Toward improving road traffic noise characterization: A reduced-order model for representing traffic flow dynamics

2021 ◽  
Vol 150 (4) ◽  
pp. A113-A114
Author(s):  
Mylan R. Cook ◽  
Kent L. Gee ◽  
Mark K. Transtrum ◽  
Shane V. Lympany ◽  
Matt Calton
Keyword(s):  
Traffic Flow ◽  
Road Traffic ◽  
Traffic Noise ◽  
Flow Dynamics ◽  
Reduced Order Model ◽  
Road Traffic Noise ◽  
Order Model ◽  
Reduced Order ◽  
Noise Characterization

Studies on Road Traffic Noise Based on Models of Traffic Flow

1977 ◽  
Vol 10 (7) ◽  
pp. 485-489
Author(s):  
Koichi Takagi ◽  
Kozo Hiramatsu ◽  
Takeo Yamamoto ◽  
Kazuhei Hashimoto
Keyword(s):  
Traffic Flow ◽  
Road Traffic ◽  
Traffic Noise ◽  
Road Traffic Noise

scholarly journals Road Traffic Noise on the Santa Marta City Tourist Route

2021 ◽  
Vol 11 (16) ◽  
pp. 7196
Author(s):  
Dámaris A. Jiménez-Uribe ◽  
Darwin Daniels ◽  
Zoë L. Fleming ◽  
Andrés M. Vélez-Pereira
Keyword(s):  
Traffic Flow ◽  
Noise Level ◽  
Road Traffic ◽  
Traffic Noise ◽  
Environmental Noise ◽  
Time Of Day ◽  
Road Traffic Noise ◽  
Sampling Point ◽  
Heavy Vehicles ◽  
Noise Levels

The objective of this study was to determine the influence of vehicular traffic on the environmental noise levels of the Santa Marta City tourist route on the Colombian coast. An analysis of vehicle types and frequencies at various times of the day over nearly a year helped to track the main sources of environmental noise pollution. Five sampling points were selected, which were distributed over 12 km, with three classified as peripheral urban and two as suburban. The average traffic flow was 966 vehicles/h and was mainly composed of automobiles, with higher values in the peripheral urban area. The noise level was 103.3 dBA, with background and peak levels of 87.2 and 107.3 dBA, respectively. The noise level was higher during the day; however, there were no differences between weekdays and weekends. The results from the analysis of variance showed that the number of vehicles and the noise levels varied greatly according to the time of day and sampling point location. The peak and mean noise levels were correlated with the number of automobiles, buses and heavy vehicles. The mean noise levels were similar at all sample points despite the traffic flow varying, and the background noise was only correlated for automobiles (which varied much more than the heavy vehicles between day and night).

scholarly journals Theoretical Comparison of the Effects of Different Traffic Conditions on Urban Road Traffic Noise

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Mohammad Maghrour Zefreh ◽  
Adam Torok
Keyword(s):  
Traffic Flow ◽  
Noise Level ◽  
Urban Areas ◽  
Road Traffic ◽  
Traffic Noise ◽  
Urban Traffic ◽  
Road Traffic Noise ◽  
Traffic Lights ◽  
Urban Road ◽  
Traffic Conditions

Road traffic noise is one of the most relevant sources in the environmental noise pollution of the urban areas where dynamics of the traffic flow are much more complicated than uninterrupted traffic flows. It is evident that different traffic conditions would play the role in the urban traffic flow considering the dynamic nature of the traffic flow on one hand and presence of traffic lights, roundabouts, etc. on the other hand. The main aim of the current paper is to investigate the effect of different traffic conditions on urban road traffic noise. To do so, different traffic conditions have been theoretically generated by the Monte Carlo Simulation technique following the distribution of traffic speed in the urban roads. The “ASJ RTN-Model” has been considered as a base road traffic noise prediction model which would deal with different traffic conditions including steady and nonsteady traffic flow that would cover the urban traffic flow conditions properly. Having generated the vehicles speeds in different traffic conditions, the emitted noise (LWA) and subsequently the noise level at receiver (LA) were estimated by “ASJ RTN-Model.” Having estimated LWA and LA for each and every vehicle in each traffic condition and taking the concept of transient noise into account, the single event sound exposure levels (SEL) in different traffic conditions are calculated and compared to each other. The results showed that decelerated traffic flow had the lowest contribution, compared to congestion, accelerated flow, free flow, oversaturated congestion, and undersaturated flow by 16%, 14%, 12%, 12%, and 10%, respectively. Moreover, the distribution of emitted noise and noise level at receiver were compared in different traffic conditions. The results showed that traffic congestion had considerably the maximum peak compared to other traffic conditions which would highlight the importance of the range of generated noise in different traffic conditions.

