Dedicating Lanes for Priority or Exclusive Use by Connected and Automated Vehicles (2018)

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.

91 This chapter expands on the detailed analysis results produced by the simulations. This chap- ter organizes the analysis results in terms of the sensitivity parameters that were assessed as part of the evaluation. 6.1 Priority Versus Exclusive Access The first set of assessments was conducted to determine whether there would be benefits or disbenefits from dedicating lanes to CAVs. The baseline results included HOV access on DLs. For the I-66 study sites, an average of 20% to 25% of the traffic consisted of HOVs utilizing the DL. Table 6.1 shows the mobility performance measures under various types of DL use. The percentages shown in the table are changes from the base case. A positive change in travel time indicates disbenefits, whereas a positive change in throughput indicates benefits. Two scenarios of lane dedication were assessed: • Priority lane access, under which CAVs were allowed on the DLs in addition to the HOVs that were already allowed. – As shown in Table 6.1, at lower market penetration (10%), CACC application improved the network efficiency by increasing the throughput of DLs. An increase in the average speed of vehicles on the GPLs and DLs also occurred. However, DSH application was found to reduce the network performance. Specifically, it increased the average travel time by over 41%. This result was due to unequipped vehicles cutting in front of equipped vehicles whose speeds were lower. – At higher market penetration (25%), dedicating lanes to CAVs and HOVs caused over- saturated DLs and undersaturated GPLs that resulted in significant gridlocks. • Exclusive lane access, under which only CAVs were allowed on the DLs. – As shown in the table, at lower market penetration (10%), the DLs were underutilized. For example, in the base case, about 25% of vehicles were using the DLs; as a result of the simulation, this number dropped to 10%. This change caused significant increase in overall network travel time of about 90%. – At higher market penetration (25%), CACC applications reduced network travel time by 13%. In addition, the average speed of vehicles on the DL increased significantly when all of the vehicles were equipped with CACC. Table 6.1 also shows environmental and safety performance measures. In general, DSH applications showed increased emissions and fuel consumptions. This result primarily reflects the goal of the application, which is to distribute congestion throughout the network so that the shockwaves are minimized. In contrast, CACC applications improved fuel efficiency when C H A P T E R 6 Evaluation Results

92 Dedicating Lanes for Priority or Exclusive Use by Connected and Automated Vehicles implemented as exclusive lane use at MPRs higher than 25%. At lower market penetration (10%), shared lane use showed a marginal improvement in fuel consumption. Figure 6.1 compares the performance of DSH when implemented on shared and exclusive lanes at 10% market penetration as percentage change in average throughput from the base case. When the DLs are exclusive to CAVs, simulations showed reductions in throughput when the DSH application was deployed at 10% market penetration. An average reduction in throughput of 10% was found. When the DLs were shared with HOVs, however, the reduction in throughput went down to 4%. Regarding safety performance measures, the lane friction increases with increasing market penetration and exclusive lane access to CAVs. This increase warrants restricted access to DLs and physical barriers to increase the safety of the network. The DSH application aims to lower the probability of shockwaves in the network by harmonizing the speeds across the freeway based on downstream congestion. Figure 6.2 demonstrates the shockwave performance mea- sures of the DSH application using speed contours. The two-colored speed contours show the spatio-temporal distribution of observed speeds in the network collected during the simulation, with the vertical axis representing time-series and the horizontal axis representing the direction of travel. Three cases are shown in the figure: (1) the base case, (2) CAVs sharing the DLs with HOVs at 10% market penetration, and (3) CAVs with exclusive access to DLs at 25% market penetration. The team also assessed an exclusive lane use scenario with 10% market penetration, but that scenario was not included in this comparison because it caused significant increase in travel time due to volume imbalance on the lanes (see Table 6.1). As shown in Figure 6.2, the base case involves sudden slow-downs in traffic at multiple places. This situation can lead to hard braking and shockwaves. In the shared DL scenario, these slow-downs are spread across the network, thereby reducing the probability of shockwaves. During visual audits of these simulations, it was found that due to the low market penetration, unequipped HOVs were cutting in front of equipped CAVs, which were traveling at a lower average speed than the general traffic. This situation caused a 41% increase in average travel time and a 5% reduction in throughput. In the exclusive DL scenario with 25% market penetration, the slow-downs reduced dramatically. A marginal 1% increase in the network throughput also occurred, but the increase in throughput came at the expense of a 12% increase in travel time. To summarize the comparison between the priority lane and exclusive lane cases, the best strategy depends on the market penetration. At lower market penetration, sharing lanes with HOVs likely offers more benefit, and at higher market penetration, dedicating lanes exclusively to CAVs enhances overall network mobility. In this evaluation, the lower and higher market penetration percentages were defined as the percentage of DLs (10% and 25%, respectively). Mobility Performance Measures Shared with HOVs Exclusive CAV DLs 10% CACC 10% DSH 10% CACC 10% DSH 25% CACC 25% DSH Change in Travel Time 1% 41% 92% 73% -13% 13% Change in Average Speed (GPV) 3% -10% -31% -28% 0% -11% Change in Average Speed (DLV) 6% -37% 37% -20% 36% -16% Change in Overall Fuel Consumption -1% 65% 83% 65% -16% 21% Lane Friction (mph) 2 2 35 26 10 15 Table 6.1. Network performance under shared and exclusive DL use.

