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

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