scholarly journals The use of Artificial Neural Networks for extending road traffic monitoring data spatially: an application to the neighbourhoods of Benevento

2020 ◽  
Vol 45 ◽  
pp. 635-642 ◽  
Author(s):  
Giuseppina De Luca ◽  
Mariano Gallo
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Road Traffic ◽  
Traffic Monitoring ◽  
Monitoring Data ◽  
Artificial Neural

COMBINATION OF ARTIFICIAL NEURAL NETWORKS AND GENETIC ALGORITHM - GAMMA TEST METHOD IN PREDICTION OF ROAD TRAFFIC NOISE

2015 ◽  
Vol 14 (4) ◽  
pp. 801-808 ◽  
Author(s):  
Leila Khouban ◽  
Abbas Ali Ghaiyoomi ◽  
Mohammad Teshnehlab ◽  
Abbas Tolooei Ashlaghi ◽  
Majid Abbaspour ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Road Traffic ◽  
Traffic Noise ◽  
Test Method ◽  
Road Traffic Noise ◽  
Gamma Test ◽  
Artificial Neural

scholarly journals Application of Artificial Neural Networks for Noise Barrier Optimization

Environments ◽  
2018 ◽  
Vol 5 (12) ◽  
pp. 135 ◽  
Author(s):  
Paulo Henrique Trombetta Zannin ◽  
Eriberto Oliveira do Nascimento ◽  
Elaine Carvalho da Paz ◽  
Felipe do Valle
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Environmental Impact ◽  
Barrier Height ◽  
Road Traffic ◽  
Noise Pollution ◽  
Modern World ◽  
Noise Barriers ◽  
Negative Effect ◽  
Artificial Neural

In the modern world, noise pollution continues to be a major problem that impairs people’s health, and road traffic is a primary contributor to noise emissions. This article describes an environmental impact study of the noise generated by the reconstruction of an urban section of a highway. Noise maps were calculated, and an environmental impact matrix was generated to determine the environmental impact of this reconstruction. The implementation of noise barriers was simulated based on these noise maps, and the effectiveness of the barriers was evaluated using Artificial Neural Networks (ANNs) combined with Design of Experiments (DoE). A functional variable significance analysis was then made for two parameters, namely, the coefficient of absorption of the barrier material and the barrier height. The aim was to determine the influence of these parameters on sound attenuation and on the formation of acoustic shadows. The results obtained from the ANNs and DoE were consistent in demonstrating that the absorption coefficient strongly influences the noise attenuation provided by noise barriers, while barrier height is correlated with the formation of larger areas of acoustic shadow. The environmental impact matrix also indicates that the existence of noise pollution has a negative effect on the environment, but that this impact can be reversed or minimized. The application of simulated noise barriers demonstrated that noise levels can be reduced to legally acceptable levels.

scholarly journals Spatial Extension of Road Traffic Sensor Data with Artificial Neural Networks

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2640 ◽  
Author(s):  
Mariano Gallo ◽  
Giuseppina De Luca
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Travel Demand ◽  
Road Traffic ◽  
Single Layer ◽  
Sensor Data ◽  
Traffic Flows ◽  
Simulation Techniques ◽  
Artificial Neural ◽  
Demand Patterns

This paper proposes a method for estimating traffic flows on some links of a road network knowing the data on other links that are monitored with sensors. In this way, it is possible to obtain more information on traffic conditions without increasing the number of monitored links. The proposed method is based on artificial neural networks (ANNs), wherein the input data are the traffic flows on some monitored road links and the output data are the traffic flows on some unmonitored links. We have implemented and tested several single-layer feed-forward ANNs that differ in the number of neurons and the method of generating datasets for training. The proposed ANNs were trained with a supervised learning approach where input and output example datasets were generated through traffic simulation techniques. The proposed method was tested on a real-scale network and gave very good results if the travel demand patterns were known and used for generating example datasets, and promising results if the demand patterns were not considered in the procedure. Numerical results have underlined that the ANNs with few neurons were more effective than the ones with many neurons in this specific problem.

Sign in / Sign up

Export Citation Format

Share Document