Evaluation Results 93 Figure 6.1. Comparison of DSH performance measures with and without exclusive DL access. Figure 6.2. Speed contours for I-66 westbound direction under shared and exclusive lane use.

94 Dedicating Lanes for Priority or Exclusive Use by Connected and Automated Vehicles 6.2 Impact of Market Penetration In this analysis, the project team reviewed the impact of market penetration on network per- formance when dedicating lanes to CAVs. 6.2.1 CACC When assessing the CACC application, it was found that at lower market penetration, sharing lanes with HOVs would prevent oversaturation of GPLs. Hence, for 10% market penetration, the team considered shared lane use, whereas for higher market penetrations, only CAVs were allowed on the DLs. Figure 6.3 shows comparison of vehicle throughputs at differing levels of market penetration for the I-66 case study site. The primary axis data shown by the left-hand vertical scale demonstrates the percentage difference in throughput when compared to the base case. All the cases showed improvement in throughput, peaking at a market penetration of 35%. This pattern occurs because of the significant volume imbalance that happens at higher levels of market penetration. The I-66 case study site has three GPLs and one DL. At 25% market pen- etration, this lane configuration results in an equitable distribution of demand. At 35% market penetration, the demand distribution becomes 35% for DL versus 22% for GPL, and at 45% market penetration, the demand distribution becomes 45% for DL versus 18% for GPL. Even at a higher market penetration, however, the lane dedication allowed higher throughput on DLs. Figure 6.4 shows a comparison of maximum DL throughput that was achieved through CACC implementation at differing levels of market penetration. As shown, at 10% market penetra- tion, sharing DLs with HOVs can improve the throughput by up to 21%, whereas not sharing -1% 1% 3% 5% 7% 9% 3: 00 P M 3: 10 P M 3: 20 P M 3: 30 P M 3: 40 P M 3: 50 P M 4: 00 P M 4: 10 P M 4: 20 P M 4: 30 P M 4: 40 P M 4: 50 P M 5: 00 P M 5: 10 P M 5: 20 P M 5: 30 P M 5: 40 P M 5: 50 P M 6: 00 P M 6: 10 P M 6: 20 P M 6: 30 P M 6: 40 P M 6: 50 P M V eh ic le T hr ou gh pu t D if fe re nc e (% ) Time 10 % CACC + HOV with 1 DL 25 % CACC with 1 DL 35 % CACC with 1 DL 45 % CACC with 1 DL Figure 6.3. Impact of market penetration on CACC DLs (I-66 case study site).

Evaluation Results 95 DLs with HOVs can decrease the throughput by almost 60%. The latter result arises primar- ily because fewer vehicles are allowed on the DLs. When not shared with HOVs, CACC DL throughput increases as market penetration goes up. At 25% market penetration, a marginal reduction occurs in throughput, whereas at 35% and 45% market penetration, the throughput increases by up to 32% and 60%, respectively. Figure 6.4 also shows the average travel speeds on the DL under each condition. As shown, the travel speeds are much higher when exclusive lane access is given to CACC-equipped vehicles, even when the throughput is higher. Even when CACC-equipped vehicles have exclusive DL access, the average travel speeds reduce to 54 mph when the CACC market penetration increases to 45%. Figure 6.5 shows the environmental impacts of CACC when implemented in a DL setting at differing levels of market penetration. In the figure, the 10% market penetration scenario refers to shared DL use with HOVs. In the figure, fuel consumption is shown as a comparison to the base case, whereas emissions are shown as aggregate values of CO and NOx as reported by the emissions model. Figure 6.4. Maximum throughput of DLs (I-66 case study site). Figure 6.5. Fuel consumption and emissions impact of CACC at different market penetrations.

