Bayesian Optimization of Road Pricing using Agent-based Mobility Simulation

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Abstract

Road pricing policies are frequently debated but not widely adopted. Tools for designing near practice-ready policies are still missing, especially considering the complex dynamics between the different levels of traveller decision-making and the networks’ performance. We couple an agentand activity-driven mobility simulator with a Bayesian Optimization (BO) framework for designing optimal road pricing policy in a daily mobility and transportation network system. We extend the literature with a BO-framework application to distance-based road pricing under a departuretime and route-choice sensitive demand model combined with a detailed mesoscopic network. We then tested a general BO and a recently proposed contextual BO algorithm for SimMobility and computational performance. Both identified a similar optimum distance-based pricing, with the second being more computationally efficient. Nonetheless, iterations number, increasing search space and dimensionality could limit their performance. Lastly, the effects of the identified policy were analyzed by leveraging the outcome capabilities of SimMobility.
Original languageEnglish
Publication date2023
Number of pages10
Publication statusPublished - 2023
Event11th Symposium of the European Association for Research in Transportation - ETH Zurich, Zurich, Switzerland
Duration: 6 Sept 20238 Dec 2023
Conference number: 11
http://heart2023.org/

Conference

Conference11th Symposium of the European Association for Research in Transportation
Number11
LocationETH Zurich
Country/TerritorySwitzerland
CityZurich
Period06/09/202308/12/2023
Internet address

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