Road traffic density estimation using microscopic and macroscopic parameters

Ouessai, Asmâa; Keche, Mokhtar; Ouamri, Abdelaziz

doi:10.1016/j.imavis.2013.09.006

Cited by 42 publications

(31 citation statements)

References 5 publications

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“…A comparative study of two approaches for road traffic density estimation from traffic video scenes was presented in [30]. Both the extracted microscopic parameters (i.e., individual vehicle motion parameters) and the macroscopic parameters (i.e., global motion parameters) are applied in classifiers to enable classification of light, medium, and heavy road traffic status.…”

Section: Protocolmentioning

confidence: 99%

Microscopic Congestion Detection Protocol in VANETs

Ahmad

Chen

Khan

2018

Journal of Advanced Transportation

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Effective transportation status surveillance imposes critical challenges for the Intelligent Transportation System (ITS) design. In this paper, the microscopic congestion detection protocol (MCDP) is proposed to make the vehicle-to-vehicle (V2V) communication capable of monitoring vehicle density and identifying traffic jam. By introducing transportation control domain in the existing network protocol header, each vehicle can count its neighbors and estimate the time spacing among vehicles. MCDP provides an infrastructure-less solution to the estimate of vehicle density, flow, and average velocity in a microscopically manner. Moreover, the safety speed limit is introduced to make each vehicle calculate its time to cover the intervehicle distance, such that every vehicle is able to assess the transportation congestion by comparing with some predefined safety time threshold. Monte Carlo simulations of the MCDP over four typical Chinese highways are presented to compare the MCDP scheme with the existing GreenShield congestion detection scheme. In addition, real road traces generated by SUMO over NS2 are utilized to show the achieved performance in terms of throughput, end-to-end delay, and packet delivery rate (PDR) in comparison to DSR and AOMDV in IEEE 802.11p and IEEE 802.11ac scenarios. On the basis of all the results, we conclude that MCDP is an inexpensive transport congestion detection technique for Vehicular Ad Hoc Networks (VANETs).

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Section: Protocolmentioning

confidence: 99%

Microscopic Congestion Detection Protocol in VANETs

Ahmad

Chen

Khan

2018

Journal of Advanced Transportation

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“…The authors used neural networks methods to extract, count and classify vehicles from video records. In a more recent work, a comparative study of two approaches for road traffic density estimation was carried out in [22]. The first approach used the microscopic parameters which were extracted using motion detection and tracking methods from a video sequence.…”

Section: Infrastructure-based Vehicular Density Estimation Techniquesmentioning

confidence: 99%

Traffic density estimation in vehicular ad hoc networks: A review

2015

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“…The techniques generally comprise thresholding, multi-resolution processing, edge detection, background subtraction and inter-frame differencing [3]. There are two approaches for road traffic density estimation, microscopic and macroscopic approach [4].…”

Section: Introductionmentioning

confidence: 99%

“…The microscopic approach produces a low accuracy because of it dependence on the quality of the environment, such as lighting and weather [5]. Moreover, it cannot be performed reliably on low resolution images or when there are many objects on the scene, for example, on the crowded highway scenes [4].…”

Section: Introductionmentioning

confidence: 99%

Image Processing Technique for Traffic Density Estimation

Kurniawan¹,

Sajati²,

Dinaryanto³

2017

IJET

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Abstract-Various techniques for the traffic density estimation in heavy traffic have been developed widely. However, most of them suffer from any drawbacks, especially for traffic fulfilling all kinds of vehicles. In the present study, a new technique of traffic density estimation using a macroscopic approach has been developed. This technique used a background construction and a traffic density estimation algorithm. The first algorithm detects parts of the image containing no moving vehicle in front of or behind the moving vehicle using inter-frame difference. After that, an edge detector detects the edge of image part and classifies whether the image can be used as a part of the background image or not. Meanwhile the second algorithm estimates the traffic density by calculating a ratio between the road area covered by vehicle and the total road area. As a result, this technique has a higher accuracy for determining the traffic density compared with the previous techniques.

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Road traffic density estimation using microscopic and macroscopic parameters

Cited by 42 publications

References 5 publications

Microscopic Congestion Detection Protocol in VANETs

Microscopic Congestion Detection Protocol in VANETs

Traffic density estimation in vehicular ad hoc networks: A review

Image Processing Technique for Traffic Density Estimation

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