This paper proposes a two-stage multi-lane unsignalized intersection cooperative control strategy based on mixed platoons (MICS-PF) in the mixed traffic environment of connected automated vehicles (CAVs) and connected human-driven vehicles (CHVs). In the first stage, we propose a 0-1 integer programming model based on vehicles’ intersection movements and potential car-following costs to organize vehicles into mixed platoons led by CAVs. Meanwhile, we design a rule-based algorithm to coordinate the relative distribution of platoons. Then, the lane-changing and car-following models are integrated to ensure that the platoons organization plan can be completed in the limited functional zone. In the second stage, we design an improved minimum covering clique algorithm to generate a conflict-free passing order of platoons by considering the platoon-level conflict relation and the potential yield delay. The proposed strategy couples the process of organizing vehicles into mixed platoons to pass through the intersection. The optimal platoon organization enables CHVs to receive accurate guidance when entering the intersection and avoids the disorderly lane change at the upstream road sections. The mixed platoon control also allows vehicles to avoid stop-and-go, improve the capacity of approach roads, and prevent the intersection capacity from being restricted by the inefficient traffic at the approach road. The conflict-free coordination method of mixed platoons optimizes the throughput of intersections and broadens the usage conditions of unsignalized intersections. A series of simulation experiments verify the effectiveness of the strategy, and the applicable boundary conditions are obtained.

The emergence of connected autonomous vehicles (CAVs) is not only improving the efficiency of transportation, but also providing new opportunities for the sustainable development of transportation. Taking advantage of the energy consumption of CAVs to promote the sustainable development of transportation has attracted extensive public attention in recent years. This paper develops a mathematical approach to investigating the problem of the optimal implementation of dedicated CAV lanes while simultaneously considering economic and environmental sustainability. Specifically, the problem is described as a multi-objective bi-level programming model, in which the upper level is to minimize the system-level costs including travel time costs, CAV lane construction cost, and emission cost, whereas the lower level characterizes the multi-class network equilibrium with a heterogeneous traffic stream consisting of both human-driven vehicle (HVs) and CAVs. To address the multi-objective dedicated CAV lane implement problem, we propose an integrated solution framework that integrates a non-dominated sorting genetic algorithm II (NSGA-II) algorithm, diagonalized algorithm, and Frank–Wolfe algorithm. The NSGA-II was adopted to solve the upper-level model, i.e., hunting for the optimal CAV lanes implementation schemes. The diagonalized Frank–Wolfe (DFW) algorithm is used to cope with multi-class network equilibrium. Finally, numerical experiments were conducted to demonstrate the effectiveness of the proposed model and solution method. The experimental results show that the total travel time cost, total emission cost, and total energy consumption were decreased by about 12.03%, 10.42%, and 9.4%, respectively, in the Nguyen–Dupuis network as a result of implementing the dedicated CAV lanes.

Compared to conventional private vehicles (CPVs), shared autonomous vehicles (SAVs) provide users the potential for the reduced value of time (VoT), improved mobility experience, and less traffic congestion. In the presence of the SAV system, numerous studies have mainly concentrated on the strategic planning and operational decision problem separately while ignoring the complicated interaction between them and the distinct features of autonomous vehicles. It is imperative to determine the relocation and pricing strategies at the operational level. In this study, in terms of the pricing strategy, we formalize a logit model to capture the mode choice behavior in a multimodal network, where the reduced VoT is considered simultaneously. A time-space network is employed to capture the daily operation problem based on the elastic demand. The minimum customer service rate is regarded as a constraint to ensure the system’s reliability. Moreover, a mixed-integer nonlinear programming (MINLP) model is formulated to jointly determine the number of stations and parking spaces, fleet size, relocation, and pricing strategies to maximize the total profit. Then, we integrate the Particle Swarm Optimization (PSO) algorithm with the optimization solver Gurobi to address the complex problem. Numerical experiments and comparative analyses are conducted to demonstrate the feasibility and efficiency of the proposed model.