Traffic Optimization For a Mixture of Self-interested and Compliant Agents

AAAI, 2018.

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Going forward, it would be worthwhile to explore the possibility of approximation algorithms for assigning compliant flow when the user equilibrium flow volume is too large to achieve a state of system optimum

Abstract:

This paper focuses on two commonly used path assignment policies for agents traversing a congested network: self-interested routing, and system-optimum routing. In the self-interested routing policy each agent selects a path that optimizes its own utility, while the system-optimum routing agents are assigned paths with the goal of maximiz...More

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Introduction
  • There are generally two paradigms of interaction. Centralized control paradigms assume that a single decision making entity is able to dictate the actions of all the agents, leading them to a coordinated social optimum.
  • The authors consider a routing scenario in which a subset of agents are controlled centrally, while the remaining are self-interested agents.
  • The authors model the system as a Stackelberg routing game (Yang, Zhang, and Meng 2007) in which the decision maker for the centrally controlled agents is the leader, and the self-interested agents are the followers.
  • The authors provide a computationally tractable methodology for 1) determining the maximum number of self-interested agents that a system can tolerate at optimal flow, 2) determining whether a given subset of centrally controlled agents are sufficient to achieve system optimum (SO), and 3) computing the actions the leader should prescribe to a sufficient set of compliant agents in order to achieve SO
Highlights
  • In multiagent systems, there are generally two paradigms of interaction
  • We provide a computationally tractable methodology for 1) determining the maximum number of self-interested agents that a system can tolerate at optimal flow, 2) determining whether a given subset of centrally controlled agents are sufficient to achieve system optimum (SO), and 3) computing the actions the leader should prescribe to a sufficient set of compliant agents in order to achieve system optimum
  • A methodology was presented for computing the minimal volume of traffic flow that needs to be compliant in order to reach a state of optimal traffic flow
  • The methodology extends to inferring which agents should be compliant and how exactly the compliant agents should be assigned to paths
  • Experimental results demonstrate that the required percentage of agents that are compliant is small for some scenarios but can be greater than 50% in others
  • Going forward, it would be worthwhile to explore the possibility of approximation algorithms for assigning compliant flow when the user equilibrium flow volume is too large to achieve a state of system optimum
Results
  • The authors are interested in the viability of opt-in micro-tolling schemes to more efficiently utilize road networks.
  • The authors haven undertaken an empirical study to investigate the minimal amount of compliant flow required for SO in six realistic traffic scenarios over actual road networks.
  • Table 1 presents the percentage of flow that must be compliant in order to guarantee an SO solution for six different traffic scenarios.
  • Each scenario is affiliated with the number of vertices, links, and zones comprising the affiliated road network as well as the number of trips that make up the affiliated demand.
Conclusion
  • This paper discussed a scenario where a set of agents traverse a congested network, while a centralized network manager is seeking to optimize the flow by influencing the route assignment of a set of compliant agents.
  • A methodology was presented for computing the minimal volume of traffic flow that needs to be compliant in order to reach a state of optimal traffic flow.
  • Going forward, it would be worthwhile to explore the possibility of approximation algorithms for assigning compliant flow when the UE flow volume is too large to achieve a state of system optimum.
  • In order to limit the necessary opt-in incentives, there is work needed to develop systems that target influential users to opt-in to these systems
Summary
  • Introduction:

    There are generally two paradigms of interaction. Centralized control paradigms assume that a single decision making entity is able to dictate the actions of all the agents, leading them to a coordinated social optimum.
  • The authors consider a routing scenario in which a subset of agents are controlled centrally, while the remaining are self-interested agents.
  • The authors model the system as a Stackelberg routing game (Yang, Zhang, and Meng 2007) in which the decision maker for the centrally controlled agents is the leader, and the self-interested agents are the followers.
  • The authors provide a computationally tractable methodology for 1) determining the maximum number of self-interested agents that a system can tolerate at optimal flow, 2) determining whether a given subset of centrally controlled agents are sufficient to achieve system optimum (SO), and 3) computing the actions the leader should prescribe to a sufficient set of compliant agents in order to achieve SO
  • Results:

    The authors are interested in the viability of opt-in micro-tolling schemes to more efficiently utilize road networks.
  • The authors haven undertaken an empirical study to investigate the minimal amount of compliant flow required for SO in six realistic traffic scenarios over actual road networks.
  • Table 1 presents the percentage of flow that must be compliant in order to guarantee an SO solution for six different traffic scenarios.
  • Each scenario is affiliated with the number of vertices, links, and zones comprising the affiliated road network as well as the number of trips that make up the affiliated demand.
  • Conclusion:

    This paper discussed a scenario where a set of agents traverse a congested network, while a centralized network manager is seeking to optimize the flow by influencing the route assignment of a set of compliant agents.
  • A methodology was presented for computing the minimal volume of traffic flow that needs to be compliant in order to reach a state of optimal traffic flow.
  • Going forward, it would be worthwhile to explore the possibility of approximation algorithms for assigning compliant flow when the UE flow volume is too large to achieve a state of system optimum.
  • In order to limit the necessary opt-in incentives, there is work needed to develop systems that target influential users to opt-in to these systems
Tables
  • Table1: Required fraction of compliant agents given as “% compliant” for different scenarios along with network specifications for each scenario: number of vertices, links and zones followed by the Total Travel Time (TTT) at UE (0% compliant agents) and SO (100% compliant agents). The percentage of improvement of the SO TTT over the UE TTT is given as “% improve”
Download tables as Excel
Related work
  • Previous work examined mixed equilibrium scenarios where traffic is composed of: UE and Cournot-Nash (CN ) controllers. A CN -controller assigns flows to a given subset of the demand with the aim of minimizing the total travel time only for that subset. For instance, a logistic company with many trucks can be viewed as a CN -controller.

    It was shown that the equilibrium for a mixed UE, CN scenario is unique and can be computed using a convex program (Haurie and Marcotte 1985; Yang and Zhang 2008). On the other hand, no tractable algorithm is known for computing the optimal Stackelberg equilibrium for scenarios that also include a SO-controller.

    Korilis et. al. (1997) examined mixed equilibrium scenarios that do include a SO-controller. In their work, a technique for computing a solution for the above questions #1 and #3 was suggested for specific types of flow models. Their technique was proven to work for networks with a common source and a common target with any number of parallel links. Moreover, the latency functions were assumed to be of a very specific form (linear function with a capacity bound). As a result, their solution is not applicable when general networks with arbitrary latency functions are considered.
Funding
  • LARG research is supported in part by NSF (IIS-1637736, IIS1651089, IIS-1724157), Intel, Raytheon, and Lockheed Martin
  • The authors would like to thank the Texas Department of Transportation for supporting this research under project 0-6838, Bringing Smart Transport to Texans: Ensuring the Benefits of a Connected and Autonomous Transport System in Texas
  • The authors would also like to acknowledge the support of the Data-Supported Transportation Operations & Planning Center and the National Science Foundation under Grant No 1254921
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