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Accurate traffic counts are important for budgeting, traffic planning, and roadway design. With thousands of centerline miles of roadways, it is not possible to install continuous counters at all locations of interest (e.g., intersections). Therefore, at the vast majority of locations, MnDOT samples axle counts for short durations, called portable traffic recorder (PTR) sites, and obtains continuous counts from a small number of strategically important locations. The continuous-count data is leveraged to convert short-duration axle counts into average-annual-daily traffic counts. This requires estimation of seasonal adjustment factors (SAFs) and axle correction factors at shortcount locations. This project focused on developing a method for estimating SAFs for PTR sites. The continuous-count data was grouped into a small number of groups based on seasonal traffic-volume patterns. Traffic patterns at PTR sites were hypothesized by polling professional opinions and then verified by performing statistical tests. PTRs with matching seasonal patterns inherited SAFs from the corresponding continuous-count locations. Researchers developed a survey tool, based on the analytic hierarchy process, to elicit professional judgments. MnDOT staff tested this tool. The statistical testing approach was based on bootstrapping and computer simulation. It was tested using simulated data. The results of this analysis show that in the majority of cases, three weekly samples, one in each of the three seasons, will suffice to reliably estimate traffic patterns. Data could be collected over several years to fit MnDOT's available resources. Sites that require many weeks of data (say, more than five) may be candidates for installation of continuous counters.
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.