Category Archives: Policy and Planning

New measure allows comparison between bridge and pavement conditions

Transportation planners lack a method to directly compare bridge and road conditions. In a new MnDOT-funded study, University of Minnesota researchers have proposed a Percent Remaining Service Interval (PRSI) measure that can uniformly assess the condition of bridges and pavements, enabling planners to make the most efficient use of preservation and improvement funding.

A nighttime view of workers and heavy equipment at a road construction site
Planners would like a condition measure similar to RSL that could be used to compare and prioritize needs for highway and bridge construction.

“Both the MnDOT Bridge Office and the Materials and Road Research Office have very good management systems in place,” says Mihai Marasteanu, a professor in the Department of Civil, Environmental, and Geo- Engineering (CEGE) and the study’s principal investigator. “There is a good potential to develop a new common metric that both offices could use.”

What Did We Do?

To begin developing this new measure, researchers conducted a literature review of current methods used in asset management and life-cycle cost analysis. The review of bridge research focused on performance measures and life expectancy assessment methods, while the study of pavement literature concentrated on performance measures as well as on the use of road service life measures.

Next, the research team, which included civil engineering bridge professor Arturo Schultz, surveyed both bridge management staff and pavement management staff from state transportation agencies. Team members then analyzed the asset management practices of MnDOT’s Office of Bridges and Structures and Office of Materials and Road Research to identify methods for assessing service lives and rehabilitation needs and to highlight the similarities and differences in approaches.

Based on the findings from the survey and analysis, researchers suggested the new method of PRSI that would serve both pavement and bridge needs and offered guidelines for the next steps in developing and implementing a unified PRSI procedure.

“Ultimately, funds for guardrail repairs are drawn from the same purse that pays to fill a pothole or repair a deck joint,” Marasteanu says. “With PRSI, planners could target average values across systems to optimize life-cycle costs and pursue an even distribution of PRSI values to make planning consistent from year to year.”

What’s Next?

In the next phase of the project, researchers will work with the pavement office to identify relevant data for calculating PRSI for pavements. “In addition, we plan to identify the time and costs required to reach the evenly distributed configuration of PRSIs necessary for planning consistency, assess how preservation activities impact funding efficiency, and calculate recommended metrics for asset sustainability,” Marasteanu says.

This article originally appeared in the Center for Transportation Studies’ Catalyst Newsletter, October 2018. The full report, published July 2018, can be accessed at “Remaining Service Life Asset Measure, Phase I,” .

 

 

Implementation of Research Strategic Plan Underway

Coverpage of Research Services Strategic PlanTo help guide the state’s future transportation research investments, the Minnesota Department of Transportation recently completed a five-year comprehensive strategic plan that looks at streamlining the research governance structure at MnDOT and developing a clearinghouse of information about the agency’s research portfolio to improve decision-making.

MnDOT Research Services, which administers the bulk of the state’s transportation research projects, recently completed a visioning session with agency stakeholders as the first step in implementing the recommendations of the strategic plan, which include:

  • Establishing agency-wide research strategic priorities
  • Tracking all of MnDOT’s research expenditures, including those performed outside Research Services
  • Tracking research investment levels to measure return on investment
  • Reporting on the outcomes of research projects beyond their life cycle
  • Identifying the value and impact of research at a topic and program level

In addition to the approximately 175 state, local and multi-state transportation research projects administered and tracked by MnDOT Research Services, several MnDOT specialty offices also invest in their own research to support or guide their work.

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.

New Project-Quantifying the Impacts of Complete Streets: The Case of Richfield

Complete Streets is a transportation policy and design approach that requires streets to be planned, designed, operated, and maintained to enable safe, convenient and comfortable travel and access for users of all ages and abilities, regardless of their mode of transportation. A newly funded research project aims to demonstrate the economic and non-economic benefits of Complete Streets in the city of Richfield, which has been active in reconstructing several previously vehicle-oriented roads to allow for safe travel by those walking, cycling, driving automobiles, riding public transportation, or delivering goods.

By measuring the impacts of pedestrian- and bike-related improvements in Richfield, this Minnesota Local Road Research Board-funded study hopes to help guide future transportation investments for building sustainable and safe urban environments.

