Multiobjective Environmentally Sustainable Optimal Design of Dedicated Connected Autonomous Vehicle Lanes

Yu Lin, Hongfei Jia, Bo Zou, Hongzhi Miao, Ruiyi Wu, Jingjing Tian and Guanfeng Wang
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Yu Lin: Transportation College, Jilin University, Changchun 130022, China
Hongfei Jia: Transportation College, Jilin University, Changchun 130022, China
Bo Zou: Department of Civil, Materials, and Environmental Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA
Hongzhi Miao: College of Transportation Engineering, Dalian Maritime University, Dalian 116026, China
Ruiyi Wu: Transportation College, Jilin University, Changchun 130022, China
Jingjing Tian: Transportation College, Jilin University, Changchun 130022, China
Guanfeng Wang: Transportation College, Jilin University, Changchun 130022, China

Sustainability, 2021, vol. 13, issue 6, 1-21

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.

Keywords: sustainable transportation; connected autonomous vehicle lane; heterogeneous traffic streams; multi-class network equilibrium (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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