Effects of Heavy Truck Volumes on Noise
Community & Cultural Concerns, Environmental Process
Research Idea Scope
Among the many types of on-road vehicles traveling the American highway system, heavy trucks are the loudest frequently-heard noise sources. Comments from residents near highways at project public hearings and informational meetings point to heavy trucks as the primary sources of annoyance with highway traffic noise. Trucks are known to cause sleep interference, multiple reflections that can degrade noise wall effectiveness, and reductions in quieter pavement effectiveness. Another possible effect is that high truck volumes in the near lanes provide noise shielding to vehicles in the far lanes.
Sleep disruption is commonly mentioned, often in connection with engine-compression brakes and with defective mufflers. It is the particularly loud truck events that awaken residents adjacent to highways. Another effect mentioned is that heavy truck traffic on highways commonly increases significantly between 5:00 AM and 7:00 AM, when many residents would prefer to be sleeping. People are in lighter stages of sleep in these early morning hours, and are more vulnerable to awakening by loud startling events. Recent research on sleep disturbance by aircraft noise has been published, but little research has been conducted on the effects of highway heavy truck volumes on sleep.
Most tractor-trailer trucks have large trailers with tall, sound-reflective side panels. Research is needed to address the degree to which multiple reflections of traffic noise may occur between these trailers and nearby noise barriers. Such reflections could negatively affect the performance of reflective noise barriers, and given that heavy trucks are the noisiest vehicles, increase the noise levels of heavy trucks relative to automobiles, exacerbating the apparent noisiness of trucks in adjacent communities. Most noise barrier performance studies to date have addressed mixed classes of vehicles, not individual vehicles. A modest noise measurement program could determine if sound-absorptive materials on barriers adjacent to highways with high heavy truck volumes would provide significantly greater reduction of heavy truck noise and thereby noticeably reduce the apparent noisiness of trucks.
States across the United States (Washington, California, Arizona, Texas, Florida, Alabama, Georgia and Florida) are currently utilizing quieter pavement such as open-graded friction course (OGFC). The effectiveness of OGFC equates to approximately a 3- to 5-decibel reduction in noise levels. The noise reduction of OGFC is a result of the surface texture, which includes air voids that absorb some sound generated by the tire/pavement interaction. High volumes of heavy trucks may degrade the noise-reduction effectiveness of OGFC and may affect the lifecycle of this pavement type.
Some noise analysts have suggested that high volumes of heavy trucks in the near lanes of traffic may provide significant shielding of noise from the far lanes of traffic. No studies have attempted to quantify this effect, and the current noise prediction models do not account for it.
Research into the different phenomena discussed in the problem statement should consist of literature review and analysis of existing noise measurement data, and where those are insufficient or inconclusive, collection and analysis of additional noise data.
It is expected that the research into the potential for sleep disruption could be partially based on the abundant literature on the sleep effects from aircraft noise events. Noise levels and effects from particularly loud heavy truck events will be needed. These data possibly could be obtained from existing data sets, but additional data collection may be needed. The research should endeavor to identify characteristics of roadways or other variables that influence the proportion of particularly loud events in a traffic stream. The research will evaluate the correlation of truck noise events to sleep disturbance metrics (i.e., probability of awakening). The effects of these events on the overall Leq values also should be quantified.
Research into reflections from flat-panel trucks should include an initial literature search to identify any useful data that may exist that could inform additional research. A noise measurement program should be undertaken to determine if sound-absorptive materials on barriers adjacent to highways with high heavy truck volumes would provide significantly greater reduction of heavy truck noise and thereby noticeably reduce the apparent noisiness of trucks.
A few longitudinal studies of the changes in noise levels from OGFC pavements over time have been and are being conducted in the U.S. and Europe. Data from these studies could be mined and analysis performed to determine whether volumes of heavy truck traffic on the affected roadways correlates with the rate of deterioration of the pavements’ noise-reduction effectiveness. No measurement program is suggested for this component of the research.
A modest noise measurement program and analysis could be developed to determine the significance of shielding of far-lane traffic noise sources by high volumes of heavy trucks. The final result would be guidance on the degree of over-prediction that may be present in TNM as a function of heavy truck volume and percentage in the near lane.
Urgency and Payoff
State noise specialists have little information about some of the effects of heavy truck volumes that are often mentioned by residents in public hearings and workshops. Additional information about these effects could provide state officials with justification for the use of measures to better address public concerns about noise, such as absorptive materials on noise barriers and signs discouraging the use of engine-compression brakes. Based on the findings of the OGFC pavement wear evaluation, State officials may better judge the costs and benefits of using OGFC on major truck routes. Noise analysts will better understand the noise levels predicted by TNM for roadways with high truck volumes, and may adjust computed noise levels as appropriate, based on this research.
Data from this research would be useful in development and refinement of reflection calculation algorithms in the FHWA TNM prediction model.
The results from this study will provide insight to those dealing with the public into the factors and underlying conditions that influence public reaction and attitudes.
Paul Kohler, Virginia Department of Transportation