Smart Survey Model of Average Daily Traffic (ADT) for Pavement Planning and Monitoring

Abstract

Daily traffic volume surveys for the planning and supervision of manual paving require work, time and are not cheap. Furthermore, the accuracy and speed of data collection are not timely. This study aims to bring a smart survey model that automatically collects and processes data on the daily volume of traffic in real time. The methodology used to model the survey process using smart devices, microcontrollers, sensors, Internet, cloud programming, and customer servers. The model survey process is divided into Part 1, Data collection and Part 2, to handle the axle load of each vehicle. The data is sent via the cloud access point; the cloud data are processed by the server. The model provides traffic volume data with speeds of up to 67.4 Mbps, 98 percent accuracy, and data processing results for monitoring the paving age in real time.