This analysis will include four closely related steps:

  • First, University of Minnesota researchers will select suitable improvement sites in Richfield to study and collect project information, including project maps, description of complete street features and GIS files at the parcel level before and after the project.
  • Identify economic and measurable non-economic benefits. The university will work with the City of Richfield to identify possible economic benefits (such as increased property value) and other measurable benefits (such as public health benefits associated with pedestrian or cycling activities) of the Complete Streets projects.
  • Estimate economic benefits, such as increased housing value or as additional business activities.
  • Lastly, researchers will quantify and monetize non-economic benefits, such as public health or environmental benefits related to pedestrian or cycling activities. Data about benefit indicators will be collected through survey or interview. These benefits will then be monetized using common value parameters identified from the literature.

New performance measures identify truck delays and bottlenecks

A new freight transportation study takes the next step in lessening traffic bottlenecks by pinpointing location and time of recurrent delays.

Freight transportation provides significant contribution to our nation’s economy. Reliable and accessible freight network enables business in the Twin Cities to be more competitive in the Upper Midwest region. Accurate and reliable freight data on freight activity is essential for freight planning, forecasting and decision making on infrastructure investment.

Researchers used detailed and specific data sets as tools to investigate freight truck mobility, reliability and extent of congestion delays on Twin Cities metropolitan area corridors. Precise locations and times of recurrent delays will help to mitigate future traffic bottlenecks.

“This research provided tools and metrics with new levels of precision concerning truck congestion. The results will allow us to take the next steps toward future investment in addressing freight bottlenecks,” said Andrew Andrusko, Principal Transportation Planner, MnDOT Office of Freight and Commercial Vehicle Operations.

What Was the Need?

The corridors of the Twin Cities metropolitan area (TCMA) provide a freight transportation network that allows regional businesses to be competitive in the Upper Midwest. However, traffic volumes on many of these roadways are facing overcapacity during peak travel periods. Heavy truck traffic is only expected to increase, and delays will continue to disrupt freight schedules.

A 2013 study by MnDOT and the Metropolitan Council suggested the need to identify when and where truck congestion and bottlenecks developed in the TCMA. Previous research funded by MnDOT examined heavy truck movement along 38 Twin Cities freight corridors. Researchers created freight mobility and reliability measures, and worked to identify significant bottlenecks. Further research was needed to extract more precise data to better understand TCMA freight traffic congestion.

2018-15-p2-image
The top five congested AM and PM peak corridors in the TCMA are listed above with the delay hours for each period. The large delay hours arise from heavy truck volume and speeds far below base free-flow speeds.

What Was Our Goal?

The aim of this project was to combine data from the U.S. DOT National Performance Management Research Data Set (NPMRDS) with information from other sources to build on the previous study’s analyses of mobility, reliability and delay along key TCMA freight corridors. New performance measures would more clearly identify the extent of system impediments for freight vehicles during peak periods in selected corridors, allowing researchers to identify causes and recommend mitigation strategies.

What Did We Do?

Researchers worked with stakeholders to prioritize a list of TCMA freight corridors with NPMRDS data coverage. The NPMRDS includes travel time data from probe vehicles at five-minute intervals for all National Highway System facilities. The travel times are reported based upon Traffic Message Channel (TMC) segments with link lengths varying from less than 1 mile to several miles. Researchers worked with 24 months of NPMRDS data from the selected corridors.

Because of varying TMC segment lengths, researchers used geographic information system (GIS)–based data to georeference the NPMRDS data to relevant maps. Combining these with average travel time data from passenger and freight vehicles, researchers used their data analysis framework to generate measures of truck mobility, reliability and delay at the corridor level.

A truck mobility analysis of all the selected corridors was performed using the truck-to- ar travel time ratio (TTR) for each TMC segment of each five-minute interval computed in AM (6-10 a.m.), midday (10 a.m.-4 p.m.) and PM (4-8 p.m.) peak periods using the 24- month NPMRDS data. A TTR of 1 describes a truck and a car traveling a distance in the same amount of time. On average, trucks are known to travel 10 percent slower than cars on freeways: a TTR of 1.1. A truck traveling 20 percent slower would have a TTR of 1.2.

Reliability measures evaluated the truck travel time reliability. Researchers computed truck delay during rush hour on the GIS network by fusing truck volumes, posted speed limit and NPMRDS data.

