Incorporating image -based data in AADT estimation: Methodology and numerical investigation of increased accuracy

2005 2005

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Abstract (summary)

Annual Average Daily Traffic (AADT) is one of the most fundamental traffic statistics used for highway planning, design, and maintenance. State departments of transportation invest heavily in personnel and equipment to collect traffic counts supporting AADT estimation on all highway segments in their systems on a regular basis.

Vehicles are detectable in air photos, high-resolution satellite images, and LiDAR data of highway segments, which are regularly collected for various purposes. A Bayesian approach is developed to incorporate the traffic data extracted from these images in the existing practice of AADT estimation. The uncertainty in the AADT on a segment is expressed by a probability distribution. In any year of interest, the approach begins with a prior AADT distribution that is updated to a posterior distribution when a traffic count is available. When incorporating the uncertainty in traffic growth, this approach can be applied year by year. Methods are developed to model the prior distribution of the AADT and the probability distribution of short-term traffic counts conditional on the AADT, which are two important components of this approach. Parameters are estimated to make the approach operational.

A numerical study is conducted to simulate AADT estimation during a typical cycle of traffic count collection on the ground. The results show that a small amount of image-based data could be exploited through the Bayesian approach to improve accuracy in AADT estimates while reducing the number of costly and dangerous ground counts. Sensitivity analysis indicates that the Bayesian approach would provide positive benefits for a large range of conditions.

Operational issues are discussed for the Bayesian approach, and it appears that the method could be implemented in state DOTs if the institutional means are developed to extract image-based data and place them in a format that could be easily integrated with data presently used to estimate AADT. Additional areas are suggested for future study.

Indexing (details)

0709: Transportation
Identifier / keyword
Social sciences; Annual average daily traffic; Estimation; Image-based data
Incorporating image -based data in AADT estimation: Methodology and numerical investigation of increased accuracy
Jiang, Zhuojun
Number of pages
Publication year
Degree date
School code
DAI-A 66/07, Dissertation Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
9780542242458, 0542242451
McCord, Mark R.
The Ohio State University
University location
United States -- Ohio
Source type
Dissertations & Theses
Document type
Dissertation/thesis number
ProQuest document ID
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
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