Dynamic relocation in ridesharing via fixpoint construction

Ian A. Kash, Zhongkai Wen, Lenore D. Zuck
Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, PMLR 180:980-989, 2022.

Abstract

To address spatial imbalances in the supply and demand of drivers, ridesharing platforms can make use of policies to direct driver relocation. We study a simple model of this problem, which allows us to give a constructive characterization of the unique fixpoint of system dynamics. Using this construction, we design a dynamic policy that provides stronger, than previous work, guarantees about its rate of convergence to the fixpoint. Simulations demonstrate the benefits of our approach.

Cite this Paper

BibTeX
@InProceedings{pmlr-v180-kash22a, title = {Dynamic relocation in ridesharing via fixpoint construction }, author = {Kash, Ian A. and Wen, Zhongkai and Zuck, Lenore D.}, booktitle = {Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence}, pages = {980--989}, year = {2022}, editor = {Cussens, James and Zhang, Kun}, volume = {180}, series = {Proceedings of Machine Learning Research}, month = {01--05 Aug}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v180/kash22a/kash22a.pdf}, url = {https://proceedings.mlr.press/v180/kash22a.html}, abstract = {To address spatial imbalances in the supply and demand of drivers, ridesharing platforms can make use of policies to direct driver relocation. We study a simple model of this problem, which allows us to give a constructive characterization of the unique fixpoint of system dynamics. Using this construction, we design a dynamic policy that provides stronger, than previous work, guarantees about its rate of convergence to the fixpoint. Simulations demonstrate the benefits of our approach.} }
Endnote
%0 Conference Paper %T Dynamic relocation in ridesharing via fixpoint construction %A Ian A. Kash %A Zhongkai Wen %A Lenore D. Zuck %B Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2022 %E James Cussens %E Kun Zhang %F pmlr-v180-kash22a %I PMLR %P 980--989 %U https://proceedings.mlr.press/v180/kash22a.html %V 180 %X To address spatial imbalances in the supply and demand of drivers, ridesharing platforms can make use of policies to direct driver relocation. We study a simple model of this problem, which allows us to give a constructive characterization of the unique fixpoint of system dynamics. Using this construction, we design a dynamic policy that provides stronger, than previous work, guarantees about its rate of convergence to the fixpoint. Simulations demonstrate the benefits of our approach.
APA
Kash, I.A., Wen, Z. & Zuck, L.D.. (2022). Dynamic relocation in ridesharing via fixpoint construction . Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 180:980-989 Available from https://proceedings.mlr.press/v180/kash22a.html.

Related Material