96 Dedicating Lanes for Priority or Exclusive Use by Connected and Automated Vehicles Figure 6.5 shows that the fuel consumption and emissions reduce as a function of market penetration, with most of the benefits being received at 35% market penetration or higher. 6.2.2 DSH A similar assessment was performed for DSH. Unlike CACC, the DSH application was not shown to increase the freeway throughput considerably; consequently, MPRs beyond 25% were not assessed for DL situations. The four levels of market penetration were assessed for mobility impacts, as shown in Figure 6.6. In the figure, 10% and 25% cases were implemented with DLs, whereas 50% and 100% were implemented without DLs. Additionally, the 10% scenario had a DL that could be used for HOVs and CAVs, whereas the 25% scenario had a dedicated CAV-only lane. The performance measure shown in the figure is the percentage change in throughput from the base case at 10-minute resolution. As shown in Figure 6.6, market penetration of DSH does impact the throughput of freeways. At 10% market penetration, the figure clearly shows an average reduction in throughput, whereas at 25% market penetration, the throughput increases by nearly 1%. At 50% market penetration with no DL, throughput is again reduced, and at 100% market penetration, throughput again increases by about 1%. As mentioned before, the primary objective of the DSH application is safety in terms of reducing shockwaves and harmonizing speeds across the freeway corridor to prevent hard braking and acceleration. To demonstrate these impacts, the project team logged simulated spatio-temporal speeds on the I-66 westbound direction, which was the DSH implementation direction. Figure 6.7 shows these speed contours at differing levels of market penetration and compared to the base case. As shown in Figure 6.7, the base case showed large variation in speeds across both spatial and temporal axes (horizontal and vertical). When DSH was introduced in the DL at 25% market Figure 6.6. Comparison of throughput for DSH under different market penetrations.

Evaluation Results 97 Figure 6.7. Speed contours for I-66 westbound direction under differing DSH market penetrations. penetration, however, the slow-downs scattered around the network, thereby reducing the prob- ability of shockwaves. At higher market penetrations, without dedicating lanes for CAVs, the DSH application was able to distribute the slow-downs in the network so that there were no significant shockwaves. 6.3 Combinations of Applications To understand how combinations of applications would impact the network performance of CAV DLs, the project team assessed combinations of CACC and DSH applications at 25% market penetration. The applications were combined so that the Leader of every CACC string on the DL would receive and respond to DSH recommendations of desired speeds. Figure 6.8 shows the three cases compared for this evaluation and the throughput difference under differ- ent applications for the I-66 case study site. As shown in the figure, the three cases compared were DSH implemented at 25% market pen- etration, CACC implemented at 25% market penetration, and both applications implemented at 25% market penetration. The figure shows that both DSH and CACC applications improved the overall freeway throughput. The DSH improved the throughput by about 1%, and the CACC

98 Dedicating Lanes for Priority or Exclusive Use by Connected and Automated Vehicles improved the throughput by about 2%. The throughput improvements were assessed under the same overall demand. The combination of DSH and CACC showed similar benefits as the CACC-only case, with an average throughput improvement of over 2%. Figure 6.9 shows the other performance measures for 25% market penetration of DSH, CACC, and both applications together. As shown, the DSH application increased the travel time and fuel consumption when compared to the base case. This increase occurred because the applica- tion reduced the speed of the equipped vehicles in response to slow-downs that existed down- stream. This reduces the probability of shockwaves. Unlike the DSH application, the CACC application reduced the total travel time by 13% and fuel consumption by 15%. In addition, the CACC application caused an increase of over 25% in the speed of equipped vehicles. When both the CACC and DHS applications were combined, the travel time and fuel consumption were reduced by 13% and 16%, respectively. Figure 6.10 shows the performance of the combination of CACC and DSH applications under two levels of market penetration(10% and 25%) when CAVs have exclusive access to the DL. The bar plots shown on the primary axis demonstrate the difference from the base case in vehicle throughput Figure 6.8. Impact of combination of CAV applications at 25% market penetration. Figure 6.9. Performance measures under combinations of CAV applications at 25% market penetration.

