Increasing the accuracy of loop detector counts using adaptive neural fuzzy inference system and genetic programming
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.
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- Find a library where document is available. Order URL: http://worldcat.org/oclc/1767712
- Abstract reprinted with permission of Taylor & Francis.
- Gholami, Ali
- Tian, Zong
- Publication Date: 2017-5
- Media Type: Web
- Pagination: pp 505-522
- TRT Terms: Accuracy; Case studies; Data collection; Intersections; Loop detectors
- Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;
- Accession Number: 01638847
- Record Type: Publication
- Files: TRIS
- Created Date: Jun 26 2017 9:31AM