Liyuan Zheng

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  • Uncertainty in Multi-Commodity Routing Networks: When does it help?

    We study the equilibrium quality under user uncertainty in a multi-commodity selfish routing game with many types of users, where each user type experiences a different level of uncertainty. We consider a new model of uncertainty where each user-type over or under-estimates their congestion costs by a multiplicative constant. We present a variety of theoretical results showing that when users under-estimate their costs, the network congestion decreases at equilibrium, whereas over-estimation of costs leads to increased equilibrium congestion. Motivated by applications in urban transportation networks, we perform simulations consisting of parking users and through traffic on synthetic and realistic network topologies. In light of the dynamic pricing policies adopted by network operators to tackle congestion, our results indicate that while users' perception of these prices can significantly impact the policy's efficacy, optimism in the face of uncertainty leads to favorable network conditions.

    09/25/2017 ∙ by Shreyas Sekar, et al. ∙ 0 share

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  • Incentives in the Dark: Multi-armed Bandits for Evolving Users with Unknown Type

    Design of incentives or recommendations to users is becoming more common as platform providers continually emerge. We propose a multi-armed bandit approach to the problem in which users types are unknown a priori and evolve dynamically in time. Unlike the traditional bandit setting, observed rewards are generated by a single Markov process. We demonstrate via an illustrative example that blindly applying the traditional bandit algorithms results in very poor performance as measured by regret. We introduce two variants of classical bandit algorithms, upper confidence bound (UCB) and epsilon-greedy, for which we provide theoretical bounds on the regret. We conduct a number of simulation-based experiments to show how the algorithms perform in comparison to traditional UCB and epsilon-greedy algorithms as well as reinforcement learning (Q-learning).

    03/11/2018 ∙ by Lillian J. Ratliff, et al. ∙ 0 share

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  • Towards a Socially Optimal Multi-Modal Routing Platform

    The increasing rate of urbanization has added pressure on the already constrained transportation networks in our communities. Ride-sharing platforms such as Uber and Lyft are becoming a more commonplace, particularly in urban environments. While such services may be deemed more convenient than riding public transit due to their on-demand nature, reports show that they do not necessarily decrease the congestion in major cities. One of the key problems is that typically mobility decision support systems focus on individual utility and react only after congestion appears. In this paper, we propose socially considerate multi-modal routing algorithms that are proactive and consider, via predictions, the shared effect of riders on the overall efficacy of mobility services. We have adapted the MATSim simulator framework to incorporate the proposed algorithms present a simulation analysis of a case study in Nashville, Tennessee that assesses the effects of our routing models on the traffic congestion for different levels of penetration and adoption of socially considerate routes. Our results indicate that even at a low penetration (social ratio), we are able to achieve an improvement in system-level performance.

    02/27/2018 ∙ by Chinmaya Samal, et al. ∙ 0 share

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  • Combinatorial Bandits for Incentivizing Agents with Dynamic Preferences

    The design of personalized incentives or recommendations to improve user engagement is gaining prominence as digital platform providers continually emerge. We propose a multi-armed bandit framework for matching incentives to users, whose preferences are unknown a priori and evolving dynamically in time, in a resource constrained environment. We design an algorithm that combines ideas from three distinct domains: (i) a greedy matching paradigm, (ii) the upper confidence bound algorithm (UCB) for bandits, and (iii) mixing times from the theory of Markov chains. For this algorithm, we provide theoretical bounds on the regret and demonstrate its performance via both synthetic and realistic (matching supply and demand in a bike-sharing platform) examples.

    07/06/2018 ∙ by Tanner Fiez, et al. ∙ 0 share

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