Researchers computed a truck congestion measure by comparing truck travel time with the target travel time in each TMC segment, which provided a measure of delay (in lost hours) at the segment and corridor level.

What Did We Learn?

The truck mobility analysis revealed that roadways with intersections have a higher TTR. Trucks on U.S. and Interstate highways take about 10 percent longer to travel the same distance as cars: TTR 1.1. On state highways, the TTR reaches 1.2 and 1.4 in the AM and PM peak periods, respectively. On county roads, trucks slow considerably: midday TTR is 1.5 and spikes to 1.7 and 1.9 in the AM and PM peak periods. Intersections in a TMC segment and delays at signalized intersections could have caused the TTR increases.

All reliability measures indicated that truck travel time in the PM peak period is less reliable than in the AM peak period. Similar to the TTR measure, roadways with signalized or unsignalized intersections were less reliable for truck traffic than freeways.

Truck congestion and delay measures revealed that the top five TCMA corridors with significant congestion had an average delay of over 3,000 hours in the AM and PM peak periods, with the PM delays notably greater. Also, in the AM peak period, eight additional interchanges had average delays of over 300 hours per mile. In the PM peak period, nine interchanges and eight segments showed significant congestion.

The top six TMC noninterchange segments exhibiting recurring PM peak period delays on average weekdays had delays ranging from 495 hours to 570 hours per mile.

Insufficient capacity, increasing demand, roadway geometry and density of weaving points (on-and off-ramps) were considered key causes of delay among these six bottlenecks.

What’s Next?

NCHRP Research Report 854, Guide for Identifying, Classifying, Evaluating and Mitigating Truck Freight Bottlenecks, provides guidelines for identifying, classifying, evaluating and mitigating truck bottlenecks. Follow-up research by MnDOT could potentially leverage this project’s effort with the NCHRP guidelines to develop mitigation strategies.

This post pertains to Report 2018-15, “Measure of Truck Delay and Reliability at the Corridor Level, published April 2018.

 

Traffic Tubes Still Provide More Accurate Counts Than GPS Smartphones

Collecting traffic volume information from smartphone data, navigation systems and other GPS-based consumer and mobile technologies is not yet ready for use by MnDOT. However, the emerging technology offers useful information on driving origins and destinations for traffic monitors and planners.

“We’re on the cusp of using GPS technology to get traffic data from more facilities. We’re not there today, but we’ve spurred the industry to look at this opportunity,” said Gene Hicks, Director, MnDOT Traffic Forecasting and Analysis, who helped lead the research study on this topic.

MnDOT conducts traffic counts on its roadway network at regular intervals: every other year on state trunk highways, approximately every four years on city and county roadways, and every 12 years on low-volume roads. To make these traffic assessments, MnDOT currently stretches pneumatic tubes across traveled lanes and counts passing axles for up to 48 hours.

2017-49-p1-image
Using pneumatic road tubes to collect traffic data is an old and reliable practice, but installing them is time-consuming and puts workers in harm’s way.

“Using road tubes to collect traffic volume data is a proven method, but it’s an old practice and puts people in harm’s way. Smartphones may offer a useful alternative,” said Shawn Turner, Division Head, Texas Transportation Institute, who helped evaluate a beta version of traffic volume estimates derived from global positioning system (GPS)-based mobile devices.

What Was Our Goal?

With this project, researchers aimed to explore using smartphones and other GPS-based systems instead of pneumatic tubes to collect traffic volume data. The information collected was compared with actual volume counts from MnDOT traffic monitoring sites.

What Did We Do?

In May 2016 researchers began identifying data collection firms interested in participating in this research effort. These firms were developing products that gather, aggregate and analyze sufficient location data from GPS mobile devices to estimate traffic volumes. Researchers assessed and sorted packages from these firms to identify the best match for MnDOT’s needs.

Two firms that were initially interested withdrew from the project because their products were not ready for rigorous testing. The research team then developed an agreement to work with a third firm, StreetLight, to develop and evaluate traffic volume estimates from GPS-based devices.

Researchers and StreetLight worked together to develop and evaluate traffic volume data. Investigators provided MnDOT traffic count data to the vendor for calibration of its approach, and investigators suggested several ways to enhance StreetLight’s analytics.

