Introduction and Problem Statement
Freeway capacity and throughput can be significantly improved via CACC vehicle string operations.
Research Team
The principal investigator for this project was Dr. Xiao-Yun Lu of California PATH. Other team members from PATH included Hao Liu, Lin Xiao, Xingan (David) Kan, Steve Shladover, Meng Wang, Wouter Schakel, and Bart van Arem.
Partners
This joint project was sponsored by the Federal Highway Administration – Exploratory Advanced Research Program (FHWA EAR). It is developed and managed by the University of California at Berkeley Institute for Transportation Studies (ITS), Partners for Advanced Transportation Technology (PATH) and Faculty of Civil Engineering and Geosciences, Department of Transport & Planning, Delft University of Technology. The project was funded by FHWA EAR under Contract Number DTFH61-13-H-00013.
Objective
This research aims to provide authoritative predictions regarding impacts of CACC on traffic flow and quantitative estimations of the influences of CACC operation strategies that might create the capacity and throughput improvement in the freeway traffic stream.
Status, Conclusions and Recommendations
The PATH and Delft team have independently developed micro simulation platforms that represent the behaviors of CACC vehicles and their interactions with human drivers. The models have been calibrated using archived data from a complicated 13-mile long section of the northbound SR-99 freeway near Sacramento, California for an 8-hour period in which the traffic fluctuated between free-flow and congested conditions. Calibration results show extremely good agreement between field data and model predictions.
The models have been cross-validated and produced similar macroscopic traffic performance. With the simulation platforms, we have explored the effects of CACC under various market penetrations and the impacts of a CACC managed lane (ML) strategy, a vehicle awareness device (VAD) strategy and discretionary lane change (DLC) restrictions on the traffic flow dynamics of a simple four-lane freeway section and the 13-mile freeway corridor. Results from both models reveal that the freeway capacity increases quadratically as the CACC market penetration increases, with a maximum value of 3080 veh/hr/lane at 100% market penetration. The disturbance from the on-ramp traffic causes the merge bottleneck and can reduce the freeway capacity by up to 13% but the bottleneck capacity still increases on a quadratic trend as CACC market penetration becomes larger. The results also indicate that the ML and VAD strategy can substantially increase the pipeline capacity of the freeway when the CACC market penetration is 60% or less. On the other hand, the DLC restriction strategy is most helpful when the penetration is 80% or higher.
The ML and VAD strategy can lead to significant improvement of the traffic operation at freeway on-ramp bottlenecks under various CACC market penetration cases. Those strategies are also capable of enhancing the overall operation of the freeway corridor, even for CACC market penetrations as low as 20%.
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