Citation

Sun Z, Huang T, Zhang P. Transp. Res. C Emerg. Technol. 2020; 120: e102764.

Copyright

(Copyright © 2020, Elsevier Publishing)

DOI

10.1016/j.trc.2020.102764

PMID

unavailable

Abstract

The rapid conceptual development and commercialization of connected automated vehicle (CAV) has led to the problem of mixed traffic, i.e., traffic mixed with CAVs and conventional human-operated vehicles (HVs). The paper studies cooperative decision-making for mixed traffic (CDMMT). Using discrete optimization, a CDMMT mechanism is developed to facilitate ramp merging, and to properly capture the cooperative and non-cooperative behaviors in mixed traffic. The CDMMT mechanism can be described as a bi-level optimization program in which state-constrained optimal control-based trajectory design problems are imbedded in a sequencing problem. A bi-level dynamic programming-based solution approach is developed to efficiently solve the problem. The proposed modeling mechanism and solution approach are generic to deterministic decisions and can guarantee system-efficient solutions. A micro-simulation environment is built for model validation and analysis of mixed traffic. The results show that compared to the scenario with 100% HVs, ramp-merging can be smoother in mixed traffic environment. At high CAV penetration, the section throughput increases about 18%. With the proposed CDMMT mechanism, traffic throughput can be further increased by 10-15%. The proposed methods form the basis of traffic analysis and cooperative control at ramp-merging sections under mixed traffic environment.

Language: en

Keywords

Bi-level optimization; Connected Automated Vehicles (CAVs); Cooperative decision-making for mixed traffic (CDMMT); Dynamic programming; Ramp merging