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Robust Data Collection Fuels UGPTI Road and Bridge Investment StudiesPosted: Apr 27, 2022
Traffic counts, the number of trucks in that traffic, predicted changes in oil, gas, and agricultural production and the impact of those changes on roads and bridges, current road conditions and other factors are all considerations in the UGPTI's studies. "The more detail that we include in our modeling efforts, the more detail we'll have in our predictions of investment needs across the state," UGPTI researcher Alan Dybing said. UGPTI uses NDDOT traffic counts and conducts its own across the state to generate estimates of average daily traffic at key points on county and township roads. The number of counts has increased and the kind of detail that can be extracted from those numbers has improved. For example, some traffic count devices can distinguish between cars, single-unit trucks, and tractor-trailer trucks. "The percent of heavy trucks in traffic is a key factor in road deterioration," Dybing said. "Also, we've moved into areas where traffic hasn't been counted as much previously," he noted. "More complete data means that our final analysis is more complete as well." Earlier studies used pavement condition information gathered by a van equipped with video and laser instrumentation and operated by technicians from the NDDOT. The van documented in detail, crumbing pavement, pavement cracks, rutting, open seams, and bumps to establish a pavement rating. But that one expensive van could only be used in targeted areas, not the entire road network. Now the researchers are using smart phones to detect and record ride quality. Most current smart phones are equipped with accelerometers, GPS, cameras, and other sensors that provide the necessary data. Thanks to research at NDSU and elsewhere, the cell phone data can be collected via an app and calibrated so that it's consistent with data from the NDDOT's van. The new technology doesn't provide as much detail, but it's relatively inexpensive and can be used in a typical car to assess ride quality across a broader area. Managing that volume of data can be a challenge. That's why UGPTI developed the Geographic Roadway Inventory Tool, an online roadway asset management tool that allows researchers and road managers to store ride-quality information, video and still images, construction data and specifications, pavement type and age, base thickness, shoulder width, load restrictions and other information. Local road managers enter their own information into GRIT which allows them to store and track roadway information in a consistent manner over time. That combination of information helps local road managers identify road segments that need maintenance. GRIT's display and mapping capabilities also allows them to visualize and display information easily for county decision makers. At the same time, that cooperative effort between researchers and road managers, gives the researchers access to road condition information from across the state to feed into the investment needs studies. In 2021, researchers collected ride-quality data and video images from about 3,000 miles of paved county roads across the southern half of the state. In 2022, they'll collect similar data from roads across the northern half of the state. The data is calibrated and geo-referenced to roadways so they can be included in GRIT. "GRIT is really a win-win for both UGPTI and local road agencies," noted to UGPTI researcher Brad Wentz who oversaw the development of the tool. "We gain access to much more up-to-date information about roads and road condition, while local managers have an improved way to access, manage and visualize road condition information across their networks." In addition to the road information, researchers collect data on traffic generators across the state. Dybing and UGPTI researchers Tim Horner and Kelly Bengtson, along with UGPTI director Denver Tolliver meet with staff of the Oil and Gas Division of the North Dakota Industrial Division to obtain the latest estimates of energy development in the state. Oil well drilling estimates, along with updated data on inbound and outbound truck, rail and pipeline shipments for each well, are used to develop comprehensive traffic models for predicting traffic across the state for the next 20 years. Similarly, the researchers assess crop production estimates and projections for every township based on information from the National Agricultural Statistics Service. Those predictions will be used to estimate road and bridge investment needs for the county and township roads and bridges. Counties and townships have been surveyed to collect data on costs of gravel and gravel hauling as well as road maintenance practices such as blading. Currently, all of that data is being entered into the UGPTI's modelling system which generates 20-year estimates of road and bridge deterioration and estimated costs to repair and maintain county and township roads and bridges for the next 20 years. Data is calibrated and reviewed for accuracy. For instance, most traffic counts are for a 48-hour period, so may be adjusted up or down for seasonal ebbs and flows in traffic. Likewise, abnormalities like the drops in traffic seen during the pandemic are accounted for. Samples of electronically collected ride quality data are compared and calibrated against known data collected by NDDOT's van. "We rely heavily on graduate students to help with both the data collection, the calibration and the modeling," Dybing noted. "They benefit from the experience of working with us and we benefit from their new perspectives their knowledge of new technologies." A report summarizing the data collection, analysis and predicted investment need will be prepared for the next session of the North Dakota Legislature to use in making decisions regarding road investments. The report is expected to be released in November. |
Since the Upper Great Plains Transportation Institute (UGPTI) at North Dakota State University began conducting statewide road and bridge investment need studies for the ND Legislature more than 10 years ago, it has continually refined and improved its data collection to increase the level of detail in the studies.