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

• Published in: Transportation planning and technology 2020-04, Vol.43 (3), p.279-292
• Main Authors: Liu, Pengfei, Fan, Wei (David)
• Format: Article
• Language: English
• Subjects: calibration; Car following; Computer simulation; Connected and autonomous vehicles; Drivers; Driving; Driving conditions; genetic algorithm; Genetic algorithms; Highways; Impact strength; Intelligent Driver Model; Market penetration; microsimulation; Penetration; Performance measurement; Speed limits; Traffic; Vehicles
• ISSN: 0308-1060; 1029-0354
• DOI: 10.1080/03081060.2020.1735746

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.