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Assessing the Access to Jobs by Shared Autonomous Vehicles in Marysville, Ohio: Modeling, Simulating and Validating
- Karina Meneses Cime - The Ohio State University ,
- Mustafa Ridvan Cantas - The Ohio State University ,
- Pedro Fernandez - The Ohio State University ,
- Bilin Aksun Guvenc - The Ohio State University ,
- Levent Guvenc - The Ohio State University ,
- Adit Joshi - Ford Motor Company ,
- James Fishelson - Ford Motor Company ,
- Archak Mittal - Ford Motor Company
ISSN: 2641-9637, e-ISSN: 2641-9645
Published April 06, 2021 by SAE International in United States
Event: SAE WCX Digital Summit
Citation: Meneses Cime, K., Cantas, M., Fernandez, P., Aksun Guvenc, B. et al., "Assessing the Access to Jobs by Shared Autonomous Vehicles in Marysville, Ohio: Modeling, Simulating and Validating," SAE Int. J. Adv. & Curr. Prac. in Mobility 3(5):2509-2515, 2021, https://doi.org/10.4271/2021-01-0163.
Autonomous vehicles are expected to change our lives with significant applications like on-demand, shared autonomous taxi operations. Considering that most vehicles in a fleet are parked and hence idle resources when they are not used, shared on-demand services can utilize them much more efficiently. While ride hailing of autonomous vehicles is still very costly due to the initial investment, a shared autonomous vehicle fleet can lower its long-term cost such that it becomes economically feasible. This requires the Shared Autonomous Vehicles (SAV) in the fleet to be in operation as much as possible. Motivated by these applications, this paper presents a simulation environment to model and simulate shared autonomous vehicles in a geo-fenced urban setting. To simulate the aforementioned applications, a simulation environment that has a realistic rendering of the chosen real-world environment with realistic traffic generated around the SAVs is developed first using a geo-fenced area centered at the city of Marysville in Ohio as an example. This paper, then, presents an algorithm to optimally utilize multiple autonomous vehicles for shared rides based on modeling of pickup locations corresponding to affordable housing at the periphery of the geo-fenced area connected to destination locations corresponding to jobs and other locations of opportunity. The presented work showcases SAV operation as a solution to the spatial mismatch between affordable housing and job locations in a realistic simulation environment in an urban setting.