2017-49-p2-image
Smartphones, navigation devices and other GPS-based consumer and commercial personal devices collect data that can be used to develop traffic volume estimators.

 

The vendor developed its proprietary approach, combining GPS-based navigation data with location-based service data. The firm normalized these two data sets with U.S. Census population projections, then calibrated and scaled samples with data from 69 MnDOT permanent ATR sites. StreetLight then estimated traffic volumes for MnDOT based on 7,837 short-duration count sites.

What Did We Learn?

On multiple-tube, high-volume roadways, MnDOT expects an accuracy of over 95 percent. The correlation between AADT tube-based data and StreetLight’s data was 79 percent without calibration and scaling, and 85 percent when scaled and calibrated. GPS-linked traffic volume estimations are approaching acceptable accuracy for MnDOT, but are not yet sufficiently accurate to replace tube counting for assessing AADT.

Estimation accuracy varies heavily with traffic volume levels. At high levels of traffic, larger sample sizes of mobile devices seem to drive more accurate assessments. At over 50,000 AADT, StreetLight estimates reached mean absolute percent error levels of 34 percent. At AADT levels below 20,000, the percent error rates ballooned. At all traffic levels, GPS-based data was measured at 61 percent mean absolute percent error.

Low-volume roads and frontage roads where multiple roadways converge had to be removed from count sites for estimating AADT. Overall, some of the GPS-based data fell within 10 to 20 percent absolute percent errors, which is acceptable, but other estimates fell well outside an acceptable range, and the highest errors occurred in low-volume roadway assessments.

What’s Next?

GPS-based data offers granular information that tube counts cannot, like average annual hourly volume estimates, and origin and destination data. With improvements to analytical processes for all data, GPS-based data may provide value outside of AADT estimates.

Currently, MnDOT is evaluating origin-destination data that StreetLight is providing for use in traffic studies and planning analyses. Current research by the University of Maryland and the National Renewable Energy Laboratory is gathering data with better error rates and will be extended in Colorado, Florida and Rhode Island. MnDOT expects volume estimation from GPS-based data will continue to improve and will likely be an acceptable alternative to tube counting in a few years.

This post pertains to Report 2017-49, “Using Mobile Device Samples to Estimate Traffic Volumes,” published December 2017.

Enhanced WIM Reporting Software to Improve Commercial Traffic Weight Monitoring and Data Sharing

An update to BullConverter allows MnDOT’s statewide weigh-in-motion (WIM) system to adopt systems from more manufacturers. The BullReporter upgrade adds new reporting functions, including a View Vehicles function that provides an image of a vehicle along with a graphical representation of WIM data, such as weight and speed.

This upgrade, developed through a research study, expands the commercial traffic information that the Office of Traffic System Management can provide to the MnDOT Office of Bridges and Structures, local and state permitting agencies, the Minnesota State Patrol and other Minnesota authorities.

“With BullReporter, now we can produce daily, weekly and monthly reports of the overweight vehicles that cross over WIM sensors,” Benjamin Timerson, Transportation Data and Analysis Program Manager, MnDOT Office of Transportation System Management.

What Was the Need?

Weigh-in-motion (WIM) systems measure characteristics of individual vehicles on the road, generating records of data that include vehicle type, speed, axle weights and spacing. When a vehicle crosses WIM sensors in the pavement, it triggers electrical signals that are transmitted to a WIM controller, which converts the signals into usable WIM vehicle data. A number of manufacturers produce WIM sensors and controllers, and each vendor employs its own methods of processing signals and producing proprietary WIM data.

Image of WIM Controller
Load sensors and loop detectors in each lane of traffic are connected to a WIM controller in a cabinet that also houses a communication device. A centralized server connects to each field WIM controller and downloads daily WIM data files, which are then processed through BullConverter/ BullReporter.

In 2009, MnDOT began using BullConverter/BullReporter (BC/BR) software with heterogeneous WIM systems. BC converts incompatible, proprietary data into a uniform comma-separated values (CSV) format. BR generates reports from the converted CSV data, allowing the analysis of WIM data over different systems.

