Citation

Singh H, Kavianipour M, Ghamami M, Zockaie A. Transp. Res. D Trans. Environ. 2023; 115: e103561.

Copyright

(Copyright © 2023, Elsevier Publishing)

DOI

10.1016/j.trd.2022.103561

PMID

unavailable

Abstract

Autonomous vehicles (AVs) and electric vehicles (EVs) will trigger disruptive changes to transportation systems via efficient driving, efficient use of travel time, reduced emissions and improved safety, mobility, roadway capacity, etc. However, these technologies might increase vehicle miles traveled (VMT), travel times, and ownership costs. Shared mobility systems can alleviate the high price of these technologies, but they will result in users' waiting time, inconvenience, and increased VMT. This study captures a variety of trade-offs in developing a fleet optimization multiclass user (with different value of travel time (VOTT)) problem considering AVs and EVs in both private and shared mobility systems. Two metaheuristic algorithms are developed and compared to solve the computationally complex large-scale non-linear optimization problem defined in this study. The case study (Ann Arbor, Michigan) results are sensitive to trip lengths, VOTT, emissions and travel time savings (in AVs). The proposed framework provide valuable insights for planning of emerging technologies.

Language: en

Keywords

Autonomous Vehicles; Electric Vehicles; Fleet Optimization; High-Performance Computing; Metaheuristic Algorithms; Shared Mobility System