Vehicular traffic noise prediction using soft computing approach

Singh, Daljeet; Nigam, S. P.; Agrawal, Vikesh; Kumar, Maneek

doi:10.1016/j.jenvman.2016.08.053

Cited by 57 publications

(25 citation statements)

References 19 publications

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“…Since most traffic noise prediction models deal with equivalent continuous sound pressure level [22], what is more, that parameter is strongly related to the changes of the environment [19] and to the objective evaluation of interior sound quality [26] [27] that was measured at each experimental combination.…”

Section: Measuring Methodsmentioning

confidence: 99%

“…Singh D. et al [22] applied four different methods to evaluate hourly traffic noise in Patiala, India: a generalized linear model and three types of soft computing methods: decision trees, random forests, and artificial neural network. 10-fold cross-validation was performed, and the prediction results were compared.…”

Section: Tactile and Kinesthetic Appreciabilitymentioning

confidence: 99%

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A Fuzzy Approach for In-Car Sound Quality Prediction

Lukács¹

2020

ACTA POLYTECH HUNG

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Numerous methods exist to characterize product quality. Nowadays, in the case of road vehicles, one of the most important issues is the acoustic comfort of the interior. However, the detection of the traffic environment is a further key question. In the case of minor vehicle collisions, the perceptibility is to analyze. Within the framework of the current study, the results of airborne noise measurements are presented. Experimental data were used to design predictive fuzzy models to estimate cabin noise level, which is in connection with the audibility of outer sourcing sounds. Two concepts of inference systems were investigated by examining accuracy, conformity and 0 residuals: Mamdani and Sugeno type ones. It was finally concluded that for estimating interior noise, Sugeno type fuzzy model is the better choice, as the accuracy and conformity are higher. In addition, the range of residuals is a magnitude lower: Mamdani type FIS provided-2.30 ~ 2.30 dB (-3.84 ~ 3.30%), Sugeno type one resulted-0.40 ~ 0.20 dB (-0.57 ~ 0.33%). Furthermore, the residuals follow a Gaussian distribution, in the case of the Sugeno predictive fuzzy model.

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Section: Measuring Methodsmentioning

confidence: 99%

Section: Tactile and Kinesthetic Appreciabilitymentioning

confidence: 99%

A Fuzzy Approach for In-Car Sound Quality Prediction

Lukács¹

2020

ACTA POLYTECH HUNG

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“…DT is considered as the optimizing point in the model because it reduces the searching time. Furthermore, Singh et al [47] presented a new model to predict the noise of vehicular traffic. Four methods were used to build the model: DT, neural network, random forest, and the generalized linear model.…”

Section: Related Workmentioning

confidence: 99%

“…where z can be considered as either the path maximum transmission unit (PMTU) of the destination or previous outstanding data chunk(s) acknowledged (data ACKed). PMTU is the destination PMTU and ACKed is the total size of chunks of the previous outstanding acknowledged data [45][46][47].…”

Section: Enhanced Slow-startmentioning

confidence: 99%

Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment

et al. 2019

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The 5G network is a next-generation wireless form of communication and the latest mobile technology. In practice, 5G utilizes the Internet of Things (IoT) to work in high-traffic networks with multiple nodes/sensors in an attempt to transmit their packets to a destination simultaneously, which is a characteristic of IoT applications. Due to this, 5G offers vast bandwidth, low delay, and extremely high data transfer speed. Thus, 5G presents opportunities and motivations for utilizing next-generation protocols, especially the stream control transmission protocol (SCTP). However, the congestion control mechanisms of the conventional SCTP negatively influence overall performance. Moreover, existing mechanisms contribute to reduce 5G and IoT performance. Thus, a new machine learning model based on a decision tree (DT) algorithm is proposed in this study to predict optimal enhancement of congestion control in the wireless sensors of 5G IoT networks. The model was implemented on a training dataset to determine the optimal parametric setting in a 5G environment. The dataset was used to train the machine learning model and enable the prediction of optimal alternatives that can enhance the performance of the congestion control approach. The DT approach can be used for other functions, especially prediction and classification. DT algorithms provide graphs that can be used by any user to understand the prediction approach. The DT C4.5 provided promising results, with more than 92% precision and recall.

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“…Singh et al [22] developed models to predict the equivalent sound level, L eq , based on soft computing methods, namely, Generalized Linear Model, Decision Trees, Random Forests and Neural Networks. The input variables include the traffic volume per hour, percentage of heavy vehicles and average speed of vehicles.…”

Section: Literature Reviewmentioning

confidence: 99%

Traffic noise and pavement distresses: Modelling and assessment of input parameters influence through data mining techniques

et al. 2018

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A B S T R A C TTraffic noise affects greatly health and well-being of people, consequently the knowledge and control of the factors affecting it is very important. In this study models to predict tyre-pavement noise acoustic and psychoacoustic indicators based on type of pavement, texture, pavement distresses and speed were developed and used to assess the importance of each factor. By applying data mining techniques, in particular artificial neural networks and support vector machines, models with good predictive capacity of both acoustic and psychoacoustic noise indicators were obtained, constituting a precious tool to reduce the tyre-pavement noise. Moreover, the proposed models allowed for the assessment of the influence of the input parameters controlling noise such as: type of pavement, texture, speed and pavement distresses for the first time. It was found that pavement distresses and, as expected, speed influence strongly tyre-pavement noise. In this way it is clearly shown that preventive maintenance of road pavements by authorities, which eliminates distresses, can have an important effect on tyre-road noise, promoting the well-being of the populations.

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Vehicular traffic noise prediction using soft computing approach

Cited by 57 publications

References 19 publications

A Fuzzy Approach for In-Car Sound Quality Prediction

A Fuzzy Approach for In-Car Sound Quality Prediction

Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment

Traffic noise and pavement distresses: Modelling and assessment of input parameters influence through data mining techniques

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