2010
DOI: 10.1250/ast.31.87
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Methods for predicting noise in the vicinity of signalized intersections

Abstract: To accurately estimate the noise at a signalized intersection, it is necessary to precisely reproduce the traffic volume, signal cycle and traffic noise for each vehicle behavior and driving state. Precise reproduction requires considerable effort, such as continuous calculations of vehicles and the setting of parameters such as engine speed, engine load and velocity. A simple method that involves using A-weighted sound power levels (L WA) under nonsteady running conditions has already been proposed for estima… Show more

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Cited by 6 publications
(8 citation statements)
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“…A method of calculating L Aeq separately for vehicles that pass a green signal under a steady condition and for vehicles that decelerate, stop and accelerate when the signal is red under a transient running condition [46].…”
Section: Calculation Methods Outlinementioning
confidence: 99%
See 3 more Smart Citations
“…A method of calculating L Aeq separately for vehicles that pass a green signal under a steady condition and for vehicles that decelerate, stop and accelerate when the signal is red under a transient running condition [46].…”
Section: Calculation Methods Outlinementioning
confidence: 99%
“…A simple method of calculating L Aeq by setting L WA separately for a steady-state flow section and a compound section with steadystate and accelerating flow sections [46].…”
Section: Simplified Methodsmentioning
confidence: 99%
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“…In this appendix, a semi-precise method and its simplified method are described. [74] For each signal cycle, noise is separately calculated for vehicles passing a green signal and for vehicles decelerating, stopping and accelerating at a red signal for each lane. In the calculation, the mean A-weighted sound power level " L L WA [dB] is applied as the power level of running vehicles, which is determined by taking account of the component ratios of different vehicle types.…”
Section: Appendix A3 Signalized Intersectionsmentioning
confidence: 99%