In recent times, there have been rapid advancements in technologies that can bring about fully autonomous driving. This can potentially result in substantial changes in the road network and traffic operations. People previously not being able to travel frequently (e.g., the elderly and people with disabilities) will be able to commute themselves. Due to this intuitive property of autonomous vehicles, additional trip production may produce an impact on travel costs between some origin-destination pairs. These unwanted changes create both positive and negative impacts on the network users resulting in an inequity issue. It is necessary to evaluate the inequity caused to a whole society by providing benefits through autonomous driving to a certain group of people. In the above context, this paper uses a bi-level optimization model technique to find an optimal solution of a multi-objective function. The proposed model aims to evaluate the maximum growth of trips that a network can support without causing inequity to other network users. The upper level of the model maximizes the generation of trips constrained by the inequity parameters, whereas the lower level minimizes the travel equilibrium costs for all the users by assigning trips to the network following the multinomial logit principle. The solution to the proposed model is provided by a multi-objective genetic algorithm.
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