scholarly journals Application of the Intermittency Ratio Metric for the Classification of Urban Sites Based on Road Traffic Noise Events

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5136 ◽  
Author(s):  
Giovanni Brambilla ◽  
Chiara Confalonieri ◽  
Roberto Benocci
Keyword(s):  
Traffic Flow ◽  
Noise Exposure ◽  
Road Traffic ◽  
Energy Content ◽  
Traffic Noise ◽  
Road Traffic Noise ◽  
Clustering Methods ◽  
Hourly Data ◽  
Noise Data ◽  
Urban Sites

Human hearing adapts to steady signals, but remains very sensitive to fluctuations as well as to prominent, salient noise events. The higher these fluctuations are, the more annoying a sound is possibly perceived. To quantify these fluctuations, descriptors have been proposed in the literature and, among these, the intermittency ratio (IR) has been formulated to quantify the eventfulness of an exposure from transportation noise. This paper deals with the application of IR to urban road traffic noise data, collected in terms of 1 s A-weighted sound pressure level (SPL), without being attended, monitored continuously for 24 h in 90 sites in the city of Milan. IR was computed on each hourly data of the 251 time series available (lasting 24 h each), including different types of roads, from motorways to local roads with low traffic flow. The obtained hourly IR values have been processed by clustering methods to extract the most significant temporal pattern features of IR in order to figure out a criterion to classify the urban sites taking into account road traffic noise events, which potentially increase annoyance. Two clusters have been obtained and a “non-acoustic” parameter x, determined by combination of the traffic flow rate in three hourly intervals, has allowed to associate each site with the cluster membership. The described methodology could be fruitfully applied on road traffic noise data in other cities. Moreover, to have a more detailed characterization of noise exposure, IR, describing SPL short-term temporal variations, has proved to be a useful supplementary metric accompanying LAeq, which is limited to measure the energy content of the noise exposure.

scholarly journals Classification of Urban Road Traffic Noise based on Sound Energy and Eventfulness Indicators

2020 ◽  
Vol 10 (7) ◽  
pp. 2451 ◽  
Author(s):  
Giovanni Brambilla ◽  
Roberto Benocci ◽  
Chiara Confalonieri ◽  
Hector Eduardo Roman ◽  
Giovanni Zambon
Keyword(s):  
Traffic Flow ◽  
Urban Areas ◽  
Road Traffic ◽  
Traffic Noise ◽  
Road Traffic Noise ◽  
Clustering Methods ◽  
Cluster Membership ◽  
Urban Road ◽  
Local Roads ◽  
Noise Data

Noise energetic indicators, like Lden, show good correlations with long term annoyance, but should be supplemented by other parameters describing the sound fluctuations, which are very common in urban areas and negatively impact noise annoyance. Thus, in this paper, the hourly values of continuous equivalent level LAeqh and the intermittency ratio (IR) were both considered to describe the urban road traffic noise, monitored in 90 sites in the city of Milan and covering different types of road, from motorways to local roads. The noise data have been processed by clustering methods to detect similarities and to figure out a criterion to classify the urban sites taking into account both equivalent noise levels and road traffic noise events. Two clusters were obtained and, considering the cluster membership of each site, the decimal logarithm of the day-time (06:00–22:00) traffic flow was used to associate each new road with the clusters. In particular, roads with average day-time hourly traffic flow ≥1900 vehicles/hour were associated with the cluster with high traffic flow. The described methodology could be fruitfully applied on road traffic noise data in other cities.

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