Evaluation Results 99 throughout the freeway. As shown, at 10% market penetration, there is a significant reduction in throughput, averaging 9%. This result occurs primarily because fewer vehicles have access to the DL when compared to the base case, in which about 25% of HOVs used the DL. At 25% market penetration, however, there is an increase in average throughput on the freeway lanes, averaging 2%. This increase occurs because, in comparison to the base case, an equal number of vehicles can utilize the DLs, and the vehicles on the DLs travel at harmonized speeds and with closer headways. The project team also analyzed the safety benefits of combining CACC with the DSH applica- tion, in terms of reduction in the probability of shockwaves. Figure 6.11 compares speed con- tours when DSH is implemented at 25% market penetration on an exclusive DL. When compared to the base case, the DSH application was able to reduce the shockwaves by dispersing concentrations of slow-downs on the network, as shown in the spatio-temporal speed contours in Figure 6.11. Additionally, when CACC was also implemented, there were no more slow-downs in the network and there were significant improvements in environmental savings. For example, the DSH-only scenario increased fuel consumption by nearly 20% over the base case, whereas the combined DSH and CACC scenario reduced fuel consumption by more than 16% over the base case. 6.4 Impact of Demand To assess the impact of demand on the benefits of CAV applications when deployed on DLs, the project team tested the I-66 case study sites with three demand levels: • Normal demand, representing the typical p.m. peak traffic volume for the I-66 case study site; • Reduced demand, representing a 20% reduction in typical p.m. peak traffic volumes for the study site; and • Increased demand, representing a 20% increase in typical p.m. peak traffic volumes for the study site. The normal demand case was calibrated to field-observed volumes and speeds, whereas the cases with reduced and increased demand were hypothetical cases derived from the calibrated normal demand case. Figure 6.10. Performance of combination of CACC and DSH applications under 10% and 25% market penetration under exclusive lane access.

100 Dedicating Lanes for Priority or Exclusive Use by Connected and Automated Vehicles 6.4.1 CACC The impact of reduced and increased demand was assessed in relation to the CACC appli- cation at 10% market penetration. As was found in the previous analysis, to avoid dispa- rate lane utilization, sharing lanes with HOVs was warranted at 10% market penetration. Therefore, this analysis compared scenarios of reduced and increased demand with the normal demand scenario assessed at 10% market penetration when the DLs were shared with HOVs. Figure 6.12 compares the performance of CACC on a shared DL (with HOV) at 10% market penetration under normal and reduced demands. In Figure 6.12, the bar graphs represented by the primary axis show time-dependent differences in vehicle throughput from the baseline when CACC is implemented. The baseline throughputs are shown using line graphs represented by the secondary axis. Under normal demand, the CACC showed average throughput benefits of up to 1%. Under reduced demand, however, the negative impacts were offset by consistent marginal positive impacts. Similarly, Figure 6.13 shows a comparison of the performance of CACC on a shared DL at 10% market penetration under normal and increased demands. Under increased demand Figure 6.11. Speed contours for I-66 westbound direction when DSH is combined with CACC.

Evaluation Results 101 conditions, the improvement in throughput was even higher, with benefits between 3% and 4% at any given time. The percentage benefits for each scenario are demonstrated in relation to the base case at the respective demand levels, not at the normal demand. 6.4.2 DSH A similar analysis was conducted for the DSH application at 10% market penetration with shared DLs with HOV vehicles. Figure 6.14 shows this comparison of the performance of DSH on a shared DL (with HOV) at 10% market penetration under normal and reduced demands. The DSH application reduced the throughput under normal demand from 2% to 6%. This reduction occurred primarily because the downstream congestion reduced the Figure 6.12. Impact of reduced demand on CACC application at 10% market penetration. Figure 6.13. Impact of increased demand on CACC application at 10% market penetration.

102 Dedicating Lanes for Priority or Exclusive Use by Connected and Automated Vehicles speeds of equipped vehicles over the entire freeway to reduce shockwaves. However, when there was reduced demand, the DSH application had marginal impact, because there were no congested areas in the network. Figure 6.15 compares the performance of DSH on a shared DL at 10% market penetration under normal and increased demands. Under increased demand conditions, the DSH showed an increase in throughput of up to 1.5%. This result occurred primarily because the increased demand reduced the overall speed of unequipped vehicles as well. Conclusively, the DSH application provided better throughput when the demand was higher than typical. This outcome reflects the fact that the heterogeneity of travel speeds of different types of vehicles is reduced when there is higher congestion. Figure 6.14. Impact of reduced demand on DSH at 10% market penetration. Figure 6.15. Impact of increased demand on DSH at 10% market penetration.

