A Multi-agent Based System for Route Planning

Белый, Е М; Patel, Indravan; Reddy, A. Basi; Mago, Vijay

doi:10.1007/978-3-319-20612-7_48

Cited by 2 publications

(3 citation statements)

References 9 publications

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“…Machine learning has been heavily used in ITS [10] in mining mobile data stream [11], in constructing mathematical models [12] and for exchange of information among vehicles [13]. Bayesian network is adopted in our research because of its ability to concisely represent probabilistic relationships [14] and its previous successful application [15]. However, other methods can also be utilized to analyze data and perform predictions in our framework.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Bayesian network (BN) also known as Bayesian Belief network, is a probabilistic directed acyclic graphical model which represents the conditional independence relationships. The conditional independence is capable of probabilistic representation and reasoning among variables by using a directed acyclic graph (DAG) [15]. The nodes and directed edges of BN are random variables and conditional dependence relationships among variables, respectively.…”

Section: Bayesian Network Modelingmentioning

confidence: 99%

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SafeRNet: Safe transportation routing in the era of Internet of vehicles and mobile crowd sensing

Kumar

Mago

2017

2017 14th IEEE Annual Consumer Communications &Amp; Networking Conference (CCNC)

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World wide road traffic fatality and accident rates are high, and this is true even in technologically advanced countries like the USA. Despite the advances in Intelligent Transportation Systems, safe transportation routing i.e., finding safest routes is largely an overlooked paradigm. In recent years, large amount of traffic data has been produced by people, Internet of Vehicles and Internet of Things (IoT). Also, thanks to advances in cloud computing and proliferation of mobile communication technologies, it is now possible to perform analysis on vast amount of generated data (crowd sourced) and deliver the result back to users in real time. This paper proposes SafeRNet, a safe route computation framework which takes advantage of these technologies to analyze streaming traffic data and historical data to effectively infer safe routes and deliver them back to users in real time. SafeRNet utilizes Bayesian network to formulate safe route model. Furthermore, a case study is presented to demonstrate the effectiveness of our approach using real traffic data. SafeRNet intends to improve drivers safety in a modern technology rich transportation system.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Bayesian Network Modelingmentioning

confidence: 99%

SafeRNet: Safe transportation routing in the era of Internet of vehicles and mobile crowd sensing

Kumar

Mago

2017

2017 14th IEEE Annual Consumer Communications &Amp; Networking Conference (CCNC)

Self Cite

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“…These advantages are high flexibility, adaptability, scalability, and robustness in decentralized systems, which are achieved by problem decomposition and the proactive, reactive, and adap-tive behavior of intelligent agents. Multiagent systems are mature sophisticated technology to implement autonomous industrial processes [25], [26] and they are also used in route planing [27].…”

Section: Related Workmentioning

confidence: 99%

Forecasting Adverse Weather Situations in the Road Network

Tomás

Pla‐Castells

Martínez

et al. 2016

IEEE Trans. Intell. Transport. Syst.

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A Multi-agent Based System for Route Planning

Cited by 2 publications

References 9 publications

SafeRNet: Safe transportation routing in the era of Internet of vehicles and mobile crowd sensing

SafeRNet: Safe transportation routing in the era of Internet of vehicles and mobile crowd sensing

Forecasting Adverse Weather Situations in the Road Network

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