Tag Archives: Geographic Information System

New Tools to Optimize Truck Station Locations

The Minnesota Department of Transportation (MnDOT) has 137 truck stations across the state. These stations house and allow maintenance of MnDOT highway equipment as well as provide office and work space for highway maintenance staff. Within 20 years, 80 of these stations will need to be replaced as they reach the end of their effective life spans. Researchers developed a geographic information system based modeling tool to determine the most effective locations for truck stations in the state. Using data from many sources, a new research study has determined that MnDOT could rebuild 123 stations, relocate 24 on land available to MnDOT and combine two. MnDOT would save millions of dollars using the location optimization alternatives over the 50-year life cycle of a typical truck station.

What Was the Need?

MnDOT operates 137 truck stations, 18 headquarter sites for maintenance operations and over 50 areas for materials delivery. Truck stations are used to house and maintain large highway equipment, and to provide office and work space for highway maintenance staff. Some stations also store materials. 

The average life span of a truck station is 50 years. Within the next 20 years, 80 of MnDOT’s truck stations will need to be replaced. With costly capital replacement imminent, MnDOT has considered measures to optimize truck station locations within its eight state districts, including possibilities of reducing the size of some, increasing others, or combining the facilities of some state and local agencies into new partnerships. Determining the best effective locations for new truck stations could reduce costs for both state and local partners.

MnDOT needed a means of selecting and collecting the most appropriate data for an investigation into optimizing truck station locations. The agency also needed tools such as a computer model to analyze the data. These resources would allow MnDOT to determine the most time- and cost-effective locations for future truck stations. 

What Was Our Goal?

The initial objective of this research project was to collect data about truck service areas, including the quantity of highway equipment and materials capacity, and the materials storage capacity of facilities. This information combined with service route data would allow MnDOT to optimize truck station locations by determining whether facilities should be closed, resized, combined or relocated, and whether other materials storage locations would be necessary. An economic benefit–cost analysis would compare alternatives. 

A map of Minnesota indicates the location of each of MnDOT’s 137 truck stations with a blue square and of major highway routes connecting the stations, also shown in blue.
This project will determine the future of more than half of MnDOT’s 137 truck stations in the next two decades.

What Did We Do?

To determine how other departments of transportation (DOTs) and related agencies have addressed choosing the best locations for facilities, researchers conducted a literature review that included reports from six state DOTs and Australia, Transportation Research Board publications and other research papers. In addition, they consulted the standards developed by MnDOT’s Truck Station Standards Committee. 

Researchers also conducted surveys and interviews of both MnDOT and outside agency stakeholders. 

With many data sets collected for each truck station site, researchers used a geographic information system (GIS) platform to solve a location-allocation problem and a multivehicle routing problem for the truck stations. The problems incorporated such factors as amount of equipment, equipment capacity, storage capacity, material demand for road segments and other information. Estimated costs of operation for each location alternative were compared to present costs of each truck station. 

“Using real-world data, we built GIS models of maintenance operations to determine optimal truck station locations. With expected life spans of around 50 years, truck stations that are optimally located will reduce operating costs and save money for MnDOT and Minnesota taxpayers.” —William Holik, Assistant Research Engineer, Texas Transportation Institute

MnDOT’s Maple Grove Truck Station and Maintenance Center is a new 108,000-square-foot facility.
MnDOT’s truck stations range in size from Class 1 buildings of at least 25,000 square feet to smaller
Class 3 facilities with four or fewer overhead doors.
MnDOT’s Maple Grove Truck Station and Maintenance Center is a new 108,000-square-foot facility. MnDOT’s truck stations range in size from Class 1 buildings of at least 25,000 square feet to smaller Class 3 facilities with four or fewer overhead doors.

What Did We Learn?

The literature review showed that optimizations of facility locations may require a second level of sites, such as strategically placed materials storage depots. Some research also showed that both transportation and facility costs must be considered and that after a certain point, consolidation of stations could cost more as vehicles and staff were required to drive farther to reach them. 

Reports of state DOT location optimization efforts were instructive. Iowa DOT noted the need to consider the slow highway speeds of snowplows. This was a critical element for researchers to include in their optimization models as it determines route travel times. Vermont Agency of Transportation highlighted the use of satellite materials depots. Generally, state DOT efforts were confined to small regional issues, unlike MnDOT’s statewide scope.

