Using Debonded Strands to Reduce End Stress in Bridge Beams

A new MnDOT-funded research study has found that most agencies in states with weather similar to Minnesota’s use debonded strands in prestressed concrete bridge beams. MnDOT may begin piloting debonding as an alternative to draping, which manufacturers claim is time-consuming, challenging to worker safety and expensive.

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Low-Temperature Cracking Test Produces Repeatable, Reliable Results

Researchers ran a sophisticated low-temperature asphalt cracking performance test at multiple labs to study the test, its variability and repeatability, and its additional promise in studying reflective cracking susceptibility of overlays. Results put MnDOT closer to implementing test specifications for low-temperature cracking test for pavement mixes.

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Investigating Wastewater Reuse at Rest Areas and Truck Stations

Researchers have provided MnDOT with a comprehensive and practical evaluation of what the agency would need to do to develop wastewater reuse systems for its truck stations and rest areas. Two sites will install the research project’s recommended systems soon. 

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Smartphone App Alerts Drivers Exceeding Speed Limits on Curves

Researchers have developed a proof-of-concept curve speed warning system for use with mobile phones, a technology they hope car manufacturers might adopt for in-vehicle systems. The proof-of-concept system uses data from local road agencies on curve locations, speed limits and signage with geofencing to trigger cloud-based data alerts to road users driving faster than recommended speeds for curves.

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Evaluating Iron-Enhanced Swale Ditch Checks for Phosphorus Removal

Researchers documented performance of an iron-enhanced ditch check filter to remove phosphorus from stormwater over three years. The filter was effective, but its performance decreased over time, and it will require relatively frequent maintenance. Several design changes may be considered.

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New Project: Use of Innovative Technology to temporarily Deter Bat-Bridge Use Prior to and During Construction

MnDOT has funded a study to evaluate the use of non-lethal ultrasonic acoustic devices to temporarily deter bats from bridges before and during construction projects.

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Guidebook helps cities and counties choose tools for managing fleets

Snowplows and other winter maintenance vehicles clearing roads during a nighttime snowstorm.

Managing a fleet of trucks, heavy equipment, and other vehicles challenges road agencies large and small. While large agencies like MnDOT use software and specialized administrators to manage fleet management systems electronically, city and county agencies often do not. For some small agencies, fleet management may fall to a shop mechanic or two.

In a recent project from the Local Road Research Board’s Research Implementation Committee, researchers identified the fleet management needs of city and county agencies and reviewed various cost-effective tools that could help these agencies make fleet management decisions. They then developed a guidebook for local agencies that addresses the tools and methods needed to manage fleets effectively.

“The guidebook provides the benefits of fleet management, a comparison of various program features and attributes, and a contact for more information about each program,” says Guy Kohlnhofer, county engineer, Dodge County, and the project’s technical liaison.

Fleet Management Tools for Local AgenciesFleet Management Tools for Local AgenciesFleet Management Tools for Local AgenciesFleet Management Tools for Local AgenciesFleet Management Tools for Local Agencies
Fleet Management Tools for Local Agencies

The guidebook—Fleet Management Tools for Local Agencies (2017RIC01)—includes a matrix comparing the eight most widely used fleet management software tools among Minnesota agencies. Costs, equipment needs, tracking features, financial analysis applications, and other attributes are reviewed. Case studies of agencies that use spreadsheets, software, and specific fleet replacement strategies are also included.

Three approaches to fleet replacement planning are presented in the guide. “You may have a vehicle that has been driven 300,000 miles and needed little maintenance, while another vehicle has been driven 100,000 miles and has needed a lot of maintenance,” says Renae Kuehl, senior associate, SRF Consulting Group, Inc., one of the co-authors. “We provide three models to determine when you should replace each.”

One of the findings of the project is that spreadsheets are effective and widely available tools for managing fleets. They are easy to tailor to local needs and fleets, are well understood by most computer users, are part of most office software suites, and work well for small data sets. Disadvantages, however, include limitations in reporting features, easy corruptibility of data, and inconsistent data entry among users.

In contrast, fleet management software offers easy report generation; software linkage to fuel, financial, and other software systems or modules; secure and consistent data; and interagency shareability. However, these tools can be expensive. Software costs for managing fleets average almost $36 per vehicle, and annual support costs average about $18 per vehicle. Other disadvantages include the need for training and internet accessibility.

This article originally appeared in the September issue of the LTAP Technology Exchange 

Leveraging Existing Inductive Loops to Classify Highway Vehicles

Researchers evaluated the use of existing inductive loop installations in Minnesota for vehicle classification. Results showed that inductive loops may be effective at identifying and classifying individual vehicles as they pass, but the system will require further refining for Minnesota use.

What Was the Need?

