Innovations in Freight Demand Modeling and Data Improvement (2014)

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Statewide Freight Demand Modeling to Support Long-Range Transportation Planning in North Dakota EunSu Lee, North Dakota State University; Alan Dybing, North Dakota State University; Denver Tolliver, North Dakota State University Presentation Notes: Presented by EunSu Lee, North Dakota State University. The model is the third generation of the state model. In 2011, the state began an integrated system modeling for the DOTs and MPOs. It combines economic and social activities into transportation planning. In addition to the state’s oil production, agriculture is a major producer for the state and, as land use has changed, there has been a change in crop mixture and growth in grain elevators. The amount of grain, by tons, shipped by elevators has increased dramatically. It is recognized that the growth of these industries has effects on the state’s economy, safety, and the environment. There is a need for spatial analysis and freight demand modeling to develop an integrated model. The model uses trip generation and crop yields for agricultural trip generation. This was validated through a traffic county program. Further, pavement information was collected for a lifecycle cost analysis. This is to be applied for decision making for state and county transportation planning. The model also supports energy and agricultural logistics, analyzes bridge needs, and provides transferability and a manner of integration to other models. Abstract North Dakota has experienced significant traffic increases during the last 10 years because of growth in oil production and changes in agricultural production and marketing patterns. As of November 2010, the number of oil wells in the state had risen to 5,200 (Tolliver and Dybing 2010). The total number of truck movements is estimated to be 2,300 per well, with approximately half of them representing loaded trips. In addition to the oil-related activities, the importance of agriculture of North Dakota’s economy is significant. In 2009, the total market value of agricultural goods produced in the state exceeded $5.5 billon (Tolliver et al. 2011). This study combines the effects of agricultural and oil-related traffic and other economic activities on county and local roads and major highways in North Dakota. This study focuses on the prediction of agricultural traffic flows, oil-related traffic flows, and passenger travel across the state. The study predicts traffic flows for the next 20 years and projects infrastructure investment needs of the state to support long-range transportation planning. Model Development and Data Sources Large-scale freight modeling is complex because of the variety of factors involved, difficulty in obtaining data, and dynamic changes in facilities and logistics behavior. As much as possible, this study utilizes private and public data sources to consider several major transportation activities in the state for freight demand modeling. Base networks are downloaded from the National Transportation Atlas Databases (NTAD), including national highway performance 59

networks, the Freight Analysis Framework (FAF3) network, railroads, waterways, intermodal facilities, and geographical boundaries. State highways and county and urban roads are collected from the North Dakota GIS Hub, which provides surface types and local route names. Trip Generation and Location Model Estimating agricultural tonnage and investment needs for individual road segments requires facility locations, crop production, and a crop distribution mode in which movements or flows are predicted from crop-producing zones to elevators and processing plants. The county-level crop production survey is available from the National Agricultural Statistics Service (NASS). In the analysis process, the land area devoted to the production of each crop in each county subdivision is estimated using GIS procedures that allow the extraction of vector data that are geometrically and mathematically associated with satellite raster images and geographically calculated cropland area. To facilitate the development of these estimates, the predicted areas devoted to crop production in each subdivision are adjusted based on the 2009 county production values (Tolliver et al. 2011). In the study, the final or interim destinations for crops are in-state processing plants or elevators that ship crops out of state to various domestic and export locations. The throughput at elevators is computed from monthly reports submitted to the North Dakota Public Service Commission by outbound mode and destination. Forecasts of future oil development were obtained from the North Dakota Oil and Gas Division. Over the long term, the total number of wells is expected to grow. However, the forecasts do not include future oil well locations. Instead, mineral spacing units (1 by 2 miles) were utilized for locating drilling locations and future wells. To forecast future well locations, a probabilistic maximum likelihood algorithm was developed. The spacing units are available from the Oil and Gas Division, and fresh water sources are available from the North Dakota Department of Natural Resources and private companies that drill water wells. Rail transloading centers are available from railroad companies and the North Dakota Pipeline Authority. However, the precise pipe loading sites are not available to the public, so this study collected the pipe transloading facilities from the public sources on the web. Route Generation and Trip Distribution Route generation provides the links between all possible origins and all possible destinations. Each spacing unit and crop field is connected to the area’s origin and destination locations in the network optimization model while considering maximum distance and capacity limitation (Tolliver and Dybing 2010). For the agricultural model, the study used the fastest paths to match origin of crop fields to the nearest elevators or elevators to the closest shuttle elevators or processing centers using PROC NETFLOW in SAS statistical software. Because trucking cost is measured on a per-mile basis, minimizing the travel time of agricultural goods movements to elevators and plants minimizes farm-to-market trucking cost on a system-wide basis. For the oil transportation model, the study finds the fastest paths to deliver materials and products through 60