In interviews with MnDOT and local agency stakeholders, researchers learned about partnerships that already existed between MnDOT and city and county agencies. These partnerships primarily included the sharing of truck stations and sometimes of materials. These partnerships were included in the optimization development.

Researchers optimized the truck station location using a GIS optimization model and separate cost analyses. They developed alternatives for each truck station individually. Each alternative was then analyzed to determine costs and savings over a 50-year life cycle. 

Finally, researchers determined which alternatives could be most effectively executed and their optimum order. They also developed an implementation plan for station relocation and replacement. This modeling was an iterative process: Each optimal location replaced the existing location and became the baseline against which the next station alternative was compared. The result was a comprehensive set of location possibilities for each MnDOT district with multiple alternatives for every truck station, including benefit–cost analyses. Researchers’ optimization solutions determined that 123 truck stations could be rebuilt on-site, 24 could be relocated on land available to MnDOT, and two could be combined. 

“We successfully analyzed all of our truck station and loading locations, determined which were good candidates for potential relocation or consolidation, and developed a data-driven plan of action to save millions of dollars.” —Christopher Moates, Planning Director, MnDOT Building Services

What’s Next?

MnDOT now has the information it needs to effectively implement cost-saving changes in future truck station planning and construction. The agency could use the researchers’ initial recommendations or further employ the GIS modeling tool to examine variations on the results of the project. 

This post pertains to Report 2019-10, “Optimizing Truck Station Locations for Maintenance Operations,” published February 2019. For more information, visit MnDOT’s Office of Research & Innovation project page.

New Project: Development of Pavement Condition Forecasting for Web-based Asset Management for County Governments

Many counties have incomplete roadway inventories, but lack asset management programs, which are often cost-prohibitive and require advanced technical training and staff to maintain. The Upper Great Plains Transportation Institute at North Dakota State University (NDSU), has developed a low-cost asset inventory program called the Geographic Roadway Inventory Tool (GRIT). The program, which is currently available to North Dakota counties, will be offered to all Minnesota counties following further development and testing by the Minnesota Local Road Research Board.

Background

NDSU created the asset inventory program as the first step for asset management to allow local roadway managers to document and understand their existing infrastructure using the latest mobile technology and Geographic Information System technology.

The goal of the research study is to expand the program to include roadway forecasting based on the American Association of State and Highway Transportation Officials(AASHTO) 93 model with inventory, pavement condition and traffic forecasting data.

Existing input data from GRIT, such as pavement thickness, roadway structural information and construction planning information, will be spatially combined with current Pathway pavement condition and traffic data from MnDOT to automatically forecast the future condition and age of roadways using the AASHTO 93 model. This forecasting model will then allow roadway managers to use this information with comprehensive GIS web maps to prioritize roadways in construction schedule or multi-year plans.

Geographic Roadway Inventory Tool

Objective

The additional information contained in the pavement forecast system will allow county roadway managers to prioritize projects that can benefit from lower cost pavement preservation activities and understand how long roadways can last before a high cost reconstruction must take place. The online GIS output maps will also enable the public to see what projects will be conducted on a year-to-year basis.

Project scope

The research team will work with Beltrami, Pope, Faribault, Pennington, and Becker counties and the city of Moorhead in Minnesota to research, develop, test and implement an additional forecasting function of the existing asset management program. This will be done using the AASHTO 93 empirical model to calculate a future pavement serviceability rating (PSR) based on the existing pavement structure and age, forecasted traffic and the latest pavement condition. While existing pavement structure and age information will come from data entered into the GRIT program by counties, processes and procedures will be researched and developed to automatically access pavement condition and traffic data from MnDOT and geospatially combine it with inventory data.

With pavement forecast information, county roadway managers will be able to better understand which roadways will deteriorate first and which will benefit from more effective, low-cost maintenance programs rather than full-depth reconstructions. The model will not forecast suggested future projects or project costs, but rather just output the future condition of the roadways on a yearly basis. The AASHTO model can be applied for both flexible and rigid pavement sections.

Watch for new developments on this project.  Other Minnesota research can be found at MnDOT.gov/research.