MnDOT periodically counts vehicles on state highways and uses this data to plan for transportation infrastructure needs, apply for federal funding, anticipate traffic demand and potential congestion, and learn how drivers use the highway system.

Automatic traffic recorders (ATRs) and weigh-in-motion stations count and measure the size of commercial vehicles. Engineers also count total traffic, classifying vehicles by size or axle number according to the Federal Highway Administration’s (FHWA’s) system of 13 vehicle classes, which includes Class 2 for passenger cars; Class 3 for pickup trucks, some SUVs and minivans; Class 4 for buses; and Class 5 through 13 for commercial vehicles.

Vehicle classification counting usually entails manual counting or the use of pneumatic tubes stretched across vehicle lanes to record speed and the number of axles passing. Tube counts are conducted for 48 hours at each of 1,200 sites throughout the Minnesota highway system once every two years. This time-consuming, costly practice also places staff in danger. Video imagery can be used, but this also takes a considerable commitment of labor to view, analyze and record vehicles.

A 2013 U.S. DOT study in California evaluated the use of inductive loops in vehicle classification. This technology is commonly used on highways for monitoring congestion by counting vehicles and measuring speed. Inductive loops are embedded just below the pavement surface and linked to a data station nearby that records electronic signals from the metal chassis of each passing vehicle.

What Was Our Goal?

MnDOT sought to evaluate the U.S. DOT approach in a Minnesota setting that would leverage existing technology. Researchers would use the method to record, identify and classify vehicles passing over inductive loops already installed throughout the Twin
Cities’ highway system.

What Did We Do?

A camera and an inductive loop data box at the U.S. Highway 169 and Trunk Highway 282 intersection.
A camera and an inductive loop data box at the U.S. Highway 169 and Trunk Highway 282 intersection.

Following a review of the 2013 U.S. DOT study and other research, the investigative team installed video systems and new loop signature circuit cards at five test sites: two at Interstate highways, one at a major highway and two at signalized intersections. Investigators gathered data at each location for three to four weeks.

Researchers then analyzed 10 to 14 days of loop and video data from each site. For ground truth, the team identified every individual vehicle from video, then analyzed loop data in two ways. First, they compared video and individual electronic signature readings for every vehicle. Then they analyzed loop signature data in 15-minute interval aggregations to evaluate how well the system works without verification on a vehicle-by-vehicle basis.

After evaluating vehicle classes using the FHWA classification system and a second classification system, researchers presented their findings and conclusions in a final report.

What Did We Learn?

The research team reviewed over 400 hours of video and counted over 807,000 vehicles. The match rate for all 13 FHWA classes averaged 75 percent with a standard deviation of 8 percent for individual vehicle matching. The overall matching rate was biased toward Class 2 and 3 vehicles, as sedans, pickups and SUVs share similar vehicle chassis configurations and loop signature patterns.

The 15-minute aggregated method showed a tendency to undercount Class 2 vehicles and overcount Class 3 vehicles by about 13 percent of total traffic. The secondary classification system results matched the FHWA system fairly well for consumer-level vehicles and tended to undercount some commercial vehicles.

A video camera and an inductive loop data box installed on Interstate 94.

Overall, Class 2 vehicles were matched by inductive loop signatures at a rate of 81 percent accuracy, with 17 percent of passenger vehicles misclassified as Class 3 vehicles. All other vehicle classes had matching rates of less than 50 percent. California results showed an average match rate across classes of about 92 percent.

These results were disappointing. Site conditions may have been a factor, particularly at one site where damaged hardware, broken sealants and other physical conditions were suboptimal. The library of vehicle signature signals in California was used as a basis for Minnesota analysis, but the data sets may not match precisely. Agricultural needs, for example, differ between states, and heavy agricultural vehicles feature different configurations, potentially generating different electronic signatures.

“We need a little more research, which will mostly be done by our office. If we get better accuracy, we’ll be able to get data continuously rather than just 48 hours every couple years,”  said Gene Hicks, Director, Traffic Forecasting and Analysis, MnDOT Office of Transportation System Management.

The U.S. DOT study in California also used loops in circular patterns, and Minnesota’s loops are arranged in rectangular patterns. Data signal crossing, diminished signal quality and shadow data repeated on neighboring lanes may have corrupted findings.

What’s Next?

Further research will be needed before loop signature data can be used reliably in traffic analytics. Researchers suggest that the investigation can be re-evaluated by installing four loop signature cards at two permanent ATR locations with loops, pneumatic tubes and video. Circuit cards can also be updated and classification algorithms better calibrated to vehicle signature profiles.

This post pertains to the LRRB-produced Report 2018-31, “Investigating Inductive Loop Signature Technology for Statewide Vehicle Classification Counts,” published October 2018. For more information, visit MnDOT’s Office of Research & Innovation.

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