The main objective of this paper was to develop a prototype CAV enabled LCS application that performs an immediate and more cooperative merge on a freeway section by providing direct control inputs to CAVs instead of displaying signs on gantries. A 1.5 miles section of freeway network from I-66 in Northern Virginia was selected as a case study site and modeled in a VISSIM microscopic simulation environment. As such, three simulation models were developed. A baseline simulation model without LCS, a real-world LCS and CAV enabled LCS. The CAV enabled LCS was finally evaluated and a sensitivity analysis was conducted based on different time gaps/headways.
Three days (Jan 3-5, 2018) of field data were collected from the detectors installed at the two positions of entrance and exit of the traffic. The network was calibrated such that volume and speeds were within 10 percent of field observed values.
Real-world data were collected from a real-world LCS is being operated on I-66 in Northern Virginia that consists of four states (red cross indicating closed lane, green arrow indicating lane is open for travel, left or right merge and a double merge sign). Transportation Management Center (TMC) logs over a 2.5-year period (Oct 2015 to Feb 2018) were used to identify congestion control strategies implemented along a 1.5-mile segment of the I-66 corridor. Researchers then developed a simple lane control strategy that could leverage V2I connectivity and automation. In general, whenever a TMC identified an incident or lane closure, it would send out merge messages to connected vehicles in the area via a roadside unit (RSU) to facilitate immediate merge actions for vehicles in specific lanes.
Then External Driver Behavior Model in the VISSIM microsimulation software was used to model the impacts of the CAV application on freeway throughput using data generated from 15 random simulations. A sensitivity analysis was also conducted based on 1 second, 1.5 second and 2 second CAV headways.
The proposed application was able to provide more efficient and cooperative merging when needed. The CAV enabled LCS application consistently outperformed the traditional LCS application for all the three strategies observed from the real-world. For 1 second, 1.5 second and 2 second headways, vehicular throughput increased by 18.4 percent, 9.6 percent and 12.8 percent as compared to traditional LCS application. Furthermore, the CAV application reduced volatility in acceleration and deceleration regimes by an average of 25.6 percent and 49.6 percent for the three strategies. Thus, pointing towards lower jerks and smoother flow of traffic.