Evaluation of Different Methods to Calculate Heavy-Truck VMT, Final Report, December 2004

Reliable estimates of heavy-truck volumes are important in a number of transportation applications. Estimates of truck volumes are necessary for pavement design and pavement management. Truck volumes are important in traffic safety. The number of trucks on the road also influences roadway capacity and traffic operations. Additionally, heavy vehicles pollute at higher rates than passenger vehicles. Consequently, reliable estimates of heavy-truck vehicle miles traveled (VMT) are important in creating accurate inventories of on-road emissions. This research evaluated three different methods to calculate heavy-truck annual average daily traffic (AADT) which can subsequently be used to estimate vehicle miles traveled (VMT). Traffic data from continuous count stations provided by the Iowa DOT were used to estimate AADT for two different truck groups (single-unit and multi-unit) using the three methods. The first method developed monthly and daily expansion factors for each truck group. The second and third methods created general expansion factors for all vehicles. Accuracy of the three methods was compared using n-fold cross-validation. In n-fold cross-validation, data are split into n partitions, and data from the nth partition are used to validate the remaining data. A comparison of the accuracy of the three methods was made using the estimates of prediction error obtained from cross-validation. The prediction error was determined by averaging the squared error between the estimated AADT and the actual AADT. Overall, the prediction error was the lowest for the method that developed expansion factors separately for the different truck groups for both single- and multi-unit trucks. This indicates that use of expansion factors specific to heavy trucks results in better estimates of AADT, and, subsequently, VMT, than using aggregate expansion factors and applying a percentage of trucks. Monthly, daily, and weekly traffic patterns were also evaluated. Significant variation exists in the temporal and seasonal patterns of heavy trucks as compared to passenger vehicles. This suggests that the use of aggregate expansion factors fails to adequately describe truck travel patterns.

2007 Greenhouse Gas Emissions from Selected Iowa Source Categories

The Department’s 2007 Greenhouse Gas Inventory is a refinement of previous statewide inventories. It is a bottom-up inventory of two sectors – fossil fuel combustion at federally-recognized major sources of air pollution and fossil fuel combustion and ethanol fermentation at dry mill ethanol plants. This is the first bottomup greenhouse gas inventory conducted for Iowa and the first bottom-up greenhouse gas inventory of ethanol plants in the nation that the Department is aware of. In a bottom-up inventory, facility-specific activity data is used to calculate emissions. In a top-down inventory, aggregate activity data is used to calculate emissions. For example, this bottom-up inventory calculates greenhouse gas emissions from the fossil fuel combustion at each individual facility instead of using the total amount of fossil fuel combusted state-wide, which would be a top-down inventory method. The advantage to a bottom-up inventory is that the calculations are more accurate than a top-down inventory. However, because the two methods differ, the results from a bottom-up inventory are not directly comparable to a top-down inventory.

Multiplan Spreadsheets for Highway and Hydraulic Engineering Computations in County Engineering Offices, HR-284, 1987

Multiplan spreadsheet solutions were developed for a set of hydraulic and highway engineering computations of common interest to county engineers. These include earthwork, vertical and horizontal curves, staking superelevated curves and sign inventories for highways. The hydraulic applications were ditch flow, runoff, culvert size and stage discharge.

Emerging Threats to Iowa's Forests, Communities, Wood Industry and Economy, 2013

Iowa’s three million acres of forest land provide environmental benefits to all Iowans in terms of soil erosion control, air quality, and water quality. In 2013, more than 6.5 million trees died. Within those trees there were more than 125 million board feet of wood, compared to 98 million board feet of wood harvested. This level of mortality is the highest level reported from US Forest Service inventories in twenty years. This is disturbing when considering more than 18,000 Iowans are employed in the wood products and manufacturing industry, generating nearly $4 billion in annual sales, more than $900 million in annual payroll and more than $25 million to private woodland owners annually from the sale of timber.

Planning information system, 1967.

The Transportation Facilities Manual provides a system of identifying and coding existing streets and highways and of recording data pertaining to these facilities. This manual is part 1 and together with the other two documents may be used in connection with the preparation of comprehensive and special planning an urban research studies of all kinds. Particular emphasis was placed on the updating of collected information so that basic inventories pertaining to the planning process can be kept current without undue effort or cost.

Grade and Cross Slope Estimation from LIDAR-based Surface Models, 2003

Many transportation agencies maintain grade as an attribute in roadway inventory databases; however, the information is often in an aggregated format. Cross slope is rarely included in large roadway inventories. Accurate methods available to collect grade and cross slope include global positioning systems, traditional surveying, and mobile mapping systems. However, most agencies do not have the resources to utilize these methods to collect grade and cross slope on a large scale. This report discusses the use of LIDAR to extract roadway grade and cross slope for large-scale inventories. Current data collection methods and their advantages and disadvantages are discussed. A pilot study to extract grade and cross slope from a LIDAR data set, including methodology, results, and conclusions, is presented. This report describes the regression methodology used to extract and evaluate the accuracy of grade and cross slope from three dimensional surfaces created from LIDAR data. The use of LIDAR data to extract grade and cross slope on tangent highway segments was evaluated and compared against grade and cross slope collected using an automatic level for 10 test segments along Iowa Highway 1. Grade and cross slope were measured from a surface model created from LIDAR data points collected for the study area. While grade could be estimated to within 1%, study results indicate that cross slope cannot practically be estimated using a LIDAR derived surface model.