Abstract
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.
Highlights
Emissions from transportation systems are considered to be an important component of environmental pollutants
Compared with scenario 1 and scenario 2, scenario 3 could assume that the capacity of the regular lane is 2000 veh/h, and the dedicated connected and autonomous vehicles (CAVs) lane is reduce the total travel time costs by 8.81% and 8.81%, respectively
Practical ming We model with multi-class network equilibrium constraints for the proposed significance and environmental considerations in this work
Summary
Emissions from transportation systems are considered to be an important component of environmental pollutants. Some scholars have proposed implementing dedicated CAV lanes in existing road networks to improve system efficiency by offering a better driving environment for CAVs, which would reduce system travel time [19]. Sustainable dedicated CAV lane design belongs to the class of transportation network design problems Such problems are inherently challenging to solve because: (1) the equilibrium constraints make the domain of feasible solutions nonconvex; (2) constructing the Pareto frontier for different objectives is a non-deterministic polynomial (NP) hard problem. (1) The proposed sustainable dedicated CAV lanes design problem is a novel research issue since it considers economic and vehicle emissions to capture the optimal CAV lanes implement scheme. (2) A multi-objective bi-level programming model with multi-class network equilibrium constraints was developed to draw sustainable dedicated CAV lanes design problems.
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