Traffic flow breakdown prediction using feature reduction through Rough-Neuro Fuzzy Networks

Affonso, Carlos; Sassi, Renato José; Ferreira, Ricardo

doi:10.1109/ijcnn.2011.6033462

Cited by 9 publications

(5 citation statements)

References 16 publications

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“…Even well-designed intelligent electronic traffic lights cannot play the correct role at intersections during rush hour, when there are many vehicles, or when traveling via roads suspended due to severe calamities or construction. In this research, traffic data are processed by employing the method of determining the ideal signal period for junctions based on traffic information and telematics observed via a loop detector [4,5]. As an example, we can trace terrible road conditions by ice, snow, and rain on the road.…”

Section: Introductionmentioning

confidence: 99%

A Proposed Waiting Time Algorithm for a Prediction and Prevention System of Traffic Accidents Using Smart Sensors

Jha

Ahmad²,

Prashar³

et al. 2022

Electronics

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One of the applications of neural networks is to predict the fault section results of traffic utilizing the combined model estimation of the fault section and self-learning models with smart sensors. The prediction of the fault section can autonomously develop the internal model of the network to fit the pre-entered “traffic accident” section data and predict the occurrence of traffic accident sections. In this paper, we propose the results of waiting time for traffic accidents in case of traffic accidents by using a neural network and fuzzy expert system, in comparison with existing algorithms and algorithms for determining traffic accidents. It is used to estimate or predict traffic accident reliability as well. Typically, the type of fault data collected is the number of faults (the number of faults recorded during a given time interval) or the time of fault (the time-of-fault data recorded when each fault occurred), and this can be utilized only for group data types, rather than the time-of-fault data type.

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Section: Introductionmentioning

confidence: 99%

A Proposed Waiting Time Algorithm for a Prediction and Prevention System of Traffic Accidents Using Smart Sensors

Jha

Ahmad²,

Prashar³

et al. 2022

Electronics

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“…In particular, the proposed method utilizes Rough-Fuzzy Sets to define inference morphology for insertion of the behavior of Dynamic Routing in a structured rule basis. In this work, rough sets theory can distinguish the weight of attribute, and then choose which fuzzy relation to be added to the Rough Neuro Fuzzy Network type Multilayer Perceptron and type Radial Basis Function [13].…”

Section: Related Workmentioning

confidence: 99%

City traffic flow breakdown prediction based on fuzzy rough set

Yang

et al. 2017

Open Physics

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Abstract:In city traffic management, traffic breakdown is a very important issue, which is defined as a speed drop of a certain amount within a dense traffic situation. In order to predict city traffic flow breakdown accurately, in this paper, we propose a novel city traffic flow breakdown prediction algorithm based on fuzzy rough set. Firstly, we illustrate the city traffic flow breakdown problem, in which three definitions are given, that is, 1) Pre-breakdown flow rate, 2) Rate, density, and speed of the traffic flow breakdown, and 3) Duration of the traffic flow breakdown. Moreover, we define a hazard function to represent the probability of the breakdown ending at a given time point. Secondly, as there are many redundant and irrelevant attributes in city flow breakdown prediction, we propose an attribute reduction algorithm using the fuzzy rough set. Thirdly, we discuss how to predict the city traffic flow breakdown based on attribute reduction and SVM classifier. Finally, experiments are conducted by collecting data from I-405 Freeway, which is located at Irvine, California. Experimental results demonstrate that the proposed algorithm is able to achieve lower average error rate of city traffic flow breakdown prediction.

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“…Yang [40] applied the p-test score to conduct the feature ranking and wrapper-like scheme to select the optimal number of features, so as to predict the traffic congestion with even better performance under remarkably reduced data dimensionality. Affonso et al [41] employed RS to identify the important attributes for a rough neuro-fuzzy networks, and obtained the good performance of predicting traffic flow breakdown. Moreover, Vlahogianni and Karlaftis [42] applied a fuzzy entropy FS to determine redundant factors and rank factor importance with respect to their contribution to the predictability of incident duration.…”

Section: Introductionmentioning

confidence: 99%