High-Performance High-Speed Wim for Sustainable Road Load Monitoring Using Gis Technology

Abstract

The increasing importance of better transport connectivity has indicated the need to develop high-speed road load monitoring technologies. The Belt and Road Initiative (BRI), Silk Road transportation programs considerable have developed the roads and highways networks in Kazakhstan and other Central Asian (CA) countries. Transportation services require proper maintenance and prompt track load monitoring. There is no holistic freight traffic management system that controls and monitors traffic flow in CA. A Weigh in Motion (WIM) technology can be used as an effective traffic management control system in the CA region. The WIM technology is designed to control axle and gross vehicle weight in motion. It has a wide range of applications, including pavement and bridge weight control, traffic legislation and state regulations. The WIM technology has advantages over conventional static weighing as it does not interrupt traffic flow by creating queues at monitoring stations. The WIM technology can be used not only as a weight control tool but also performs a comprehensive analysis of other traffic flow parameters. In cooperation with Korean UDNS experts with support from KAIA, we test the application of WIM in Nur-Sultan city, North of Kazakhstan, with Siberian-type cold weather. These works create much challenges and innovative approach to test sensors in the harsh environment, from the extreme cold to hot temperatures, with intensive dust distortions. Our Talapker WIM pilot test site was installed in September 2020, and it performs Gross Vehicle Weight (GVW) and Axle of Weight (AOW) analyses. The Talapker WIM High Speed (HS) sensors are capable of detecting different driving patterns, including everyday driving, acceleration or deceleration more than 10km/h/s and eccentric driving (partial contact with the platform to avoid excessive weighting). The pilot Talapker HS WIM site has demonstrated a positive effect on implementing WIM technology in Kazakhstan. Every 10th car passing through the WIM site registered as an overloaded vehicle by gross weighting, and every 5th car is considered overloaded by axle weighting. GIS-based location allocation analysis (LAA) performed in the given study provided an understanding of a practical implementation of WIM sensors. Taking into consideration different geographical data, the WIM site map was developed to reveal 43 suitable locations. Further improvements for the CA road network and their WIM demand points will be the focus of future research investigations.