An algorithm for the recognition of levels of congestion in road traffic problems

Lozano, Angélica; Manfredi, Giuseppe; Nieddu, Luciano

doi:10.1016/j.matcom.2007.06.008

Cited by 46 publications

(20 citation statements)

References 17 publications

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“…Recognition techniques can be used to determine the level of traffic congestion [29,30] in order to automatically annotate traffic data. Detecting and identifying objects in images or video sequences and annotating events, such as car accidents or the type of road incident, can be accomplished by applying current object detection and identification algorithms [31,32].…”

Section: A Design Considerationmentioning

confidence: 99%

Seeing Is Believing: Sharing Real-Time Visual Traffic Information via Vehicular Clouds

et al. 2016

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From today's conventional cars to tomorrow's selfdriving cars, advances in technology will enable vehicles to be equipped with more and more-sophisticated sensing devices, such as cameras. As vehicles gain the ability to act as mobile sensors that carry useful traffic information, people and vehicles are sharing sensing data to enhance the driving experience. This paper describes a vehicular cloud service for route planning, where users collaborate to share traffic images by using their vehicles' on-board cameras. We present the architecture of a collaborative traffic image-sharing system called Social Vehicle Navigation (SVN), which allows drivers in the vehicular cloud to report and share visual traffic information called NaviTweets. A set of NaviTweets is then filtered, refined, and condensed into a concise, user-friendly snapshot summary of the route of interest, called a Traffic Digest. These digests can provide more pertinent and reliable information about the road situation and can complement predictions like estimated time of arrival, thereby supporting users' route decision making. As proof of concept, this paper presents the system design and a prototype implementation running on the Android smartphone platform, along with its evaluation.

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Section: A Design Considerationmentioning

confidence: 99%

Seeing Is Believing: Sharing Real-Time Visual Traffic Information via Vehicular Clouds

et al. 2016

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“…In [3], it is proposed an algorithm that allows you to identify the level of congestion of an intersection.…”

Section: Previous Workmentioning

confidence: 99%

A model to estimate the throughput value for a transport network

Haragos

Holban

Cernazanu-Glavan

2013

2013 IEEE 8th International Symposium on Applied Computational Intelligence and Informatics (SACI)

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During the last period, road traffic has become one of the most studied topics. The main problem in urban transport network is congestion. Congestion is due to a several factors such as infrastructure, the ratio of the large number of vehicles and road network capacity as well as traffic signals mode. For this reason, several models have been developed in order to estimate the throughput value for a street or intersection. In this paper, we propose a new model to estimate the throughput for node of the network. This analytical model is developed based on existing elements in queueing theory. The model takes into account an attenuation factor that describes the quality of street/connection between network nodes, more precisely it shows the traffic status in analyzed section (traffic jam or traffic fluently).

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“…Therefore, to address these shortcomings, the historical specific traffic flow data should be studies sufficiently. Lozano et al [5] proposed a recognition algorithm for road congestion levels by analyzing real-time traffic flow data based on the K-means clustering analysis algorithm. Sun [6] employed the fuzzy c-means (FMC) cluster analysis and fuzzy synthetic evaluation method to classify urban traffic into six different states classification, but it is difficult to obtain the quantitative evaluation results of traffic state.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Selecting Gated Community as Opening Its Micro-Inter-Road Network

Dong¹,

Rinoshika²,

Tang³

2018

ENG

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The opening of gated community to expand the micro-road network in the urban traffic system is a hot topic on the urban congestion. To satisfy the demand of opening early choosing case, this paper proposed a comprehensive selecting framework on qualified communities and its appropriate opening time. Firstly, the static influential factors on internal road structure, boundary road structure and traffic flow are qualitatively analyzed. Then, an evaluation opening state index system based on describing accurately traffic flow state is obtained, which takes the opening factors into account at the boundary road network. In this structure, the modified fuzzy C-means (FCM) method calculates the fuzzy entropy weight and range of each opening states index. Finally, the simulation results show that the proposed method is capable of selecting qualified community and the optimum opening time.

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An algorithm for the recognition of levels of congestion in road traffic problems

Cited by 46 publications

References 17 publications

Seeing Is Believing: Sharing Real-Time Visual Traffic Information via Vehicular Clouds

Seeing Is Believing: Sharing Real-Time Visual Traffic Information via Vehicular Clouds

A model to estimate the throughput value for a transport network

Analysis of Selecting Gated Community as Opening Its Micro-Inter-Road Network

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