Project Name

Advanced Freeway Merge Assistance: Harnessing the Potential of Connected Vehicles, Exploratory Advanced Research Program (EARP)

Research Team

Brian Smith

Hyungjun Park

Malathi Veeraraghavan

Byungkyu (Brian) Park


Federal Highway Administration

Project Dates

November 2009 - July 2012

Project Description

Freeway merge conflicts contribute significantly to freeway congestion. With the new capabilities that will be available in a Connected Vehicle environment, which enables vehicle-to-vehicle and vehicle-to-infrastructure communications, new approaches to management of freeway merges are possible. Given this background, the University of Virginia Center for Transportation Studies (UVA CTS) has conducted the Advanced Freeway Merge Assistance project of FHWA’s Exploratory Advanced Research Program (EARP) in order to develop an advanced freeway merge assistance system for the Connected Vehicle environment.


Three critical concepts needed for the successful implementation of freeway merge assistance were identified in the EARP solicitation document as follows:

  • The creation of individual gaps in the mainline freeway lane
  • The communication of the availability of the gap to an individual driver waiting to merge
  • The control over the merging action through real-time communication and control actions


Based on these critical concepts, five tasks were prepared as presented below. A summary of the accomplishments obtained from each of the tasks are provided here.


Task 1: Connected Vehicle Testbed

In this task, a Connected Vehicle traffic/communications simulation environment was developed, that (a) replicates precise vehicular movements, (b) incorporates Connected Vehicle wireless communications based on the WAVE/DSRC standards, and (c) finally simulates real Connected Vehicle message sets defined in the SAE J2735 standard.

A case study of a prototype lane changing advisory algorithm conducted on a freeway network in Northern Virginia has revealed that communication delays are not likely to be a significant factor but that the probability of successful communications was found to be a critical factor that can impact the evaluation results significantly. For example, for a lane changing advisory algorithm, the probability of successful transmission of a series of required messages was only 50% when there are less than 120 vehicles within a communications radius of 50 meters, implying a significant degradation in the algorithm performance. The results of this case study demonstrates the feasibility of integrating traffic and communications simulation models, and also illustrates the need to consider both components in Connected Vehicle evaluation.


Task 2.1: Lane-Level Variable Speed Limit

A lane-level variable speed limit algorithm was developed in this task. The proposed algorithm implements a lower speed limit on the rightmost lane of a freeway mainline based on the densities on the left and the rightmost lanes to encourage mainline vehicles to move to the left, thus creating a better merging situation.

Evaluations have shown that the proposed algorithm can improve network-wide performance only marginally – up to 6.5% higher travel speeds, 1.1% more vehicle miles traveled and 5.6% less vehicle hours traveled – in the best case. In addition, from the individual lane level analysis, the maximum improvement (9.1% increase in average speed) was obtained in the rightmost lane of the freeway mainline within a merge area. Therefore, it can be concluded that lane-level speed control has “potential” to create better merging situations and thereby improve freeway operations.


Task 2.2: Lane Changing Advisory

In this task, an enhanced Connected Vehicle enabled lane changing advisory algorithm was developed. This algorithm first calculates anticipated lead/lag gap sizes utilizing the equations of motion prepared for two types of vehicles (a car and a truck) and for three different vehicle dynamics (accelerating, maintaining current speeds, and decelerating). It then provides lane changing advisories to freeway mainline vehicles (selected based on the calculated variable gap sizes) to create more space within the ramp merging area.

Evaluation result has shown that the proposed algorithm has potential to address freeway merge conflicts. The maximum operational improvement observed was 6.4% higher average speed in the freeway mainline within a merge area. In addition, up to 5.2% reduction in emissions was achieved. However, further analysis conducted in terms of the sensitivity of the algorithm performance to driver compliance rates suggested that a very high compliance rate (90% or higher) is necessary for the proposed algorithm to work as intended.


Task 3: Gap Responsive On-Ramp Signal

Three gap responsive on-ramp signal algorithms were developed in this task. These algorithms first estimate the target time step, i.e. the time when a ramp vehicle gets to a merging point, of the current signal; determine a gap availability in the target time step by predicting locations of mainline vehicles; and finally set the color of the on-ramp signal based on the gap availability.

Based on the evaluation results, it was concluded that the gap responsive type on-ramp signal algorithms are not effective in improving mobility due to the difficulties in estimating target time steps and predicting main line vehicle locations. However, the gap responsive on-ramp signal algorithm could generate a meaningful benefit for the environmental measures, an 8.4% reduction in fuel consumption and a 15.6% reduction in CO emissions. In conclusion, it would be reasonable to say that the gap responsive on-ramp signal algorithm has potential in improving the freeway mobility marginally but can improve environmental aspects significantly.


Task 4: Merging Control

In this task, an innovative freeway merging control algorithm was designed, implemented, and calibrated. The main concept behind this algorithm is to directly control a leading mainline vehicle, a lagging mainline vehicle, and a ramp merging vehicle cooperatively to achieve smoother merging, by fully utilizing the detailed data and control capabilities enabled by the Connected Vehicle technology.

Evaluations demonstrate that the proposed application has significant potential in improving freeway merging operations, under certain conditions of congestion level, compliance rate, and the Connected Vehicle equipped vehicle market penetration rate. To be more specific, in an ideal  situation, the proposed freeway merging control algorithm will generate significant benefits in network performance as follows: 1) traffic efficiency: 6.3% increase in vehicle miles travelled (VMT),  42.2% increase in network average speed, 25.9% reduction in total travel time (VHT), and 54.6% reduction in total delay time; 2) traffic safety: 8% reduction in crossing conflicts, 70.2% reduction of rear-end conflicts, 28.1% of lane changing conflicts, and 44.5% reduction in total number of conflicts. It is therefore reasonable to say that the merging control algorithm can improve freeway operations significantly.


Task 5: System Integration and Evaluation

The final task of this project was conducted in two steps: 1) all the algorithms developed – lane-level variable speed limit, lane changing advisory, gap responsive on-ramp signal, and merge control –  were first integrated with the Connected Vehicle traffic/communication evaluation environment, and 2) all the individual algorithms were then evaluated with Connected Vehicle communications effects.

The results are summarized as following:

  • The lane-level variable speed limit algorithm is not effective when real communications of the Connected Vehicle message sets are considered.
  • The lane changing advisory algorithm is not able to improve the mobility when Connected Vehicle communications are considered. The best case could only show the potential in improving link-wise average speeds without statistical significance.
  • The gap responsive on-ramp signal algorithm does not have the capability of improving the mobility of the freeways with communication drops and latency.
  • The merge control algorithm could generate statistically significant benefits even with the Connected Vehicle simulation environment: 2.4% increase in vehicle miles traveled, 23.6% increase in average speeds, 11.5% decrease in total travel time, and 17.9% decrease in total delay time.
  • In conclusion, with the Connected Vehicle communications in effect, the merging control algorithm was the only one that can still generate statistically meaningful improvements.

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