Currently, MnDOT’s Office of Transportation System Management (OTSM) uses WIM systems from International Road Dynamics (IRD), but recently began evaluating systems from Kistler and Intercomp. In a current study, investigators are evaluating the use of Intercomp WIM controllers with Intercomp sensors, IRD controllers with Kistler sensors, and Kistler controllers with Kistler sensors. These new WIM system combinations require new conversion functions in BC.

What Was Our Goal?

The goal of this project was to upgrade the BC/BR software package by improving  existing functions and incorporating new functions that will convert Intercomp and Kistler formats to the Bull-CSV format and refine export functions in BC. MnDOT also wanted to expand data reporting capabilities and analytical options in BR, including a View Vehicles capability for analyzing individual vehicles.

What Did We Implement?

MnDOT funded enhancements to the BC/BR software package to include Kistler and Intercomp formats and develop new data retrieval, statistical assessments and report generation applications, including View Vehicles.

How Did We Do It?

MnDOT provided the original BC/BR developer with a detailed list of enhancements and new conversion and reporting functions. The team developed a new WIM data downloading tool for Kistler controllers that would connect the controllers through the Internet and download and archive the raw data. Developers added two new conversion functions in BC to support conversion from Kistler and Intercomp formatted data to CSV-formatted data. The team also updated the export function in BC.

Image of View Vehicles Report display
The View Vehicles report displays on-screen images of vehicles along with WIM data in graphics that include vehicle class, GVW, speed and ESAL.

The software team then added View Vehicles report, a new reporting function, to BR. View Vehicles allows queries of vehicle records under any combination of parameters, including lane numbers, date and hour ranges, class numbers, gross vehicle weight (GVW), speed range, axle weight ranges and warning flags. Retrieved vehicle data are then displayed in web or PDF formats with a digital photo of the vehicle and graphics of selected WIM parameters.

The team added histogram functions for GVW and equivalent single-axle load (ESAL), which would retrieve a set of vehicle data based on user-selected parameters and then plot a graph or produce a spreadsheet. Developers enhanced a few other elements of BC/BR, wrote a manual for editing classification schemes and trained OTSM staff on the editing procedures.

What Was the Impact?

Deploying the updated BC/BR software package has significantly helped MnDOT and other state agencies. OTSM now can produce many different reports with a range of user-selectable data queries that can be customized to share with the MnDOT Office of Bridges and Structures, the Minnesota State Patrol and overweight permitting offices.

Expanded GVW and ESAL data generated with the updated software can be used in evaluating designs for new bridge construction. Permitting offices can draw upon BR reports to request changed axle configurations of overweight vehicles to prevent bridge damage. OTSM can also provide reports and vehicle images for compliance activities to the MnDOT Bridge Office, permitting offices and the State Patrol.

In addition, the updated BC/BR can provide data on traffic volume and vehicle class to the Office of Traffic Safety and Technology, can inform design decisions by the Office of Materials and Road Research, and can offer a wide range of useful information to the Office of Freight and Commercial Vehicle Operations.

“This software allows us to use different WIM systems and generate reports and analysis by integrating incompatible systems. We added more capabilities in BullConverter and increased BullReporter functions from 40 to more than 60,” Taek Kwon, Professor, University of Minnesota Duluth Department of Electrical Engineering.

What’s Next?

BC and BR are now fully updated for current needs and are in use by OTSM. The upgraded software will be used until industry changes or new analytical needs arise at MnDOT.

This posting pertains to Report 2017-34, “Enhanced Capabilities of BullReporter and BullConverter,” published September 2017. The full report can be accessed at mndot.gov/research/reports/2017/201734.pdf.

Investment in Transportation is Linked to Job Creation in Minnesota Counties

A new study by the Local Road Research Board (LRRB) shows that transportation investments within a county can increase the local employment rate, while investments in trunk highways surrounding a county can also enhance county and regional employment.

The goal of this project was to quantify the relationship between transportation investment and economic development as it is represented in data showing the effect of  the investment on job creation in counties.

“The entire project was new and useful. It provided answers to questions about the benefits for counties building local roads, beyond getting traffic from here to there,”
said Bruce Hasbargen, County Engineer, Beltrami County.

Background

As federal resources for transportation development have declined, state departments of transportation and local organizations have needed to be selective in funding transportation projects, choosing those that generate the greatest local return on investment.

Transportation engineers and planners understand the positive effects new roadway projects have on local and regional economies. But to demonstrate these effects to elected officials who develop the budgets—as well as to the tax-paying public— they have needed supporting quantitative data.

