The accuracy of traffic noise predictions obtained using the TRRL computer model of traffic noise depends to a considerable extent on the degree of simplification adopted in categorizing vehicles according to their sound output and speed in the traffic stream. This report examines and summarizes the available data on the acoustic classification of vehicles in traffic streams for predicting traffic noise. Measurements of speed, noise level and vehicle type have been made in road conditions ranging from fairly congested urban situations with speeds around 20 km/h to free flow on motorways with speeds over 100 km/h. At speeds greater than 50 km/h vehicle noise increases with speed at approximately 9 dB(A) per doubling of speed and there is a clear distinction between the noise from heavy vehicles, i.e. those exceeding 1525 kg unladen weight, and light vehicles. At low speeds, below 30 km/h, vehicle noise levels are independent of vehicle speed. The vehicle population can be conveniently divided into three distinct groups; light vehicles less than 3000 kg unladen weight, medium heavy commercial vehicles with 2 axles including buses and coaches and heavy vehicles which include all commercial vehicles with 3 or more axles. The measurements have been used to construct approximate vehicle noise levels and speed characteristics over the speed range 20-100 km/h for up to 6 vehicle categories, and used as input in the TRRL computer model of traffic noise.
Classifying Road Vehicles for the Prediction of Road Traffic Noise
1977
26 pages
Report
Keine Angabe
Englisch
Noise Pollution & Control , Road Transportation , Transportation & Traffic Planning , Transportation noise , Highway transportation , Vehicles , Predictions , Mathematical models , Acoustic measurement , Vehicular traffic , Sound pressure , Ratings , Acceleration , Urban transportation , Great Britain
Road shoulder-based road traffic noise source intensity prediction method
Europäisches Patentamt | 2021
|Kraftfahrwesen | 1987
|New Perspectives in Road Traffic Noise Prediction
British Library Conference Proceedings | 2012
|