Personalized real-time traffic information provision: Agent-based optimization model and solution framework

Ma, Jiaqi; Smith, Brian L.; Zhou, Xuesong

doi:10.1016/j.trc.2015.03.004

Cited by 55 publications

(19 citation statements)

References 34 publications

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“…The day-to-day framework proposed by Jia et al (2011) is used in this study to simulate travelers with different levels of information availability and cognitive limitations, implemented through a set of bounded rationality rules. By developing a mixed integer programming model, Ma et al (2015) further extended the above simulation-based model to an agent-based optimization framework to improve system-wide mobility measures through providing personalized real-time travel time information.…”

Section: Day-to-day Emission Dynamics In a Work Zone Test Casementioning

confidence: 99%

Integrating a simplified emission estimation model and mesoscopic dynamic traffic simulator to efficiently evaluate emission impacts of traffic management strategies

Zhou

Tanvir

Lei

et al. 2015

Transportation Research Part D: Transport and Environment

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Section: Day-to-day Emission Dynamics In a Work Zone Test Casementioning

confidence: 99%

Integrating a simplified emission estimation model and mesoscopic dynamic traffic simulator to efficiently evaluate emission impacts of traffic management strategies

Zhou

Tanvir

Lei

et al. 2015

Transportation Research Part D: Transport and Environment

Self Cite

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“…For example, in [20], [21] and [22], sensor networks for monitoring traffic are proposed. The authors of [23]- [25] use ABM for traffic optimization and simulation. Reference [23] describes an ABM solution to generate personalized real-time data to present route information to travelers.…”

Section: Introductionmentioning

confidence: 99%

Agent-Based Modelling for Distributed Decision Support in an IoT Network

Butt¹,

Dey²,

Dzaferagic³

et al. 2020

Preprint

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<div>An increasing number of emerging applications, e.g.,</div><div>Internet of Things (IoT), vehicular communications, augmented reality, and the growing complexity due to the interoperability requirements of these systems, lead to the need to change the tools used for the modeling and analysis of those networks. Agent-Based Modeling (ABM) as a bottom-up modeling approach considers a network of autonomous agents interacting with each other, and therefore represents an ideal framework to comprehend the interactions of heterogeneous nodes in a complex environment. Here, we investigate the suitability of ABM to</div><div>model the communication aspects of a road traffic management system as an example of an IoT network. We model, analyze and compare various Medium Access Control (MAC) layer protocols for two different scenarios, namely uncoordinated and coordinated. Besides, we model the scheduling mechanisms for the coordinated scenario as a high level MAC protocol by using three different approaches: Centralized Decision Maker, DESYNC and decentralized learning MAC (L-MAC). The results clearly</div><div>show the importance of coordination between multiple decision makers in order to improve the information reporting error and spectrum utilization of the system.</div>

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“…without the need of an external information provider (Barthélemy and Carletti, 2017). Let us also observe that the introduction of a global information system, for instance GPS or radio, is not so straightforward because it requires to study and incorporate into the model the way agents decide to cope with such information (Ma et al, 2016). For instance the total solar eclipse of August 2017 caused disastrous traffic jams in various states across the United States of America, even though road traffic information was provided to the drivers through various means of communications (Wright, 2017).…”

Section: Introductionmentioning

confidence: 99%