This Small Business Innovation Research (SBIR) Phase II project will develop a fully functional prototype of an accelerometer-based wireless Weigh In Motion (WIM) station. The WIM system will comprise an array of battery-powered 3? cubes, embedded in the pavement, each consisting of an accelerometer, a microprocessor for local signal processing, and a radio that sends the processed measurements to an Access Point (AP) on the side of the road. The AP estimates the pavement load from each axle of a truck at freeway speeds and the truck?s class, and transmits these estimates to the traffic management center. The cubes take up minimal space and are installed within minutes, so WIM systems can be deployed anywhere at a fraction of the cost of traditional WIM stations. Phase I research demonstrated the technical feasibility and commercial potential of the WIM. The technical objectives of Phase II concern the WIM packaging and installation; calibration: sensitivity to weight, speed and temperature (especially for asphalt pavements); signal compression and source coding; channel coding; wide area data backhaul; overall system design; manufacturing prototype samples; and extensive testing.
The broader impact/commercial potential of this project is to dramatically enhance the regulation of truck weights and provide data to greatly improve the maintenance of the US road and bridge infrastructure by drastically reducing the costs of WIM stations. Current WIM stations have limited deployment as they are costly to install requiring shutting the road for days and needing expensive maintenance and re-calibration. The new WIM stations could be widely deployed in additional locations on arterial streets and near ports to monitor truck traffic and be a component in a truck weight-based enforcement and toll system. These WIM stations could also meet similar objectives in overseas markets creating employment for US residents with diverse skills in the design, manufacturing, sales, and installation.
Normal 0 false false false EN-US X-NONE X-NONE Project Outcomes Report In this project, we developed two systems that monitor vehicles at highway speeds: an Automatic Vehicle Classification (AVC) system and a Weigh In Motion (WIM) system. The AVC system identifies the number of axles on a vehicle and the distance between the various axles. Based on this information, vehicles are categorized into a number of classes ranging from cars to multi-axle tractor-trailer trucks. The WIM system in addition estimates the weight of the individual axles and the Gross Vehicle Weight (GVW). The primary customers for these systems are state and regional transport agencies. These types of systems are important for highway operations, safety, and maintenance. The AVC systems are used by agencies to collect data on truck routes on the national and state highways and for automatic toll collection where tolls are determined by vehicle class. The WIM systems are used by agencies to screen overweight trucks before a highway static weigh and inspection station, to reduce the delays and dangers associated with trucks backing up onto the highway during peak periods. Note that WIM systems have not been legally approved in the United States yet for direct enforcement. WIM systems also provide valuable cumulative loading data for pavement maintenance systems to determine when a highway needs to rehabilitated before it fails. Current AVC and WIM systems are expensive to manufacture, install, operate, and maintain. In this project we developed a unique accelerometer-based, battery-operated, wireless sensor in a hardened 3" cube that installs in less than 15 min in the roadway. The accelerometer senses the pavement vibrations generated by the vehicle axles and sends the information wirelessly to a roadside unit. A similar magnetometer-based wireless sensor identifies the presence/absence of a vehicle corresponding to changes in the magnetic field. Based on the measurements by arrays of these accelerometer and magnetometer sensors, the AVC system can identify the number and spacing between the axles and the WIM system can in addition estimate the weight of the axles. The weight estimation is a significant challenge as the system has to compensate for changes in the sensed vibration due to vehicle speed, chassis, trajectory, pavement material, and temperature. The primary intellectual contributions of this project were threefold: (i) a system design of wireless sensors that enables a cost-effective system that is quick and easy to install and maintain, (ii) signal processing algorithms to overcome noise in the roadway and wireless links, and (iii) estimation procedures to compensate for the above mentioned factors and achieve high AVC classification accuracy (>99%) and low weight estimation error (< 10%). We were able to test and validate the AVC and WIM system performance under real world operating conditions at a test site on Highway 80 in Pinole, CA with the support of the California Department of Transportation (Caltrans) and the California Highway Patrol (CHP). The systems are in the process of being commercialized by Sensys Networks. We anticipate the high benefit/cost ratio of the systems will create new markets with local traffic agencies and ports. The technology developed in this project is being adapted for applications to health monitoring of structures and railroad WIM.