Evaluation Results 103 6.5 Impact of Access Restrictions One of the research questions aimed to compare the impacts of dedicating lanes to CAV users when access restrictions to the DL exist. This section describes the impacts of continuous versus restricted access to DLs using the primary case study site, the I-66 corridor. All other sections of this report consider continuous (no physical barrier) access to the DLs. To model restricted lane access to DLs, the research team considered a scenario of exclusive DL access to CAVs at 25% market penetration. The results for this analysis, along with four performance measures analyzed, are shown in Figure 6.16. The two situations analyzed involved CAVs using CACC applications and CAVs using both CACC and DSH applications. To model restricted DL access, the I-66 model and the CACC API were modified. Modifica- tions to the I-66 model included lane-change restrictions for the left lane except for the sub-links that were placed before or after each interchange. The locations of these restrictions were repre- sentative of field conditions. Modifications to the CACC API included changes to the preferen- tial lane logic (described in the section on CACC in Chapter 5). These changes enabled a CAV to move to the DL only if its built-in static routes allow for being on the freeway for at least two interchanges. Additionally, the string dispersion logic was modified to look for exit points based on the built-in static routes. Figure 6.16. Impact of continuous and restricted lane access, assessed using 25% market penetration of CAVs.

104 Dedicating Lanes for Priority or Exclusive Use by Connected and Automated Vehicles Several performance measures were collected during the simulation, but the following perfor- mance measures were highlighted to develop conclusions about DL access settings: • Change in Average Network Travel Time. This measure showed the network-wide mobility impacts of CACC and the CACC+DSH combination as a comparison with the base case— HOV DLs with continuous access. When CAV DLs had restricted access, there was a reduc- tion in travel time savings in comparison to the base case with continuous access to CAV DLs. As noted earlier, this result occurred because the restricted access discouraged some users with shorter trips from using the DLs. Additionally, the users of the DL needed to exit early to help them navigate their predefined routes. • Change in CAV:Non-CAV Speed Ratio. This measure showed whether the CAVs achieved more mobility at the expense of non-CAVs. The ratio was calculated as the ratio of average spot speeds of CAVs (either in CACC-mode or not) and non-equipped vehicles. As shown in Figure 6.16, in the CACC-only case, the CAVs had over 46% more speed than non-CAVs when access was restricted and only 42% more speed when access was continuous. When CACC-equipped vehicles received DSH recommendations as well, the continuous access sce- nario had a higher CAV:Non-CAV speed ratio than it did under restricted access. This result occurred because of the presence of CAVs making shorter trips on GPLs. These shorter-trip CAVs received harmonized speed messages, which are generally lower than the speed limit. • Change in Travel Speeds of General Purpose Lanes. This measure indicated whether the GPLs had higher speeds or lower speeds compared to the base case. Under the CACC-only case, there was a (marginal) increase in the speeds of GPL vehicles when there was continu- ous access, owing to the lesser number of vehicles on GPLs due to the switch to DLs. Under restricted access, however, a reduction in travel speed occurred in the GPLs. This reduction in speed might have occurred because fewer CAVs use DLs owing to the entry/exit limitations causing a higher volume on GPLs. When CACC was implemented with DSH, the restricted access scenario demonstrated more benefits than continuous access. Restricting access also helped the DSH application by reducing cut-ins in front of slower DSH-equipped vehicles. • Increase in DL Throughput. This measure indicated whether the throughput of the DLs increased due to the CAV applications. In the CACC-only scenario, providing continuous access increased the throughput by 6%, whereas the restricted access scenario reduced the throughput by almost 8%. When CACC was paired with DSH, the throughput of DLs remained the same as the baseline in both the continuous access and restricted access scenarios. Given scope limitations, only 25% market penetration was used in these scenarios. Higher MPRs (such as 35% or 45%) would likely provide even higher benefits to GPLs. 6.6 Hypothetical Scenarios The analysis plan for this project also outlined research questions that require developing hypothetical scenarios for modeling and simulation. These scenarios were designed to answer the following two research questions: • What is the impact of an incident-related temporary lane closure? • What is the impact of a slow-moving vehicle on the DL? Although these scenarios are highly probable, they were not part of the original baseline model or the baseline data collected from the field. The primary case study site, I-66, was utilized for this analysis. 6.6.1 Impact of Incident-Related Temporary Lane Closure Traffic incidents on roadway facilities represent a common form of static bottleneck and typi- cally result in operational deterioration due to one or more lane closures. In this project, traffic

