AN IOT PROTOTYPE TO COLLECT TRANSVERSE VEHICLE POSITION DATA FOR LOCAL ROADS

Abstract

In New Zealand, most roads are chip-sealed over granular base pavements, which rely on the Transverse Variable Application Spray Bar (TVAS) during the resealing process. TVAS sprays a lower application rate of bitumen in the wheel path areas to limit flushing and uses a higher application rate outside of wheel path areas to limit raveling. Currently, this is carried out by visually identifying the wheel paths where flushing and raveling exist. This manual process is prone to human bias. Ideally, pavement resealing needs to be done before visible signs of distress appear. This study aims to automate obtaining lateral wheel path distribution (LWD) using an Internet of Things (IoT) prototype. This prototype is based on the Arduino platform, which was used to collect wheel placement distributions from vehicles on several sites in Christchurch, New Zealand. Results show that narrow roads exhibit a concentrated wheel path distribution. Additionally, vehicles tend to drive further away from the solid white shoulder line and closer together when parked cars are present. The results can be incorporated into the calibration of the TVAS and pavement design for improved construction and maintenance efficiency.