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| Title: | Overcoming mobility poverty with shared autonomous vehicles: A learning-based optimization approach for Rotterdam Zuid | | | Authors: | B.A. Beirigo, F. Schulte, R.R. Negenborn |
| | Conference: | 11th International Conference on Computational Logistics (ICCL'20) | | Address: | Enschede, The Netherlands | | Date: | September 2020 |
| | Abstract: | Residents of cities' most disadvantaged areas face significant barriers to key life activities, such as employment, education, and healthcare, due to the lack of mobility options. The widespread adoption of shared autonomous vehicles (SAVs) creates an opportunity to overcome this problem. By learning user demand patterns, SAV providers can improve service levels by laying out anticipatory relocation strategies that take into consideration when and where requests are more likely to appear. The nature of transportation demand, however, invariably creates learning biases towards servicing cities' most affluent and densely populated areas, where alternative mobility choices already abound. As a result, current disadvantaged regions may end up perpetually underserved, therefore preventing all city residents from enjoying the benefits of autonomous mobility-on-demand (AMoD) systems equally. In this study, we propose an anticipatory rebalancing policy that aims to compensate for the lack of transportation and mobility options in cities' most underserved areas. This policy integrates an approximate dynamic programming (ADP) formulation that processes historical demand data to estimate value functions of future system states iteratively. We investigate the extent to which manipulating cost settings, in terms of subsidies and penalties, can override the demand patterns naturally incorporated into value functions to improve service levels of disadvantaged areas. We show for a case study in the city of Rotterdam, The Netherlands, that the proposed method can harness these cost schemes to better cater to users departing from these disadvantaged areas, substantially outperforming myopic and reactive benchmark policies. |
| | Reference: | Overcoming mobility poverty with shared autonomous vehicles: A learning-based optimization approach for Rotterdam Zuid. B.A. Beirigo, F. Schulte, R.R. Negenborn. In Proceedings of the 11th International Conference on Computational Logistics (ICCL'20), Enschede, The Netherlands, pp. 492-506, September 2020. | | | Request: | A
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