Introduction of Cooperative Vehicle-To-Infrastructure Systems to Improve Speed Harmonization

Executive Summary

The U.S. Department of Transportation's Federal Highway Administration has initiated a research program on cooperative vehicle-to-infrastructure (V2I) highway systems with the goal of increasing overall system performance and sustainability, including safety, mobility, and environmental impacts. Cooperative vehicle-highway systems coordinate vehicle communications and remote traffic microwave sensors (RTMSs) in pursuit of these goals. One strategy of cooperative vehicle-highway systems is speed harmonization, which dynamically adjusts vehicle speed recommendations in order to reduce speed differentials. Speed harmonization can be applied near areas of congestion, accidents, or special events to optimize mobility and safety. Speed harmonization has been implemented in a few locations in the United States with some success, but the current approach faces significant challenges. As presently implemented, speed harmonization is conducted with the use of variable speed limit signs or dynamic message signs. This method of implementation is susceptible to unpredictable and uncoordinated driver response. Moreover, these signs are costly for State and local agencies to deploy, operate, and maintain.

Despite these challenges, simulation has shown that speed harmonization does not require perfect driver compliance to significantly improving traffic flow and performance.^(1,2) In this project, researchers performed a preliminary experiment of V2I-based speed harmonization in which speed guidance was communicated directly to vehicles. This experiment involved a set of micro-simulation experiments and a limited number of prototype field runs.

Site Selection

Speed harmonization is believed to produce significant benefits at sites where excessive vehicle speed oscillations cause premature formation of congestion and bottlenecks. The section of I-66 inside the beltway (I-495) approaching Washington, DC, is a congested roadway with one of the least dependable travel times in the United States.⁽³⁾ Daily recurring congestion at the merge of VA-267 into I-66 (and the subsequent lane drops) leads to a "stop-and-go" formation. At this site, shown in figure 1, it was hypothesized that speed harmonization could have a positive impact.

©2016 Google^®

Figure 1. Map. Geographic scope of the study area and typical traffic situation in afternoon peak hours.⁽⁴⁾

In order to understand the traffic dynamics on this section of freeway, a number of field runs were used to identify typical speed trajectories during weekday peak periods. These field runs were performed by probe vehicles equipped with Global Positioning System receivers, cell phones, and computers. The computers transmitted vehicle trajectories in real time to servers at the Saxton Transportation Operations Laboratory in McLean, VA. Trajectories were transmitted before and during the recurring congested period. Figure 2 illustrates actual probe speed trajectories shown in blue plus a computed average trajectory shown in red. The average trajectory shows a significantly trended periodic component. Large features of the average trajectory can be described by a sinusoid, as shown in figure 3. The oscillatory trend shown in figure 3 will increase fuel consumption and may impact mobility and safety. Given this recurring structure in the speed profiles along I-66, the corridor was deemed a suitable candidate for the experiment on speed harmonization using connected and automated vehicles (CAVs).

1 mi/h = 1.61 km/h

Figure 2. Graph. I-66 probe vehicle actual and average speed trajectories.

1 mi/h = 1.61 km/h

Figure 3. Graph. I-66 probe vehicle actual and average speed trajectories with harmonic model.

Research Results

Due to resource constraints, the field experiments could only deploy a maximum of three CAVs. Simulation results across three software platforms (VISSIM^®, INTEGRATION^©, and Aimsun^®) showed that the introduction of three CAVs, with a goal of harmonizing overall speeds, did not produce macroscopic traffic benefits.^(5–7) When analyzing higher CAV penetration rates, the simulation experiments produced mixed results.

On the Aimsun^® platform, all penetration rates above 10 percent produced corridor-wide travel time reductions between 8 and 10 percent. The researchers concluded that this was due to congested conditions that minimized lane changing (i.e., if 10 percent of vehicles reduced
their speeds, most other vehicles were impacted). Similar results were observed on the INTEGRATION^© platform, where all penetration rates above 10 percent produced corridor-wide delay reductions between 7 and 11 percent. On the VISSIM^® platform, a 1,000-ft (300-m) freeway segment believed to be most impacted by speed harmonization saw 32, 39, and 42 percent travel time reductions under penetration rates of 10, 25, and 50 percent, respectively. However, corridor-wide travel time reductions were only 1, 2, and 3 percent, respectively.

Although the simulation experiments produced mixed results, the results were positive enough to warrant follow-up field experiments. These experiments demonstrated that with modifications to the manufacturer-supplied adaptive cruise control (ACC), CAVs can successfully implement V2I-based speed harmonization, at least from a mechanical standpoint. From an operational standpoint, the field experiments were constrained by the availability of only three CAVs. These vehicles were shown to significantly reduce speed oscillations in their vicinity but did not have a significant impact on aggregate average speeds or travel times, which is consistent with the simulation outcomes.

Next Steps

Future field experiments should thus include a larger number of CAVs. Future algorithm development should optimize vehicle speeds to achieve maximum safety benefits. If a bottleneck is not yet formed, slowing the right proportion of vehicles could prevent or delay the onset of bottleneck formation. If a bottleneck is already formed, slowing all vehicles by the right amount could mitigate bottleneck severity. Other factors subject to optimization include CAV penetration rates, speed reduction magnitudes, and lane-specific congestion levels. The remainder of this report summarizes the technical details from phase 1 of the V2I-based speed harmonization research.