Developing a method for estimating AADT on all Louisiana roads.

Details

• Creators:
  Sun, Xiaoduan ; Das, Subasish
• Corporate Creators:
  University of Louisiana at Lafayette. Department of Civil and Environmental Engineering
• Corporate Contributors:
  United States. Federal Highway Administration
• Subject/TRT Terms:
  Annual Average Daily Traffic Pattern Recognition Systems Regression Analysis Rural Highways Sensitivity Analysis Traffic Counts Traffic Estimation Traffic Flow Traffic Volume
• Publication/ Report Number:
  FHWA/LA.14/548
• Resource Type:
  Tech Report
• Geographical Coverage:
  Louisiana
• Corporate Publisher:
  Louisiana Transportation Research Center
• Abstract:
  Traffic flow volumes present key information needed for making transportation engineering and planning decisions.
  
  Accurate traffic volume count has many applications including: roadway planning, design, air quality compliance, travel
  
  model validation, and administrative purposes. Traffic counts also serve as an important input in highway safety
  
  performance evaluation. However, collecting traffic volume on all rural non-state roads has been very limited for various
  
  reasons, although these roads constitute a great portion (60 to 70%) of road mileage in the roadway network of any state in
  
  the U.S. For example, out of 61,335 miles of roadway in Louisiana, 73% of the roadways are non-state roads. Due to
  
  limited resources, traffic volume information on non-state roadways has not been systematically collected in Louisiana.
  
  Generally, traffic volumes on these roads are fairly low, and VMT on these roads is much less compared with that on
  
  interstate or arterial roads. Thus, regularly conducting traffic count is not economically feasible for non-state roadways. This
  
  study develops an AADT estimation methodology by using modern statistical and pattern recognition methods. By using
  
  available traffic counts on non-state roadway and four variables (namely: population, job, and distance to intersection and to
  
  major state highways at block level), a training set to estimate roadway AADT for eight parishes were obtained by a
  
  modified support vector regression (SVR) method. This pattern recognition method yields better AADT estimates than the
  
  conventional parametric statistical methods. Sensitivity analyses were also conducted in this study, which indicates a parish-specific model works better than an aggregated single model.
  
  With the estimated AADT, the DOTD and local government agencies can make better decisions on funding allocations for
  
  safety improvement projects and pavement maintenance actions. The estimated parish-specific AADT on non-state roads
  
  can also improve statewide travel demand forecasting models and air quality assessment.
  
  
• Format:
  PDF
• Funding:
  LTRC No. 14-3SA;SIO No. 30001700
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:610150ab82e78e0b60f7e3dc2cc3171f80dad37531f3ffaa47d8008fbeb6b129e1f2f6308022fd1ac53de31db38b6b3a2c0304dd0df194a1d4288a212fbee0d2
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/29681/dot_29681_DS1.pdf
• File Type:
  [PDF - 3.80 MB ]