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Weigh-in-motion (WIM) is a process of measuring the dynamic tire forces of a moving vehicle and estimating the corresponding tire loads of the static vehicle. This collection of lectures from the International Conference on Weigh-in-Motion details applications such as: collection of statistical traffic data, support of commercial vehicle enforcement, roadway and bridge cost allocation, and traffic management.
The Arizona Department of Transportation (ADOT) plans to install new weigh‐in‐motion (WIM) stations with either piezo-polymer or piezo-quartz sensors. Recognizing some limitations of WIM sensor technologies, ADOT sponsored this study to ensure the accuracy of the future WIM data collection. The project tasks included (1) reviewing other highway agencies’ practices related to WIM data quality assurance through literature review and a survey; (2) developing a guidebook of clear recommendations for managing WIM installation, calibration, maintenance, and data quality assurance; and (3) developing a research report with recommendations on how to achieve successful implementation of a WIM program. Through reviewing available literature and surveying selected highway agencies, the project team determined that the piezo-quartz sensors perform much better than the piezo-polymer sensors due to their consistent reliability, reduced calibration requirements, and relative temperature insensitivity. With proper installation, piezo-quartz WIM sensors should provide accurate axle and truck weight measurements in Arizona. Findings also indicated that piezo-polymer sensors should perform well in Arizona for vehicle classification, traffic volume, and speed studies, but not for weight data collection. This is due to the temperature sensitivity of piezopolymer sensors and to the limitations of auto-calibration and temperature compensation technologies in environments where pavements undergo rapid day-to-night temperature changes and are subjected to high seasonal temperatures. Piezo-polymer sensor use with an auto-calibration feature for weight measurements should be evaluated on a case-by-case basis. Using findings from the literature review and the successful WIM practices survey, the research team developed a guidebook with recommendations and procedures for WIM site selection and qualification, installation, calibration, maintenance, data quality assurance, and personnel needed to support ADOT’s WIM program. These recommendations are specific to WIM systems that use piezo-quartz sensors and piezo-polymer sensors. The guidebook is included as Chapter 4 of this final report.
Introduction -- Types of equipment -- Technology descriptions -- A process for selecting equipment -- Best practices for equipment use -- Abbreviations used without definitions in TRB publications.
An intelligent transportation system (ITS) offers considerable opportunities for increasing the safety, efficiency, and predictability of traffic flow and reducing vehicle emissions. Sensors (or detectors) enable the effective gathering of arterial and controlled-access highway information in support of automatic incident detection, active transportation and demand management, traffic-adaptive signal control, and ramp and freeway metering and dispatching of emergency response providers. As traffic flow sensors are integrated with big data sources such as connected and cooperative vehicles, and cell phones and other Bluetooth-enabled devices, more accurate and timely traffic flow information can be obtained. The book examines the roles of traffic management centers that serve cities, counties, and other regions, and the collocation issues that ensue when multiple agencies share the same space. It describes sensor applications and data requirements for several ITS strategies; sensor technologies; sensor installation, initialization, and field-testing procedures; and alternate sources of traffic flow data. The book addresses concerns related to the introduction of automated and connected vehicles, and the benefits that systems engineering and national ITS architectures in the US, Europe, Japan, and elsewhere bring to ITS. Sensor and data fusion benefits to traffic management are described, while the Bayesian and Dempster–Shafer approaches to data fusion are discussed in more detail. ITS Sensors and Architectures for Traffic Management and Connected Vehicles suits the needs of personnel in transportation institutes and highway agencies, and students in undergraduate or graduate transportation engineering courses.