Day-to-day dynamic traffic assignment with imperfect information, bounded rationality and information sharing

Yu, Yang Yang; Han, Ke; Ochieng, Washington Yotto

doi:10.1016/j.trc.2020.02.004

Cited by 40 publications

(14 citation statements)

References 80 publications

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“…In a different perspective, works such as Yu, Han & Ochieng (2020) evaluate the impact of incomplete information sharing in the TAP. They do not employ a RL-based but rather a classical approach, namely multinomial Logit model.…”

Section: Background and Related Workmentioning

confidence: 99%

Sharing diverse information gets driver agents to learn faster: an application in en route trip building

Santos¹,

Bazzan²

2021

PeerJ Computer Science

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With the increase in the use of private transportation, developing more efficient ways to distribute routes in a traffic network has become more and more important. Several attempts to address this issue have already been proposed, either by using a central authority to assign routes to the vehicles, or by means of a learning process where drivers select their best routes based on their previous experiences. The present work addresses a way to connect reinforcement learning to new technologies such as car-to-infrastructure communication in order to augment the drivers knowledge in an attempt to accelerate the learning process. Our method was compared to both a classical, iterative approach, as well as to standard reinforcement learning without communication. Results show that our method outperforms both of them. Further, we have performed robustness tests, by allowing messages to be lost, and by reducing the storage capacity of the communication devices. We were able to show that our method is not only tolerant to information loss, but also points out to improved performance when not all agents get the same information. Hence, we stress the fact that, before deploying communication in urban scenarios, it is necessary to take into consideration that the quality and diversity of information shared are key aspects.

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Section: Background and Related Workmentioning

confidence: 99%

Sharing diverse information gets driver agents to learn faster: an application in en route trip building

Santos¹,

Bazzan²

2021

PeerJ Computer Science

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“…In some cases, the information is manipulated to bias the agents to reach an expected outcome, as in [Bazzan 2019]. In a different perspective, works like [Yu et al 2020] evaluate the impact of incomplete information sharing in the TAP. Lack or loss of information was also investigated in [Santos and Bazzan 2021], where robustness tests were performed in order to test the effect of communication failures and of a reduction in the storage capacity of the communication devices.…”

Section: Related Workmentioning

confidence: 99%

Using Car to Infrastructure Communication to Accelerate Learning in Route Choice

Santos

Bazzan

Baumgardt

2021

jidm

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The task of choosing a route to move from A to B is not trivial, as road networks in metropolitan areas tend to be over crowded. It is important to adapt on the fly to the traffic situation. One way to help road users (driver or autonomous vehicles for that matter) is by using modern communication technologies.In particular, there are reasons to believe that the use of communication between the infrastructure (network), and the demand (vehicles) will be a reality in the near future. In this paper, we use car-to-infrastructure (C2I) communication to investigate whether the road users can accelerate their learning processes regarding route choice by using reinforcement learning (RL). The kernel of our method is a two way communication, where road users communicate their rewards to the infrastructure, which, in turn, aggregate this information locally and pass it to other users, in order to accelerate their learning tasks. We employ a microscopic simulator in order to compare this method with two others (one based on RL without communication and a classical iterative method for traffic assignment). Experimental results using a grid and a simplification of a real-world network show that our method outperforms both.

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“…In some cases, the information is manipulated to bias the agents to reach an expected outcome. In a different perspective, works like [Yu et al 2020] evaluate the impact of incomplete information sharing in the TAP.…”

Section: Related Workmentioning

confidence: 99%

Accelerating Learning of Route Choices With C2I: A Preliminary Investigation

Santos¹,

Bazzan²

2020

Anais Do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe 2020)

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How to choose a route that takes you from A to B? This is an issue that is turning more and more important in modern societies. One way to address this agenda is through the use of communication between the infrastructure (network), and the demand (vehicles). In this paper, we use car-to-infrastructure (C2I) communication to investigate whether the road users (agents) can accelerate their learning process regarding route choice problem, via reinforcement learning (RL). We employ a microscopic simulator in order to compare our method with two others: RL without communication and an iterative method. Experimental results show that our method outperforms both methods in terms of effectiveness and efficiency.

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Day-to-day dynamic traffic assignment with imperfect information, bounded rationality and information sharing

Cited by 40 publications

References 80 publications

Sharing diverse information gets driver agents to learn faster: an application in en route trip building

Sharing diverse information gets driver agents to learn faster: an application in en route trip building

Using Car to Infrastructure Communication to Accelerate Learning in Route Choice

Accelerating Learning of Route Choices With C2I: A Preliminary Investigation

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