Positive offset left-turn lanes are one solution to improving left-turning motorists’ visibility of opposing through and right-turning traffic.
MnDOT is revising its Road Design Manual and seeks to incorporate more information, policies and design guidance regarding positive offset left-turn lanes.
Researchers from the University of Wisconsin’s Traffic Operations and Safety Laboratory reviewed safety performance data from research that examined left-turn offsets. They also consulted 23 state DOT road design guides to understand the extent of available guidance.
According to recent survey results, new highway signs promoting rest area amenities are influencing motorists’ decisions to use them.
Among the 947 respondents using an electronic customer feedback system, 33 percent said they had seen the signs and 29 percent were not sure if they had. Of these two groups, 27 percent indicated the signs influenced their decision to stop and 61 percent described the signs as helpful.
These visitors had the opportunity to take a quick survey via QR code displayed on door decals, stand signs and flyers at the rest areas.
“No other states have installed advance rest area signage that list amenities available at upcoming rest areas,” said Rob Williams, MnDOT’s Safety Rest Area program manager. “We believe this is a cost-effective way to entice people off the road for breaks.”
MnDOT began a two-year pilot project in 2015 to implement findings from its 2009 Rest Area Amenities Study, which suggested that more detailed signage about rest area amenities could encourage motorists to pull off and take a break – which could save lives. According to the National Highway Traffic Safety Administration, drowsy driving-related crashes resulted in 795 deaths in 2017. Williams applied for research implementation funds to install 36 signs advertising the amenities ahead of 21 rest areas along Interstate 35 and I-94, as well as at the Brainerd Lakes Area Welcome Center.
Safety rest areas are one tool to keep drivers safe by giving them a place to stop, rest and refresh. MnDOT operates 51 Class I rest areas throughout the state, but not all rest areas offer the same amenities. Depending on the traveler, it may be a family restroom, fenced dog park, or children’s play area that best serves their needs.
MnDOT’s Rest Area Program is continuously working to improve rest areas to better serve travelers and reduce driver fatigue accidents.
“Our rest areas provide an opportunity to directly interface with Minnesotans and visitors traveling through our state, and we want to provide them with the best possible experience,” Williams said.
Ongoing enhancements to our rest areas include improving safety, accessibility and sustainability by:
Increasing visibility in rest area lobbies and installing video recording systems to improve visitor safety
Improving accessibility and introducing family restrooms
Increasing sustainability by use of native vegetation, installing electric vehicle charging stations, using materials with lower life-cycle costs and, in some cases, developing green roofs
The safety rest area program will seek funds to install additional advance signage as rest areas are remodeled. To learn more about the safety rest area signage project, visit the Office of Research & Innovation.
Turtles and other wildlife are at risk along Minnesota roadways.
MnDOT is collaborating with the Minnesota Zoo on a new research
project installing small animal exclusion fencing. The fencing is
intended to redirect turtles (and other small animals) to culverts and bridges
where they can cross the road safely.
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.
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.
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.
Using an innovative method to calculate vehicle trajectories and gather large amounts of driver data, researchers tested and evaluated the new Smart Work Zone Speed Notification system and determined that its messages successfully influenced drivers to reduce their speed.
What Was the Need?
Maintenance and construction on Minnesota’s roadways often create travel disruption for drivers through traffic slowdowns and queuing. MnDOT has previously tested systems to inform drivers of traffic backups in rural work zones, but slowdowns near complex urban work zones are less predictable. Drivers traveling at highway speed may come upon congestion suddenly, resulting in abrupt braking and the risk of rear-end collisions.
Posting advisory speed limit messages near these work zones has not been effective. To address the problem, MnDOT developed a Smart Work Zone Speed Notification (SWZSN) system designed to inform drivers of the actual speed of slowed downstream traffic near large urban work zones. Researchers from the Minnesota Traffic Observatory then tested and evaluated this system over time in an actual urban highway construction work zone.
What Was Our Goal?
The primary objectives of the project were to quantify the speed notification system’s effect on drivers’ behavior and determine its impact on the safety of the work zone. With an effective work zone speed notification system, MnDOT’s goal was to create safer work zones by giving highway drivers real-time information that would influence them to slow down adequately before a congestion hazard, avoiding dangerous braking and collisions.
What Did We Do?
A variable message sign, part of the SWZSN, displays the warning “Slow Traffic Ahead” in a construction zone.
The SWZSN was designed to collect traffic speed data throughout a work zone and run it through an algorithm, generating the appropriate message for drivers on a variable message sign, such as “35 MPH 1 Mile Ahead” or “Stopped Traffic Ahead.”
MnDOT wanted to deploy the system within a project replacing 4.4 miles of Interstate 94 (I-94) east of downtown St. Paul. The construction was to be completed in stages between spring 2016 and fall 2017. This large project would include many lane closures and expected traffic congestion; the new system could mitigate some of the traffic disruption. The deployment and evaluation of the SWZSN took place in three phases:
Pre-SWZSN deployment (mid-2016): Gathering data from the work zone before the system was deployed to obtain data demonstrating drivers’ behavior and crash frequency data without the new system.
Post-SWZSN Phase I deployment (2016 into 2017): Gathering the same data from the work zone after the SWZSN was deployed.
Post-SWZSN Phase II deployment (the entire 2017 work season): Gathering data from the work zone after initial deployment, with troubleshooting and improvements of algorithms and messages in place, showing drivers’ behavior and work zone crash frequency with the improved SWZSN and the analyses of these data.
Speed detection sensors were installed on poles every half-mile through a new highway construction work zone.
