With the development of automation and connectivity technology, there will be more connected and autonomous vehicles (CAVs) entering the road network. Before all of the existing human-driven vehicles (HVs) are replaced by CAVs, there will be a long transition period where CAVs and HVs are mixed on the roadway.
Equipped with vehicle-to-vehicle communication capability, CAVs are expected to improve the road capacity due to smaller reaction times and headways. However, in mixed traffic, the movement of human-driven vehicles are uncertain and unmanageable, which will severely hinder the communication between CAVs. Managing the operation of CAVs in mixed traffic is a struggle.
To address this problem, the study proposed a lane-changing algorithm to guide CAV platoons to bypass the slower HVs. The simulation results verified that the proposed algorithm speeds up not only the CAVs but also the HVs, the total travel time decreased, and the highway outflow was improved. The study also found that as CAV penetration rates increased, the urban street capacity may increase or decrease depending upon the reaction time settings. This is important for traffic operators and vehicle industries to be aware as CAVs become more common.
The advent of CAVs also has the potential to increase vehicle travel demand by improving road capacity and reducing travel and parking costs. However, the extent to which travel demand will increase is still uncertain. This uncertainty makes it challenging to determine how to modify existing road networks to improve travel flow/throughput. To circumvent this challenge, the study used a macroscopic fundamental diagram (MFD) method to identify design solutions that would improve the transportation network.
Three products were developed as a result of this project:
1. Lane-Changing Algorithm for CAVs – The proposed algorithm should be helpful to control the autonomous and connected vehicles in a mixed traffic environment. Potential users could be vehicle industry and state Departments of Transportation. A research paper, “A Cooperative Bypassing Algorithm for Connected and Autonomous Vehicles in the Mixed Traffic,” is being published by IET Intelligent Transport Systems (https://doi.org/10.1049/iet-its.2019.0707).
2. Methodology to Identify Design Solutions that Improve the Transportation Network – The proposed methodology should be useful for city planners to determine which new roads can be built to improve road network capacity. A research paper describing the methodology, “Macroscopic Fundamental Diagram Based Discrete Transportation Network Design” has been published in the Journal of Advanced Transportation (https://doi.org/10.1155/2020/4951953).
3. Methodology for Determining CAV Reaction Times and Penetration Rates – The methodology is useful for the vehicle industry to determine the reaction time of CAVs, and for the Department of Transportation to determine the penetration rate of autonomous vehicles. A paper describing the methodology, “An Analytical Approximation for the Corridor Macroscopic Fundamental Diagram of Mixed Human and Connected and Autonomous Traffic,” is being developed.
This STRIDE Project O2 “Macroscopic Fundamental Diagram Approach to Traffic Flow with Autonomous/Connected Vehicles” was completed by Dr. Robert Whalin, Jackson State University, Dr. Feng Wang, formerly Jackson State University, and Guojing Hu, Jackson State University.
Learn more about the project and read the final report at https://stride.ce.ufl.edu/project-o2/