Traffic density estimation using progressive neural architecture search

Abstract

The current research work is to develop an automated system for estimating the traffic density of roads at traffic junctions and proposed a traffic control algorithm for smooth movement of vehicles based upon the information provided by the density estimation algorithm. The existing methodologies of hand-crafted features and rule-based estimation to count the number of vehicles has been replaced here by Convolutional Neural Network (CNN). The conventional CNN has been replaced by Progressive Neural Architecture (PNAS) which is based on sequential model-based optimization (SMBO) strategy.

Subject Classification:

• Primary 93A30
• Secondary 49K15

Keywords:

• Convolutional Neural Network (CNN)
• Progressive Neural Architecture Search (PNAS)
• Traffic Density Estimation
• Image Classification