational improvements test. 86
Figure 5.16. Comparison of mean TTI and percent reduction on I-5 for operational improvements test. Scenario Test #3: Dynamic Ramp Metering In the third scenario, the study team tested the implementation of dynamic ramp metering along the facility. The facility already has pre-timed ramp metering and a dynamic algorithm is expected to be more effective in reducing congestion and improving reliability. As with the previous scenario, the C11 tool does not have a lever to model dynamic ramp metering directly. The study team decided to model the scenario by increasing capacity (using estimates of VMT changes from microsimulation model). This scenario further increased the capacity along the facility by 6.3 percent to 11,204 pcph as shown in Figure 5.17. Figure 5.18 shows the results of this scenario compared to the baseline calibrated model. As can be seen in the figure, the dynamic ramp metering led to a further improvement in reliability. Figure 5.19 shows that the biggest improvement in reliability is predicted to occur at 4:00 p.m., followed by some improvements at 5:00 and 6:00 p.m. As with the previous scenario, the reliability benefits are predicted to be a small portion of overall project benefits. 87
Figure 5.17. Dynamic ramp metering scenario input. Figure 5.18. Mean TTI on I-5 for dynamic ramp metering test. 88
Figure 5.19. Comparison of mean TTI and percent reduction on I-5 for dynamic ramp metering test. Scenario Test #4: High-Occupancy Vehicle + General Purpose (GP) Widening Test In the fourth scenario, the study team tested widening the I-5 facility by adding a high-occupancy vehicle and a general-purpose lane in each direction. Testing this scenario was more straightforward than the previous scenarios, since the C11 tool allows the number of lanes to be adjusted. As shown in Figure 5.20, the study team increased the number of lanes in each direction to seven and increased the capacity to 14,200 pcph. These calculations do not take into account the previous improvements or the differences in capacity between general-purpose and high-occupancy vehicle lanes. Figure 5.21 shows the results of this scenario compared to the baseline calibrated model. As can be seen in this figure, these improvements are expected to improve reliability significantly. This is because the C11 tool estimates reliability impacts using volume-capacity ratios, and the improvements provide significantly more capacity on the facility. Figure 5.22 shows that the scenario improves reliability by over 45 percent for the entire p.m. peak period. 89
Figure 5.20. High-occupancy vehicle + GP widening scenario input. Figure 5.21. Mean TTI on I-5 for high-occupancy Vehicle + GP widening test. 90
