A New Traffic Congestion Detection and Quantification Method Based on Comprehensive Fuzzy Assessment in VANET

doi:10.3837/tiis.2018.01.003

Cited by 8 publications

(3 citation statements)

References 19 publications

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“…Over the past decades, traffic congestion has become increasingly serious. Dynamic traffic management is a common method for reducing congestion in fast developing intelligent transportation systems (ITSs) and advanced traffic management systems (ATMSs) [ [6]. Accurate and timely traffic flow information is crucial for traffic regulation and vehicular navigation.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Cascading Extremely Randomized Trees Framework for Short-Term Traffic Flow Prediction

Zhang¹,

Bai²,

Li³

et al. 2019

KSII TIIS

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Short-term traffic flow prediction plays an important role in intelligent transportation systems (ITS) in areas such as transportation management, traffic control and guidance. For short-term traffic flow regression predictions, the main challenge stems from the non-stationary property of traffic flow data. In this paper, we design an ensemble cascading prediction framework based on extremely randomized trees (extra-trees) using a boosting technique called EET to predict the short-term traffic flow under non-stationary environments. Extra-trees is a tree-based ensemble method. It essentially consists of strongly randomizing both the attribute and cut-point choices while splitting a tree node. This mechanism reduces the variance of the model and is, therefore, more suitable for traffic flow regression prediction in non-stationary environments. Moreover, the extra-trees algorithm uses boosting ensemble technique averaging to improve the predictive accuracy and control overfitting. To the best of our knowledge, this is the first time that extra-trees have been used as fundamental building blocks in boosting committee machines. The proposed approach involves predicting 5 min in advance using real-time traffic flow data in the context of inherently considering temporal and spatial correlations. Experiments demonstrate that the proposed method achieves higher accuracy and lower variance and computational complexity when compared to the existing methods.

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Section: Introductionmentioning

confidence: 99%

An Ensemble Cascading Extremely Randomized Trees Framework for Short-Term Traffic Flow Prediction

Zhang¹,

Bai²,

Li³

et al. 2019

KSII TIIS

View full text Add to dashboard Cite

show abstract

“…Liu and Li developed an approach for determining the integrated weights of experts based on interval-valued preference matrices [6]. Practical application of multiple attribute group decision making and fuzzy information can be found in [7][8][9][10][11][12][13][14] The above methods provide various ideas for addressing MAGDM with IVIFS information. However, different methods always yield different results for the same example.…”

Section: Introductionmentioning

confidence: 99%

Evaluation criterion for different methods of multiple-attribute group decision making with interval-valued intuitionistic fuzzy information

2018

KSII TIIS

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A number of effective methods for multiple-attribute group decision making (MAGDM) with interval-valued intuitionistic fuzzy numbers (IVIFNs) have been proposed in recent years. However, the different methods frequently yield different, even sometimes contradictory, results for the same problem. In this paper a novel criterion to determine the advantages and disadvantages of different methods is proposed. First, the decision-making process is divided into three parts: translation of experts' preferences, aggregation of experts' opinions, and comparison of the alternatives. Experts' preferences aggregation is considered the core step, and the quality of the collective matrix is considered the most important evaluation index for the aggregation methods. Then, methods to calculate the similarity measure, correlation, correlation coefficient, and energy of the intuitionistic fuzzy matrices are proposed, which are employed to evaluate the collective matrix. Thus, the optimal method can be selected by comparing the collective matrices when all the methods yield different results. Finally, a novel approach for aggregating experts' preferences with IVIFN is presented. In this approach, experts' preferences are mapped as points into two-dimensional planes, with the plant growth simulation algorithm (PGSA) being employed to calculate the optimal rally points, which are inversely mapped to IVIFNs to establish the collective matrix. In the study, four different methods are used to address one example problem to illustrate the feasibility and effectiveness of the proposed approach.

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“…In [9], authors investigate, implement, and install a traffic management/monitoring prototype for watching the work zone. In [10], a traffic congestion detection method is explored. The authors apply fuzzy logic for filtering the real time performance.…”

mentioning

confidence: 99%

Vehicle License Plate Detection and Perspective Rectification

Alhussein¹,

Aurangzeb²,

Haider³

2019

ELEKTRON ELEKTROTECH

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The character segmentation and perspective rectification of Vehicle License Plate (VLP) is essential in different applications, including traffic monitoring, car parking, stolen vehicle recovery, and toll payment. The character segmentation of the VLP and its horizontal as well as vertical (pan and tilt) correction is a crucial operation. It has considerable impact on the precision of the vehicle identification process. In this work, we investigate an effective framework for the perspective rectification and homography correction of vehicle's images. The captured images of the vehicle could be tilted in vertical or horizontal or vertical-horizontal mix directions due to different movements. For reasonable high identification results, a polynomial fitting based homography correction method for rectifying the tilted VLPs is applied. A method for determining four corner points of the rotated VPLs is explored. These four detected corner points are applied in the homography correction algorithm. For comprehensively evaluating the performance of the proposed framework, the detected VLPs in various directions, such as horizontal, vertical, and mix horizontal-vertical, are rotated. For the experiments, the real images of the vehicles in the outdoor environment, from different directions and different distances are captured. With our proposed method, we achieve an accuracy of 97 % and 95 % for the simulated and real captured images, respectively.

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A New Traffic Congestion Detection and Quantification Method Based on Comprehensive Fuzzy Assessment in VANET

Cited by 8 publications

References 19 publications

An Ensemble Cascading Extremely Randomized Trees Framework for Short-Term Traffic Flow Prediction

An Ensemble Cascading Extremely Randomized Trees Framework for Short-Term Traffic Flow Prediction

Evaluation criterion for different methods of multiple-attribute group decision making with interval-valued intuitionistic fuzzy information

Vehicle License Plate Detection and Perspective Rectification

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