County GIS Maps Help Road Departments Anticipate Slope Failure

In a recently completed pilot study, researchers developed maps for two Minnesota counties that rank the failure potential of every slope using a geographic information system (GIS)-based model.

“GIS mapping has been applied to very small watersheds. The two counties in this study are huge areas in comparison. We used a physics-based approach that shows engineers where slope failure is likely to occur,” said Omid Mohseni, Senior Water Resources Manager, Barr Engineering Company.

What Was Our Goal?

The goal of this study was to determine if slope failure models could be developed to help counties anticipate where failures may occur. Researchers used publicly available data, research findings and geotechnical theory to develop failure models that could then be mapped with GIS in two topographically dissimilar Minnesota counties. These maps would identify slopes susceptible to failure so that county highway departments could develop preventive strategies for protecting roadways from potential  lope failure or prepare appropriate failure response plans.

What Did We Do?

Researchers began with a literature review of studies about the causes of slope failure, predictive approaches and mapping. They were particularly interested in research related to potential failure mechanisms, algorithms used for predicting failures and slope-failure susceptibility mapping.

Then investigators collected data on known slope failures in Carlton County in eastern Minnesota and Sibley County in south central Minnesota to identify failure-risk factors not found in the literature. Researchers reviewed various statewide data sets, identifying topographic, hydrologic and soils information that could be used in GIS-based modeling. Next, they developed a GIS-based slope-failure model by incorporating the available data with geotechnical theory and probability factors from hydrologic data, and writing computer code to allow the data to be input into mapping software.

Researchers tested the software on known failure sites to refine soil parameter selection and failure models. The refined models and software were then used to identify and map slope failure risks in Carlton and Sibley counties.

2018-05-p2-image
A detailed GIS map of a length of County Highway 210, color-coded to show slope failure susceptibility along the roadway.

What Did We Learn?

After analyzing the literature and the failure and geotechnical data, researchers identified the following key causal factors in slope failure: slope angle, soil type and geology, vegetation, land use and drainage characteristics, soil moisture, and rainfall intensity and duration.

Researchers then developed mapping models for the two counties using three key data sets. The first was data from 3-meter resolution, high-quality lidar, which measures distances with laser range finders and reflected light, available through Minnesota’s Department of Natural Resources website. The team augmented this data with U.S. Department of Agriculture soils survey data, and with National Oceanic and Atmospheric Administration and National Weather Service hydrologic data for precipitation and storm duration information.

Based on research in geotechnical theory, researchers developed algorithms for anticipating failure and built these into the lidar-based topographic mapping model. They also developed input parameters based on the failure factors and established output parameters representing five levels of failure susceptibility: very low, low, moderate, high and very high.

After testing the GIS-based model against a slope along County Highway 210 in Carlton County, researchers confirmed that failure potential correlated well with documented or observed slope failure. The team further validated the model by applying it to several small areas in the adjacent Carver and Sibley counties, finding similarly effective correlation with identifiable failure sites.

Independent geotechnical experts examined the modeling software and further refined geotechnical, soil and hydrologic elements. Finally, the team developed maps of Carlton and Sibley counties that assigned failure susceptibility levels to slopes in the two counties. Viewing maps through the software remains the most useful way to examine slopes, although large-format maps are available.

“If county engineers have higher slopes adjacent to roadways, they can use this basic tool to predict slope failures and then hire a geotechnical consultant to investigate the site.” – Tim Becker, Public Works Director, Sibley County

What’s Next?

With additional funding, mapping could be extended to every county in Minnesota to further refine failure modeling. Maps may also be useful in identifying structures such as roadways, ecological features, transmission lines and pipelines, bridges and culverts that may be threatened by slope failure susceptibility. Potential risks could be used to prioritize slope treatment plans.

This research effort is part of a slope failure risk mitigation strategy that includes the recently released Slope Stabilization Guide for Minnesota Local Government Engineers. Another project, underway at MnDOT, is identifying, mapping and ranking slopes vulnerable to slides that could affect the state highway network. The project

This post pertains to the Local Road Research Board-produced Report 2018-05, “Storm-Induced Slope Failure Susceptibility Mapping,” published January 2018. More information is available on the project page.