Improved AADT Estimation for Local Roads Using Parcel-level Modeling - Wang, Tao
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Annual Average Daily Traffic (AADT) is a critical input to many transportation analyses. Traditionally, AADT is estimated using a mix of permanent and temporary traffic counts. Because field collection of traffic counts is expensive, it is usually done for only the major roads, thus leaving most of the local roads without any AADT information. However, AADTs are needed for local roads for many applications. A new method for estimating AADTs for local roads using travel demand modeling is developed in this research.

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Produktbeschreibung
Annual Average Daily Traffic (AADT) is a critical input to many transportation analyses. Traditionally, AADT is estimated using a mix of permanent and temporary traffic counts. Because field collection of traffic counts is expensive, it is usually done for only the major roads, thus leaving most of the local roads without any AADT information. However, AADTs are needed for local roads for many applications. A new method for estimating AADTs for local roads using travel demand modeling is developed in this research.
Autorenporträt
Dr. Tao Wang is a Research Associate at the Lehman Center for Transportation Research of Florida International University in the United States. The subjects he researches include travel demand modeling and intelligent transportation systems (ITS). He is also an expert in software development.