Evaluation Results 105 incidents were modeled in the simulation network to observe the effects of a static bottleneck on freeway operations on I-66. The location and duration of the modeled incident was devel- oped using historical data and empirical observations derived from the Transportation Technical Report: Interstate 66 – From US Route 15 in Prince William County to Interstate 495 in Fairfax County, published in February 2013 (Virginia DOT, Virginia DRPT, and FHWA 2013). Fig- ure 6.17 shows the location, direction, and peak-hour period for the commonly observed traffic incidents that occur on I-66. The incident location shown in Figure 6.17 was selected based on the distance from either end of the simulation model boundary limits to maximize the capture of the static bottleneck’s operational impacts. For modeling purposes, the incident location was not located within close proximity to the network upstream location, which would impede significant numbers of vehi- cles from entering the network, and it was not located at the downstream location, where a significant, unrelated bottleneck could occur due to facility capacity issues. The location chosen for modeling allowed the researchers to better isolate the effects of the incident itself. The peak hour was also limited to the p.m. peak in the westbound peak direction where the heavy con- gestion forms. Traffic and incident data within the simulation boundaries for October 2016 on I-66 was provided by the Virginia DOT. This incident data indicated the average incident in the westbound direction during the p.m. peak hour lasted for approximately 32 minutes. The mod- eled traffic incident was based on a combination of information from the technical report and I-66 incident data, as follows: • Location: I-66 westbound, West of VA-123 before the Jermantown Road overpass; • Lane Closure: Right-most lane (1 lane) was closed; • Duration: 32 minutes; and • Start Time: 4:30 p.m. This scenario was modeled with both CACC and DSH DLs at 10% market penetration. The DL was shared with HOVs in both cases, and the incident condition was compared to the situation without the temporary lane closure, as shown in Figure 6.18. In Figure 6.18, the percentages shows the differences in vehicle throughput between the CACC and DSH scenarios and the base case. In the base case “with incident” scenario, throughput was significantly less than in the base case “without incident” scenario. Significant differences can Source: NCHRP 20-102(08) project team; base map data © Google. Figure 6.17. Commonly occurring traffic incidents on I-66.

106 Dedicating Lanes for Priority or Exclusive Use by Connected and Automated Vehicles be seen in the benefits obtained from both CACC and DSH DLs. For the CACC application— represented in the figure as (a) and (b)—dedicating lanes to CACC-equipped vehicles in addition to HOVs, improved vehicle throughput by up to 1% with or without a tempo- rary lane closure. With regard to the DSH application—represented in the figure as (c) and (d)—the incident worsened the vehicle throughput by up to 10%, as opposed to just 6% for the case without incident. It should be mentioned that the lane closure was modeled on the right lane of the freeway, whereas the DL was modeled on the left lane of the freeway. As a result, the impacts represented in this section are primarily due to the additional strain placed on the open GPLs. 6.6.2 Impact of a Slow-Moving Vehicle on the DL When slower vehicles are on the DL, they are expected to cause noticeable degradation in the operational performance for vehicles traveling on the DL (i.e., “moving bottlenecks”). To assess the impact of CACC applications on CAV DLs responding to moving bottlenecks, the project Figure 6.18. Impacts of freeway incidents with and without CACC and DSH applications.

Evaluation Results 107 team modeled a scenario to replicate this situation. Such a situation could arise from many factors, such as a slow driver, a heavy vehicle that cannot perform at a faster speed, a maintenance vehicle performing maintenance, and so forth. In a scenario with CACC-equipped vehicles with exclusive access on the DLs, the CACC performance could be metered by a slower moving lead vehicle. Traffic data provided by the Virginia DOT from spot-speed detections indicated the speed distribution by lane type during the p.m. peak hour for October 2016, as shown in Table 6.2. The values represent average, minimum, and maximum speeds obtained from Virginia DOT detectors in the field and represent spot speeds at different segments of the corridor for every hour. Data from RITIS also indicated recorded speeds up to a maximum of 68 mph for the same period as the spot-speed data. Limited information was available on current slow-moving vehicles operating on the existing HOV lane during peak hours; therefore, the following moving bottleneck parameters were modeled: • Frequency: One slow vehicle every 30 minutes; • Desired Speed of Slow Vehicle: 45 mph (10 mph below posted speed limit); • Lane Utilization: Left-most lane (existing HOV lane); • Entry Location: I-66 westbound direction at I-495; and • Exit Location: I-66 westbound past US 29. Figure 6.19 shows a comparison between a moving bottleneck scenario and a scenario without a moving bottleneck. In both scenarios, the CACC application was modeled in CAVs at a 25% MPR. CAVs had exclusive access to the DL. General Purpose Lane HOV Lane Average 30.7 mph 38.0 mph Minimum 17.3 mph 18.7 mph Maximum 58.5 mph 62.2 mph Table 6.2. Speed distribution on I-66 by lane type for p.m. peak hour. Figure 6.19. Impact of moving bottlenecks on CACC dedicated lanes.