Previous LRRB research has produced data linking transportation investments to increases in local property values in Minnesota counties. More analysis and information were required about the possible links between local transportation investment and other economic indicators, such as job creation.

What Did We Do?

After an initial literature search, researchers followed the methodology of the earlier study by gathering and examining data from several sources. The Minnesota County Finances Report yielded investment information. Since 1985, this report has collected information about grants and expenditures for county-managed local roads. MnDOT’s Trunk Highway Construction and Maintenance Costs provided data related to these expenditures collected from 1995 to 2012.

To determine transportation investment effects on job creation and employment, researchers used several comprehensive data sources to measure employment across the state and in counties: the Quarterly Census of Employment and Wages (which reports overall employment); County Business Patterns (which reports private employment only, based on business register data); and data from the Minnesota Department of  employment and Economic Development.

Researchers combined data on transportation investment, business patterns and socioeconomic conditions in Minnesota counties from 1995 to 2010. The data included the number of county business establishments, jobs in Minnesota counties by sectors and the amount of the annual payroll. Investigators also examined spatial (GIS-map based) data from counties.

By linking the data of county business patterns to expenditures on local roads and trunk highways, researchers performed statistical analyses and created an econometric model to address these questions:

• How does transportation investment affect the employment rate, aggregate employment (number of jobs) and annual payrolls in Minnesota counties?
• Which type of transportation investment—trunk highways or local roads—is more effective in job creation?
• Does the link between transportation investment and job creation differ between metropolitan and rural counties?

The model’s design controlled for unrelated factors that would affect employment rates, including population, age structure, population density, educational attainment and level of urbanization.

What Did We Learn?

The literature search showed evidence of connections between transportation projects and local economic development across many decades and countries, although the results were varied and not predictive.

The data analysis found that long-term transportation investments contribute to employment in Minnesota counties, including several positive and statistically significant relationships:

• A 1 percent increase in local road capital within a county is associated with a 0.007 percent increase in the employment rate in the county, holding constant various socioeconomic factors.
• A 1 percent increase in trunk highway capital in surrounding areas is associated with a 0.008 percent increase in the employment rate of a county, again holding constant various socioeconomic factors.

The impacts are significant but not substantial, which researchers say may be explained by the fact that most Minnesota counties are rural and already have relatively high employment rates. Moreover, not all areas are positively affected by these investments.

The overall findings are largely driven by rural areas, while the evidence for metropolitan and micropolitan areas is mixed.

The results suggest that in Minnesota it would be more effective to invest in rural areas compared to urban areas as far as employment growth is concerned.

“As federal transportation money decreases, state and local agencies must make difficult policy decisions with diminishing budgets. This research provides quantifiable data about the local and regional benefits of new roads, which agencies can use to promote and support transportation projects,” said Zhirong (Jerry) Zhao, Associate Professor, University of Minnesota, Humphrey School of Public Affairs.

Image of trunk highway 61
Investments in trunk highways, such as Trunk Highway 61 in northeast Minnesota, are associated with employment rate increases in the counties where improvements are built, as well as regional benefits.

What’s Next?

The results of this project provide an internal decision-making tool for local agencies. They also offer quantitative data in support of transportation investment to convey to elected officials and the tax-paying public. Although no follow-up research is currently planned, many further studies of this type are feasible. For example, studies could evaluate associative effects of transportation investment on other socioeconomic factors, such as sales tax bases, small business development, workforce specialization and public education.

This post pertains to LRRB-produced Report 2018-04, “Transportation Investment and Job Creation in Minnesota Counties,” published January 2018. The full report can be accessed at mndot.gov/research/reports/2018/201804.pdf.

Developing a Uniform Process for Quantifying Research Benefits

Researchers worked with MnDOT technical experts to develop a method for identifying the financial and other benefits of MnDOT research projects. They developed a seven-step process for quantifying benefits and applied the process to 11 recent MnDOT research projects. Results showed that these projects were yielding significant financial benefits.

“We have very high expectations for the research dollars we spend,” said Hafiz Munir, Research Management Engineer. “MnDOT Research Services & Library. Following this project, we now ask investigators to tell us upfront what benefits their research could achieve, and we have improved our internal process for tracking and assessing the quantifiable benefits.”