higher classification and a higher speed limit, because the truck drivers are time-sensitive and congestion is an issue in the rural area, due to heavy oil development activity. Trip Assignment and Visualization During the process of the route generation, the researchers collected the road segments using Network Analyst in GIS. Thus, the assigned trips on a route can be converted to traffic on the segments that belong to the route. By adding up the traffic for all origin-destination pairs, the study can visualize the total traffic on a segment. Traffic Analysis (Traffic Data Survey and Traffic Counts) Traffic counters were deployed at 100 locations in 15 of the 17 oil and gas producing counties (Tolliver and Dybing 2010). At each of the selected sites, a count of no less than 24 hours was taken and adjusted to present the traffic over a 24-hour period. These raw counts were adjusted for monthly variation in the traffic to estimate the average daily trips (ADT) from each segment. The traffic counters were utilized to calibrate and validate the model for the base scenario. To classify road segments by traffic volume, the segments were classified by county road managers using maps obtained from the North Dakota Department of Transportation. The process is essentially to send a survey to county managers that includes detailed maps of each county with instructions to classify road sections by traffic volume (high, medium, and low). The secondary survey to the county contact persons included another set of county maps with instruction to classify the paved and gravel sections by road condition. A two-page questionnaire included in the condition survey identified component costs and existing maintenance and improvement practices. In addition to miles of road and forecasted traffic levels, the key factors that influence paved road investments are the number of trucks that travel the road, the types of trucks and axle configurations used to haul inputs and products, the structural characteristics of the road, the width of the road, and the current surface condition. The primary indicator of a truck’s impact is its composite axle load—which, in turn, is a function of the number of axles, the type of axle (e.g., single, double, or triple), and the weight distribution to the axle units. The pavement design equations of the American Association of State Highway and Transportation Officials (AASHTO) are used to analyze paved road impacts. Conclusions and Limitations More than 5,600 miles of paved county and local roads (exclusive of city streets) are traveled by agricultural and oil-related traffic and other highway uses. Some of these roads are under the jurisdiction of governments or agencies other than counties, such as townships, municipal governments, the Bureau of Indian Affairs (BIA), and the U.S. Forest Service. BIA and tribal roads are included, but the city streets and Forest Service roads are excluded from the study. To support energy and agricultural logistics, this study identified road segments to be improved and in need of investment for the next 20 years. 61

This study could be improved by including oil collection points along pipelines and by including urban traffic models. Smaller units of TAZs such as census blocks or 2-mile-by-2-mile polygons would provide better analysis for rural county roads than using 6-mile-by-6-mile township boundaries. References Tolliver, D., and A. Dybing. 2010. Additional Road Investments Needed to Support Oil and Gas Production and Distribution in North Dakota. Report submitted to the North Dakota Department of Commerce, December 2010. Tolliver, D., A. Dybing, P. Lu, and E. Lee. Modeling Investments in County and Local Roads to Support Agricultural Logistics in Journal of the Transportation Research Forum, Vol. 50, No. 2, 2011, pp. 101–115. 62