108 Dedicating Lanes for Priority or Exclusive Use by Connected and Automated Vehicles Figure 6.19 shows the difference in vehicle throughput with respect to the baseline scenario in two situations: (a) without the moving bottleneck and (b) with the moving bottleneck. The fig- ure also shows the base throughput (without CACC) under the same circumstances. As shown, the base throughput with a moving bottleneck is significantly lower than the base throughput without the bottleneck. The CACC application is able to improve the throughput without the bottleneck by up to 3%. When there are moving bottlenecks, however, the improvement in throughput due to the CACC application is up to 4.5%. The research team made two observa- tions about this difference: • The baseline measurement with the bottleneck had lower throughput than the baseline measurement without the bottleneck, and • The CACC application was able to maintain a streamlined traffic flow even with the slow-moving vehicle. Figure 6.19 should not be considered to confirm that the presence of a moving bottleneck is better than not having a moving bottleneck. Rather, it appears that the percentage savings are better because the moving bottleneck has caused a significant reduction in the baseline through- put. In other words, it appears the CACC application mitigates the reduction in throughput. 6.7 Impact on US-101 Corridor The project team also utilized a secondary case study site to assess the impact of the CACC application. Owing to scope limitation, the US-101 study site was used only for a subset of sce- narios that were analyzed and modeled for the I-66 corridor. Specifically, the US-101 case study site was used for two types of sensitivity analysis—market penetration and demand conditions. This section describes the results for the US-101 corridor scenarios. 6.7.1 Sensitivity Toward Market Penetration The US-101 corridor case study site was used to study the impact of CACC market penetra- tion on overall improvement in system efficiency. Four scenarios were analyzed and compared to the base case, which represented a typical p.m. traffic peak between 3:00 p.m. and 7:00 p.m. The four scenarios were: • 10% CACC + HOV. In this scenario, CACC has 10% market penetration in the system, and the CACC-equipped vehicles share the DLs with HOV vehicles; • 25% CACC. In this case, CACC has 25% market penetration in the system, and the CACC- equipped vehicles have exclusive access to the DLs; • 50% CACC/No DLs. In this case, CACC has 50% market penetration in the system, but no lanes are dedicated to the CACC-equipped vehicles; • 100% CACC/No DLs. In this case, CACC has 100% market penetration in the system, but no lanes are dedicated to the CACC-equipped vehicles. Two performance measures were used to demonstrate the impact of market penetration: network-wide reduction in travel time and increase in overall throughput. The observed perfor- mance is shown in Figure 6.20. As shown in Figure 6.20, both mobility performance measures increased as market penetra- tion went up. For example, the travel time reduction increased from a marginal 0.5% at 25% market penetration to more than 6% at 100% market penetration. The increase in throughput grew from almost 0% at 10% market penetration to 10% at 100% market penetration. It should be noted that the model used by the project team had a fixed time-varying demand through- out the analysis time period. In other words, even though the CACC application increased the

Evaluation Results 109 theoretical throughput of the lanes, the actual number of vehicles released into the simulation was constrained by the “present-day” demand. Analysis at a macroscopic scale is required to understand the impact on the demands on transportation systems with the advent of CAV technology. 6.7.2 Sensitivity to Demand To study the impact of varying demand on the performance of CACC, the project team also modeled a scenario with higher-than-typical peak demand. This scenario was developed using a fully calibrated model that represented typical p.m. peak demand by increasing the volume in increments of 20%. As in the previous section, three market penetration scenarios were assessed: • 10% market penetration of CACC-equipped vehicles that shared the DLs with HOVs, • 25% market penetration of CACC-equipped vehicles that had exclusive access to the DLs, and • 50% market penetration of CACC-equipped vehicles without any DLs. The results, in terms of reductions in travel time and increases in throughput, are shown in Figure 6.21. As shown in Figure 6.21, for the typical p.m. peak demand, both performance measures improved as a function of market penetration of CACC. For example, network-wide benefits Reduction in Travel Time Increase in Throughput Figure 6.20. Impact of CACC on US-101 corridor; four scenarios. Figure 6.21. Impact of demand on CACC application.

