[Submitted on 17 Feb 2020 (v1), last revised 23 Aug 2020 (this version, v3)]
Download PDFAbstract: A large portion of passenger requests is reportedly unserviced, partially dueto vacant for-hire drivers' cruising behavior during the passenger seekingprocess. This paper aims to model the multi-driver repositioning task through amean field multi-agent reinforcement learning (MARL) approach that capturescompetition among multiple agents. Because the direct application of MARL tothe multi-driver system under a given reward mechanism will likely yield asuboptimal equilibrium due to the selfishness of drivers, this study proposes areward design scheme with which a more desired equilibrium can be reached. Toeffectively solve the bilevel optimization problem with upper level as thereward design and the lower level as a multi-agent system, a Bayesianoptimization (BO) algorithm is adopted to speed up the learning process. Wethen apply the bilevel optimization model to two case studies, namely,e-hailing driver repositioning under service charge and multiclass taxi driverrepositioning under NYC congestion pricing. In the first case study, the modelis validated by the agreement between the derived optimal control from BO andthat from an analytical solution. With a simple piecewise linear servicecharge, the objective of the e-hailing platform can be increased by 8.4%. Inthe second case study, an optimal toll charge of $5.1 is solved using BO, whichimproves the objective of city planners by 7.9%, compared to that without anytoll charge. Under this optimal toll charge, the number of taxis in the NYCcentral business district is decreased, indicating a better traffic condition,without substantially increasing the crowdedness of the subway system.
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Submission history
From: Zhenyu Shou [view email]
[v1] Mon, 17 Feb 2020 00:10:58 UTC (2,267 KB)
[v2] Sat, 29 Feb 2020 17:51:40 UTC (2,827 KB)
[v3]Sun, 23 Aug 2020 16:48:23 UTC (2,962 KB)
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