Network Learning via Multiagent Inverse Transportation Problems

  • Susan Jia Xu
    C2SMART University Transportation Center, Department of Civil and Urban Engineering, New York University, Brooklyn, New York 11201
  • Mehdi Nourinejad
    Rotman School of Management, University of Toronto, Toronto, Ontario M5S 3E6, Canada
  • Xuebo Lai
    Department of Computer Science, Courant Institute of Mathematical Sciences, New York University, New York, New York 10012
  • Joseph Y. J. Chow
    C2SMART University Transportation Center, Department of Civil and Urban Engineering, New York University, Brooklyn, New York 11201

抄録

<jats:p> Despite the ubiquity of transportation data, methods to infer the state parameters of a network either ignore sensitivity of route decisions, require route enumeration for parameterizing descriptive models of route selection, or require complex bilevel models of route assignment behavior. These limitations prevent modelers from fully exploiting ubiquitous data in monitoring transportation networks. Inverse optimization methods that capture network route choice behavior can address this gap, but they are designed to take observations of the same model to learn the parameters of that model, which is statistically inefficient (e.g., requires estimating population route and link flows). New inverse optimization models and supporting algorithms are proposed to learn the parameters of heterogeneous travelers’ route behavior to infer shared network state parameters (e.g., link capacity dual prices). The inferred values are consistent with observations of each agent’s optimization behavior. We prove that the method can obtain unique dual prices for a network shared by these agents in polynomial time. Four experiments are conducted. The first one, conducted on a four-node network, verifies the methodology to obtain heterogeneous link cost parameters even when multinomial or mixed logit models would not be meaningfully estimated. The second is a parameter recovery test on the Nguyen–Dupuis network that shows that unique latent link capacity dual prices can be inferred using the proposed method. The third test on the same network demonstrates how a monitoring system in an online learning environment can be designed using this method. The last test demonstrates this learning on real data obtained from a freeway network in Queens, New York, using only real-time Google Maps queries. </jats:p>

収録刊行物

  • Transportation Science

    Transportation Science 52 (6), 1347-1364, 2018-12

    Institute for Operations Research and the Management Sciences (INFORMS)

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