Exploring the impact of connected and autonomous vehicles on freeway capacity using a revised Intelligent Driver Model

ABSTRACT

Connected and autonomous vehicle (CAV) technologies are expected to change driving/vehicle behavior on freeways. This study investigates the impact of CAVs on freeway capacity using a microsimulation tool. A four-lane basic freeway segment is selected as the case study through the Caltrans Performance Measurement System (PeMS). To obtain valid results, various driving behavior parameters are calibrated to the real traffic conditions for human-driven vehicles. In particular, the calibration is conducted using genetic algorithm. A revised Intelligent Driver Model (IDM) is developed and used as the car-following model for CAVs. The simulation is conducted on the basic freeway segment under different penetration rates of CAVs and different freeway speed limits. The results show that with an increase in the market penetration rate, freeway capacity increases, and will increase significantly as the speed limit increases.

KEYWORDS:

• Connected and autonomous vehicles
• calibration
• genetic algorithm
• microsimulation
• Intelligent Driver Model

Disclosure statement

No potential conflict of interest was reported by the author(s).

ORCID

Pengfei Liu http://orcid.org/0000-0001-7217-151X

Wei (David) Fan http://orcid.org/0000-0001-9815-710X

Additional information

Funding

The authors want to express their deepest gratitude to the financial support by the US Department of Transportation, University Transportation Center through the Center for Advanced Multimodal Mobility Solutions and Education (CAMMSE) at The University of North Carolina at Charlotte [grant number 69A3551747133].