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This paper investigates deploying connected and automated vehicle (CAV) lanes in transportation networks with a focus on measuring and preserving equity among travelers. A new metric is proposed to characterize equity based on (1) generalized travel cost per unit origin-destination (OD) distance for travelers on each OD pair and using each vehicle type and (2) maximum deviation of the standardized unit generalized travel cost from system average. A bi-level bi-objective program is developed to simultaneously minimize system travel cost and inequity while deploying CAV lanes. A solution algorithm that combines nondominated sorting genetic algorithm II and variable neighborhood search is designed. Through extensive numerical experiments, we find (1) inequity is more prominent when travel demand is high; (2) human-driven vehicle travelers become more disadvantageous with lower CAV price and higher CAV automation; and (3) subsidy is effective in mitigating inequity, but a fee for using CAV lanes is less promising.
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Mobility Equity and Economic Sustainability Using Game Theory
In this paper, we consider a multi-modal mobility system of travelers each with an individual travel budget, and propose a game-theoretic framework to assign each traveler to a “mobility service” (each one representing a different mode of transportation). We are interested in equity and sustainability, thus we maximize the worst-case revenue of the mobility system while ensuring “mobility equity,” which we define it in terms of accessibility. In the proposed framework, we ensure that all travelers are truthful and voluntarily participate under informational asymmetry, and the solution respects the individual budget of each traveler. Each traveler may seek to travel using multiple services (e.g., car, bus, train, bike). The services are capacitated and can serve up to a fixed number of travelers at any instant of time. Thus, our problem falls under the category of many-to-one assignment problems, where the goal is to find the conditions that guarantee the stability of assignments. We formulate a linear program of maximizing worst-case revenue under the constraints of mobility equity, and we fully characterize the optimal solution.
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- PAR ID:
- 10421258
- Date Published:
- Journal Name:
- 2023 American Control Conference
- Page Range / eLocation ID:
- 1698-1703
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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