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Abstract
We present a computational study of network ensembles with two types of coexisting vehicle classes: an altruistically routing vehicle (ARV) class – potentially automated vehicles that are routed to reduce total system travel time – and a selfishly routing vehicle (SRV) class, corresponding to human-driven vehicles. We investigate the performance of these networks when some links are reserved for exclusive use by the ARVs. The goal of these interventions is to avoid or mitigate the detrimental effects of the SRVs on the costs of the ARVs. We formulate the problem as a bi-level network design problem, where the upper level deals with optimising the choice of ARV-exclusive links minimising the statistical dispersion of used-route costs, while the lower level finds the corresponding traffic equilibrium under static traffic assignment conditions. We tackle the ARV-exclusive link selection with a genetic algorithm, where the fitness of solutions is based on the dispersion of the costs of routes used by ARVs. The mixed equilibrium is found by solving a multi-class static traffic assignment problem, with constraints on the SRV flows on the ARV-exclusive links. SRVs attempt to minimise their personal travel time, whilst ARVs attempt to drive the flows to system optimal. Our approach is effective in reducing the per-vehicle travel cost of the ARVs to below that of the SRVs, making altruistic routing a more attractive option on average. Our results are consistent across networks with different structures and demand levels.
Original language | English |
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Article number | 101042 |
Number of pages | 17 |
Journal | Transportation research interdisciplinary perspectives |
Volume | 25 |
DOIs | |
Publication status | Published - May 2024 |
MoE publication type | A1 Journal article-refereed |
Keywords
- Altruistic routing
- Automated vehicles
- Genetic algorithm
- Multiclass equilibrium
- Network design
- Traffic management
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Dive into the research topics of 'Network traffic management via exclusive roads for altruistic vehicles under mixed traffic equilibrium'. Together they form a unique fingerprint.Projects
- 1 Finished
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ALCOSTO: Adaptive and Learning COntrol strategies for Sustainable future Traffic Operations
Roncoli, C. (Principal investigator)
01/01/2022 → 31/12/2024
Project: RCF Academy Project