“A lack of before-research data on the transportation activities being studied may be the biggest challenge to quantifying the benefits of research on Minnesota transportation needs. Other states are also trying to do this, but they use informal or ad hoc processes,” said Howard Preston, Senior Transportation Engineer, CH2M Hill.

What Was the Need?

MnDOT Research Services & Library manages more than $10 million in research each year, with 230 active projects covering everything transportation-related — from subgrade soils to driver psychology. Communicating the value of these research investments is an important component of transparency in government, a core interest in Minnesota.

Quantifying the benefits of research projects that lead to innovations such as new and improved materials, methods and specifications is important to MnDOT and its customers. However, because MnDOT conducts such a wide variety of research projects, it is challenging to assess the benefits that will, when applied in practice, result in quantifiable savings of time, materials or labor, or that will lead to safer roads and fewer traffic crashes.

What Was Our Goal?

MnDOT undertook this project to develop a more systematic method for identifying and measuring the financial and other benefits of its research in relation to the costs. The goal was to develop an accessible, easily applicable process that could be pilot-tested on a selection of MnDOT research projects from recent years.

What Did We Do?

MnDOT provided researchers with documents about benefits quantification practices to review and with the results of a survey of state departments of transportation on their approaches to quantifying research benefits. This review identified few states that had developed formal guidelines for assessing research benefits, and none were easily applicable to MnDOT procedures.

After reviewing the findings and consulting with MnDOT technical experts, investigators recognized that any procedure for quantifying benefits should be rooted in current MnDOT research processes. Researchers worked with a number of MnDOT offices to identify research projects that were suitable for assessing financial and other benefits from research results.

In addition to identifying projects for benefits analysis, investigators and MnDOT identified categories of benefits and developed a seven-step process for gathering and organizing cost data for various project types, applying a benefits assessment process and comparing benefits to research cost.

What Did We Learn?

The research team performed benefit-cost assessments for 11 projects. Six of the assessments had high confidence levels. One challenge in developing a uniform process included refining the complex range of cost input categories, input data options and research objectives associated with the research projects. Assembling and organizing before-research data, even for fairly simple maintenance activities, proved particularly challenging and impeded the development of benefits assessment processes.

Investigators developed a user guide, a training presentation and a quantification tool — a complex set of spreadsheets for inputting data and calculating comparative benefits. The quantification tool should eventually develop into a user-friendly software package or Web interface.

SAFL baffle
The SAFL baffle was developed in a MnDOT research project for $257,000. Researchers determined that its use across Minnesota would save taxpayers $8.5 million over three years.

Based on the analysis of cost and savings data, the 11 research projects showed significant benefits. In one 2012 project, investigators developed an inexpensive baffle that is inserted into stormwater sumps and slows the flow of water in and out, allowing more contaminated sediment to settle rather than being carried into streams and lakes. Re-search to develop the baffle, at the University of Minnesota St. Anthony Falls Laboratory (SAFL), cost $257,000. The cost to purchase and install the baffle is about $4,000 in Minnesota compared to $25,000 for more traditional stormwater mitigation solutions. Use of SAFL baffles in Minnesota is projected to save the state about $8.5 million in equipment, installation and environmental costs over a three-year period.

In total, the research cost of $1.98 million for the 11 projects analyzed is expected to save an estimated $68.6 million for MnDOT and Minnesota cities and counties over a three-year period, for a benefit-to-cost ratio of about 34-to-1. The expected savings will be enough to pay for the research budget for six or seven years.

What’s Next?

MnDOT has added quantification-of-benefits elements to its research proposal evaluation process, and since late 2015 has asked potential principal investigators to supply information on the current costs of the activities they propose to study and improve.

Since 2016, research project awards have included a request that investigators develop quantifiable data resulting from their research activity. The awards offer additional funds for that work. Investigators now provide a brief memorandum within the first 90 days of the project describing how they will quantify benefits, and in some cases presenting preliminary data. At the end of the project, these investigators describe their quantification process and results. MnDOT has tracked this information in a database, finding that about three out of every four projects show potential to yield quantifiable benefits.


This post pertains to Report 2017-13, “Development of a Process for Quantifying the Benefits of Research,” published July 2017.