Modeling roadway traffic noise in a hot climate using artificial neural networks

Hamad, Khaled; Khalil, Mohamad Ali; Shanableh, Abdallah

doi:10.1016/j.trd.2017.04.014

Cited by 92 publications

(39 citation statements)

References 20 publications

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“…Hamad et al [23] employed artificial neural network technique to model L eq in a city with known hot climate, namely Sharjah City in United Arab Emirates. They used the following inputs: Distance from the edge of the road in meters, hourly light-vehicle volume, hourly heavy-vehicle volume, average speed in km/h and roadway temperature.…”

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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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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“…Almost all these models are designed by using experimental samples; consequently, these models are highly influenced by the traffic flow condition and the measurement style and the geographic locations [14]. The main drawback of these models is that they can not be generalized because of the local environment like vehicle model and type and the weather [15,16]. Ref.…”

Section: Introductionmentioning

confidence: 99%

Modeling of CO Emissions from Traffic Vehicles Using Artificial Neural Networks

et al. 2019

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Traffic emissions are considered one of the leading causes of environmental impact in megacities and their dangerous effects on human health. This paper presents a hybrid model based on data mining and GIS models designed to predict vehicular Carbon Monoxide (CO) emitted from traffic on the New Klang Valley Expressway, Malaysia. The hybrid model was developed based on the integration of GIS and the optimized Artificial Neural Network algorithm that combined with the Correlation based Feature Selection (CFS) algorithm to predict the daily vehicular CO emissions and generate prediction maps at a microscale level in a small urban area by using a field survey and open source data, which are the main contributions to this paper. The other contribution is related to the case study, which represents the spatial and quantitative variations in the vehicular CO emissions between toll plaza areas and road networks. The proposed hybrid model consists of three steps: the first step is the implementation of the correlation-based Feature Selection model to select the best model’s predictors; the second step is the prediction of vehicular CO by using a multilayer perceptron neural network model; and the third step is the creation of micro scale prediction maps. The model was developed using six traffic CO predictors: number of vehicles, number of heavy vehicles, number of motorbikes, temperature, wind speed and a digital surface model. The network architecture and its hyperparameters were optimized through a grid search approach. The traffic CO concentrations were observed at 15-min intervals on weekends and weekdays, four times per day. The results showed that the developed model had achieved validation accuracy of 80.6 %. Overall, the developed models are found to be promising tools for vehicular CO simulations in highly congested areas.

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“…This main disadvantage of road traffic emission prediction models limits their use universally [14]. The models fail to generalise due to local conditions such as vehicle type and weather conditions [15].…”

Section: Introductionmentioning

confidence: 99%

Vehicular CO Emission Prediction Using Support Vector Regression Model and GIS

2018

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Transportation infrastructures play a significant role in the economy as they provide accessibility services to people. Infrastructures such as highways, road networks, and toll plazas are rapidly growing based on changes in transportation modes, which consequently create congestions near toll plaza areas and intersections. These congestions exert negative impacts on human health and the environment because vehicular emissions are considered as the main source of air pollution in urban areas and can cause respiratory and cardiovascular diseases and cancer. In this study, we developed a hybrid model based on the integration of three models, correlation-based feature selection (CFS), support vector regression (SVR), and GIS, to predict vehicular emissions at specific times and locations on roads at microscale levels in an urban areas of Kuala Lumpur, Malaysia. The proposed model comprises three simulation steps: first, the selection of the best predictors based on CFS; second, the prediction of vehicular carbon monoxide (CO) emissions using SVR; and third, the spatial simulation based on maps by using GIS. The proposed model was developed with seven road traffic CO predictors selected via CFS (sum of vehicles, sum of heavy vehicles, heavy vehicle ratio, sum of motorbikes, temperature, wind speed, and elevation). Spatial prediction was conducted based on GIS modelling. The vehicular CO emissions were measured continuously at 15 min intervals (recording 15 min averages) during weekends and weekdays twice per day (daytime, evening-time). The model's results achieved a validation accuracy of 80.6%, correlation coefficient of 0.9734, mean absolute error of 1.3172 ppm and root mean square error of 2.156 ppm. In addition, the most appropriate parameters of the prediction model were selected based on the CFS model. Overall, the proposed model is a promising tool for traffic CO assessment on roads.

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Modeling roadway traffic noise in a hot climate using artificial neural networks

Cited by 92 publications

References 20 publications

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

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

Modeling of CO Emissions from Traffic Vehicles Using Artificial Neural Networks

Vehicular CO Emission Prediction Using Support Vector Regression Model and GIS

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