|
" Estimation of Annual Average Daily Traffic (AADT) and missing hourly volume using artificial intelligence "
Sababa Islam
Chowdhury, Mashrur
| Document Type
|
:
|
Latin Dissertation
|
| Language of Document
|
:
|
English
|
| Record Number
|
:
|
804355
|
| Doc. No
|
:
|
TL49184
|
| Call number
|
:
|
1864672903; 10247462
|
| Main Entry
|
:
|
Yuzhu, Ge
|
| Title & Author
|
:
|
Estimation of Annual Average Daily Traffic (AADT) and missing hourly volume using artificial intelligence\ Sababa IslamChowdhury, Mashrur
|
| College
|
:
|
Clemson University
|
| Date
|
:
|
2016
|
| Degree
|
:
|
M.Engr.
|
| field of study
|
:
|
Civil Engineering
|
| student score
|
:
|
2016
|
| Page No
|
:
|
135
|
| Note
|
:
|
Committee members: Luo, Feng; Sarasua, Wayne
|
| Note
|
:
|
Place of publication: United States, Ann Arbor; ISBN=978-1-369-55077-1
|
| Abstract
|
:
|
Annual Average Daily Traffic (AADT) is one of the most important traffic parameters used in transportation engineering analysis. Moreover, each state Department of Transportation (DOT) must report the AADT data to Federal Highway Administration (FHWA) annually as part of the Highway Performance Monitoring System (HPMS) requirements. For this reason, state DOTs continually collect AADT data via permanent count stations and short-term counts. In South Carolina, only interstates and primary routes are equipped with permanent count stations. For the majority of the secondary routes, AADT data are estimated based on short-term counts or are simply guesstimated based on their functional classifications. In this study the use of Artificial Neural Network (ANN) and Support Vector Regression (SVR) were applied to estimate AADT from short-term counts. The results were compared to the traditional factor method used by South Carolina Department of Transportation (SCDOT) and also to the Ordinary Least-square Regression method. The comparison between ANN and SVR revealed that SVR functions better than ANN in making AADT estimation for different functional classes. A second comparison was conducted between SVR and the traditional factor method. The comparative analysis revealed that SVR performed better that the traditional factor method. Similarly, the comparison between SVR and regression analysis for the principal arterials revealed no significant difference in the actual AADT and the AADT estimated through SVR. However, it did show a significant difference between the actual AADT and AADT estimated through regression analysis.
|
| Subject
|
:
|
Civil engineering; Transportation planning
|
| Descriptor
|
:
|
Social sciences;Applied sciences
|
| Added Entry
|
:
|
Chowdhury, Mashrur
|
| Added Entry
|
:
|
Civil EngineeringClemson University
|
| |