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Spatial Mapping of Traffic Noise Levels in Urban Areas

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  • Obaidat, Mohammed T.

Abstract

This paper combines field data with an analytical approach to spatially map noise levels due to traffic movements at relatively high traffic volume signalized intersections utilizing the potential of Geographic Information Systems (GIS). Noise data were collected using a discrete mapping technique at 29 signalized intersections, as well as between intersections, and at their respective neighborhood areas in Amman, capital of Jordan. Data were collected in three different highly congested traffic peak periods: 7:30 a.m.-9:00 a.m., 1:30 p.m.-3:00 p.m., and 9:00 p.m.-11:00 p.m. A portable precision sound level meter capable of measuring noise levels from 34 to 134 decibels (dB) was used during the data collection process. The highest recorded noise level at some signals was 80 dB, while the lowest was 34 dB. In fact, some signalized intersections showed higher noise levels than the acceptable or the standard ones, i.e., 65 dB for daytime and 55 dB for nighttime in residential areas at city center. Two-dimensional (2D) vector and raster maps of noise levels, at different time periods for signals’ areas and neighborhoods, were spatially displayed. Results showed that the developed GIS maps could be useful for city planning and other environmental management applications for the purpose of: 1) temporal monitoring and queries of noise level changes as a function of time, 2) spatial queries to find the highest noise disturbance location and its time of the day, 3) development of an online noise information system, 4) using noise level based spatial maps as indicators of variation in land prices, and 5) forecasting and current assessment of the acoustic climate of urban areas.

Suggested Citation

  • Obaidat, Mohammed T., 2008. "Spatial Mapping of Traffic Noise Levels in Urban Areas," Journal of the Transportation Research Forum, Transportation Research Forum, vol. 47(2).
  • Handle: RePEc:ags:ndjtrf:206913
    DOI: 10.22004/ag.econ.206913
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    Keywords

    Public Economics;

    Statistics

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