EconPapers    
Economics at your fingertips  
 

Increasing the accuracy of loop detector counts using adaptive neural fuzzy inference system and genetic programming

Ali Gholami and Zong Tian

Transportation Planning and Technology, 2017, vol. 40, issue 4, 505-522

Abstract: Loop detectors are devices that are most commonly used for obtaining data at intersections. Multiple detectors are usually required to monitor a location, and this reduces the accuracy of detectors for collecting traffic volumes. The purpose of this paper is to increase the accuracy of loop detector counts using Adaptive Neural Fuzzy Inference System (ANFIS) and Genetic Programming (GP) based on detector volume and occupancy. These methods do not need microscopic analysis and are easy to employ. Four approaches for one intersection are used in a case study. Results show that the models can improve intersection detector counts significantly. Results also show that ANFIS produces more accurate counts compared to regression and GP.

Date: 2017
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03081060.2017.1300241 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:transp:v:40:y:2017:i:4:p:505-522

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/GTPT20

DOI: 10.1080/03081060.2017.1300241

Access Statistics for this article

Transportation Planning and Technology is currently edited by Dr. David Gillingwater

More articles in Transportation Planning and Technology from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

 
Page updated 2025-03-20
Handle: RePEc:taf:transp:v:40:y:2017:i:4:p:505-522