An efficient algorithm for detecting traffic congestion and a framework for smart traffic control system

Islam, Md. Rokebul; Shahid, Nafis Ibn; Karim, Dewan Tanzim ul; Mamun, Abdullah Al; Rhaman, Md. Khalilur

doi:10.1109/icact.2016.7423566

Cited by 20 publications

(10 citation statements)

References 19 publications

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“…Due to its operability and collectability, traffic congestion features based on the V/C ratio were widely used. In previous researches, the determination of traffic congestion is based on a specific feature such as vehicle speed and occupancy rate [14], [15]. Islam et al [14] proposed a smart traffic control system based on the measurement of traffic density using the real-time video processing technique, which can detect the traffic congestion.…”

Section: A Prediction Of Traffic Congestionmentioning

confidence: 99%

“…In previous researches, the determination of traffic congestion is based on a specific feature such as vehicle speed and occupancy rate [14], [15]. Islam et al [14] proposed a smart traffic control system based on the measurement of traffic density using the real-time video processing technique, which can detect the traffic congestion. Yang et al [15] proposed a framework for the prediction of traffic congestion.…”

Section: A Prediction Of Traffic Congestionmentioning

confidence: 99%

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MF-TCPV: A Machine Learning and Fuzzy Comprehensive Evaluation-Based Framework for Traffic Congestion Prediction and Visualization

Lin

Wan

et al. 2020

IEEE Access

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Section: A Prediction Of Traffic Congestionmentioning

confidence: 99%

Section: A Prediction Of Traffic Congestionmentioning

confidence: 99%

MF-TCPV: A Machine Learning and Fuzzy Comprehensive Evaluation-Based Framework for Traffic Congestion Prediction and Visualization

Lin

Wan

et al. 2020

IEEE Access

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“…It is variable depending on the time. It is high at the peak time or during emergency period whereas it is much less during late night [14]. In addition, the density is not the same in urban, rural, and highway.…”

Section: Affecting Factors On Groupingmentioning

confidence: 92%

Grouping vehicles in Vehicular Social Networks

Mahdi

Hasson

2017

KJAR

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As vehicles have become intelligent objects, creating social friendships among them is justifiable. Internet of Vehicles (IoVs) and Vehicular Ad-hoc NETworks (VANETs) are associated terms that are recently highlighted to improve the transportation systems. Intelligent vehicles that can communicate with each other are the main component of vehicular networks (VNs). Indeed, they have resources for that intelligence: CPU, transceiver, sensors and memory. On the other side, social networks (SNs) are also brought to light to mainly study the human relationships. In this context, gathering these vehicles in social groups is reasonable to share their information by bridging VN and SN in a multidisciplinary research direction called vehicular social networks (VSNs).This paper will present a model to group vehicles in two types of social groups. Casual groups and permanent or formal groups will be discussed to increase social ties among vehicles that have the same interests. The results will be analyzed to clarify the most important factors that affect such these relationships.

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“…There are some investigations into multiple modalities for congestion sensing from more indirect measures such as weather [5] and social media [6], [7], as well as more direct observations of the traffic network, such as the use of computer vision for analysing images for signs of congestion [8], [9].…”

Section: Summary Of Prior Artmentioning

confidence: 99%

“…Congestion determinations can also be part of a feedback loop into smart or autonomous control systems [8], [10], [11], thereby enabling the action taken to affect the sensed stimulus.…”

Section: Summary Of Prior Artmentioning

confidence: 99%

Distributed opportunistic sensing and fusion for traffic congestion detection

Notile

Harborne

Braines

et al. 2017

2017 IEEE SmartWorld, Ubiquitous Intelligence &Amp; Computing, Advanced &Amp; Trusted Computed, Scalable Computing &Amp; Commun

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Abstract-Our particular research in the Distributed Analytics and Information Science International Technology Alliance (DAIS ITA) is focused on "Anticipatory Situational Understanding for Coalitions". This paper takes the concrete example of detecting and predicting traffic congestion in the UK road transport network from existing generic sensing sources, such as real-time CCTV imagery and video, which are publicly available for this purpose. This scenario has been chosen carefully as we believe that in a typical city, all data relevant to transport network congestion information is not generally available from a single unified source, and that different organizations in the city (e.g. the weather office, the police force, the general public, etc.) have their own different sensors which can provide information potentially relevant to the traffic congestion problem. In this paper we are looking at the problem of (a) identifying congestion using cameras that, for example, the police department may have access to, and (b) fusing that with other data from other agencies in order to (c) augment any base data provided by the official transportation department feeds. By taking this coalition approach this requires using standard cameras to do different supplementary tasks like car counting, and in this paper we examine how well those tasks can be done with RNN/CNN, and other distributed machine learning processes.In this paper we provide details of an initial four-layer architecture and potential tooling to enable rapid formation of human/machine hybrid teams in this setting, with a focus on opportunistic and distributed processing of the data at the edge of the network. In future work we plan to integrate additional data-sources to further augment the core imagery data.

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An efficient algorithm for detecting traffic congestion and a framework for smart traffic control system

Cited by 20 publications

References 19 publications

MF-TCPV: A Machine Learning and Fuzzy Comprehensive Evaluation-Based Framework for Traffic Congestion Prediction and Visualization

MF-TCPV: A Machine Learning and Fuzzy Comprehensive Evaluation-Based Framework for Traffic Congestion Prediction and Visualization

Grouping vehicles in Vehicular Social Networks

Distributed opportunistic sensing and fusion for traffic congestion detection

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