110 Dedicating Lanes for Priority or Exclusive Use by Connected and Automated Vehicles were seen in terms of reductions in travel time as well as increases in throughput when CACC market penetration was greater than 25%. With higher demand, however, the research team observed much greater benefits even at a lower market penetration. For example, at 10% market penetration, sharing of the DL between HOVs and CACC-equipped vehicles reduced the travel time of all vehicles by up to 1.3%. 6.7.3 Comparison of US-101 Corridor Results to I-66 Corridor Results The project team compared the results obtained from the US-101 case study network to those obtained from the I-66 case study network. The two corridors differ significantly in terms of traffic patterns, roadway geometry, entry-exit configurations, and vehicle types. The study team compared the percentage impacts of CACC on the two networks using the scenario of 25% market penetration and exclusive DL use. The results of the comparison appear in Figure 6.22. Figure 6.22 shows improvement in vehicle throughput as a bar graph (primary axis) and base- line throughput as a line graph (secondary axis). The benefits from US-101 were smaller than those demonstrated in the I-66 network; however, the baseline throughput was similar in both networks. This comparison indicates that the differences in roadway geometry had a significant impact on the results obtained for the CACC application, as did the driving parameters in the two networks. 6.8 Analysis of VTTS As indicated in Chapter 2, the team developed a simplified VTTS analysis to compare the ben- efits to users of DLs versus users of GPLs. VTTS is a complex concept that undertakes economic analysis on reducing travel times. The researchers’ simplified approach was developed to at least inform general guidance on whether dedicating lanes to CAV users produces economic benefits to either category of users. For this analysis, the study team used the simplified VTTS approach to quantify travel time savings for DL users over GPL users over a 1-year period using average observed speeds for DLs and GPLs as computation drivers. The term computation driver represents the parameter that Figure 6.22. Comparison of impact of CACC on US-101 network compared to I-66 network.

Evaluation Results 111 drives the computation to compare the economic benefits of one category of users over the other. The following assumptions were used in this analysis: • Computation Driver: Average observed travel speeds of DL (vDL) and GPLs (vGPL); • Average Trip Distance on Freeway: 12 miles (saverage); • Number of Commute-Trips per Year: 500 (250 working days with a.m. and p.m. commute); • VTTS for Personal Travel: $12.30/hour (VTTSpersonal); and • VTTS for Business Travel. $24.10/hour (VTTSbusiness). Using these assumptions, the research team computed the annual savings per DL user as follows: = ∆ × × +500 2 ,Annual Savings per DL User t VTTS VTTS trip personal business where Δttrip = the difference in travel time between a DL user and a GPL user, and Δttrip is computed as −   1 1 .s v v average DL GPL Based on this analysis, the research team computed the annual savings per DL user under dif- fering market penetrations of the CACC application. Although CACC DL users might also receive additional savings from fuel and maintenance, these benefits were not considered in this evaluation. The DSH application also was not part of this evaluation. DSH produced marginal travel time savings and therefore was not expected to produce any economic benefit to users apart from a reduction in surrogate factors such as prevention of secondary crashes. Figure 6.23 compares the annual VTTS savings per DL user based on the above model and assumptions for a variety of market penetrations. The savings shown in the figure are for all DL users, not for CAV users alone. In other words, the savings shown include the scenarios under which HOVs also were allowed on the DL. As shown in Figure 6.23, the greatest benefit for DL users was shown at 10% CACC with exclusive DL access. Under this scenario, the DLs were undersaturated and the GPLs were over- saturated; therefore, the benefit to DL users in terms of travel time was very high when compared to the GPL users. The 25% CACC scenario formed an ideal case of equitable lane-volume dis- tribution (wherein both GPLs and DLs were saturated at the same level) but CACC streamlined the flow of traffic through the DLs and hence reduced their travel time. Figure 6.23. Annual VTTS savings estimated per DL user (CAV user and/or HOV user) under CACC DL scenarios.