To collect data for the system, MnDOT’s Regional Transportation Management Center mounted Wavetronix speed detection sensors on poles every half-mile in the work zone, replacing old loop detectors. Researchers also deployed nine solar-powered cameras on mobile trailers about every half-mile. This allowed researchers to capture traffic flow images in more strategic locations where traffic queues were forming since the construction zones were complex, crowded and often changing, with many visual obstructions. Data were transferred primarily via an arranged wireless radio link to the Minnesota Traffic Observatory.
Researchers then applied their own innovative methodology—a Trajectory Extraction Tool (TET)—to the traffic images captured by the cameras using video alone to calculate a vehicle’s deceleration rate when approaching traffic congestion. The cameras were positioned to optimize TET performance. Researchers gathered tens of thousands of data points for analysis from traffic in the work zone over the course of the project.
What Did We Learn?
During the first year of SWZSN implementation, the project team identified discrepancies in the speed notification algorithm, such as unreasonable or delayed messages. By the second construction season, those anomalies were significantly reduced. The most significant results of this project showed that in situations where messages communicated to drivers were consistent and accurate, reductions of more than 30 percent in the selected deceleration rates were observed.
Most importantly, the speed notification system is clearly noticed by drivers and results in a statistically significant influence on drivers’ behavior, suggesting that downstream speed notification is an effective traffic control tool.
What’s Next?
This evaluation project was considered a success: The system is more effective than previously used work zone advisory speed limits. The SWZSN has already been deployed in two highway construction sites and will be used for future projects.
MnDOT sought to determine the full range of intersection control information (ICI) currently used in the state and how it could best be made accessible for state transportation system needs. Researchers created the Regional Database of Unified Intersection Control Information, a machine-readable, cloud-based unified ICI system. They determined steps MnDOT could take toward more effective use of its central traffic signal control system, such as mitigating traffic disruption around construction zones and participating more fully in emerging technologies such as vehicle information systems and vehicle automation.
What Was the Need?
Traffic signal control has evolved since the 1950s from simple time-based signal protocols to current dynamic systems that allow adjustment of signals to traffic conditions. Intersection control information (ICI) is increasingly important to transportation agencies, researchers and private companies involved in developing traffic models and technologies.
Historically, the availability of traffic signal control information in Minnesota and traffic data formats have varied across jurisdictions. Nationally, increased use of central traffic signal control systems (CTSCS) has supported recent trends toward more dynamic traffic models and control, as well as toward advances in automated intelligent vehicles. This quickly evolving environment makes the creation of a unified, standardized system of ICI in Minnesota both feasible and necessary.
MnDOT sought to examine the use of CTSCS to manage all aspects of traffic near construction zones more strategically and effectively in order to mitigate the frequent and often severe disruption of traffic these zones can cause. This project was initiated to determine the state of Minnesota’s ICI systems and to develop guidance for reaching MnDOT’s goal of a unified ICI and better statewide traffic management through CTSCS.
Unified ICI can identify all the parameters traffic signal controllers need to effectively manage challenging highway configurations like this diverging diamond interchange in Bloomington.
What Was Our Goal?
This project had three objectives:
Deliver guidance and tools to collect ICI from all Twin Cities metro area jurisdictions and automate the importation of this information into each jurisdiction’s CTSCS and signal performance measure (SPM) applications. A stated priority was the ability to import this data into MnDOT’s digital products used in construction design.
With the help of all stakeholders, define the most inclusive format to represent all required information.
Design a Regional Database of Unified Intersection Control Information (RDUICI), and propose methods and tools for importing and exporting data between the RDUICI and all CTSCS and SPM applications by local jurisdictions.
“A unified set of intersection control information is valuable for developing a regional signal timing database to model construction project impacts and provide standardized information for use with connected vehicle technologies.”
—Kevin Schwartz, Signal Optimization Engineer, MnDOT Metro Traffic Engineering
What Did We Do?
Researchers distributed surveys to signal operators and transportation model builders to identify the contents and develop the format of the unified ICI.
A survey sent to 153 signal professionals sought to learn how operators in diverse jurisdictions store and distribute ICI. Responses from 42 participants helped researchers assess the availability of ICI and the degree of effort a regional unified ICI would require.
A second survey was sent to 58 designers, modelers and planners who have experience working with MnDOT signal information to learn about ICI’s various uses; 25 people responded. Researchers also interviewed a selected group of signal operators and modelers to gain more detailed information.
Most of Minnesota’s traffic signals use complex controllers to manage traffic, responding to information gathered from multiple sources, such as loop detectors and other sensors.
These surveys and in-depth interviews allowed researchers to create intersection models of varying complexity to drive the identification and categorization parameters of the proposed unified ICI. Researchers developed a complete unified ICI for a diverging diamond interchange, a complex interchange that is difficult to represent with traditional intersection models. Researchers also developed a relational database schema for containing the data set in a machine-readable format. This schema is a starting point for developing a system for standardizing the management and availability of ICI across jurisdictions.
What Did We Learn?
Researchers documented all intersection signal control codes in use. They showed the feasibility of a unified ICI and demonstrated it through the example of a fully coded diverging diamond interchange. They learned that some data in older formats would need to be digitized to be included.
“Identifying the needs of different stakeholder groups allowed us to produce an organized, comprehensive format for intersection control information.”
—John Hourdos, Research Associate Professor, Minnesota Traffic Observatory, University of Minnesota
Further investigation and communications with the software developers of MaxView, MnDOT’s CTSCS, showed researchers that current systems could not be used to store the entire unified ICI. While the systems contain much of the unified ICI data set, some detailed geometric information is missing that is critical to understanding the intersection control. MaxView also contains information that is not readable by other systems.
Because of these challenges, researchers suggested managing unified ICI through a custom-built, centralized cloud repository. This solution would only require that vendors develop tools for exporting the information they have in a unified ICI format. The cloud repository would then be accessible to signal control vendors and to MnDOT, and security would remain intact.
What’s Next?
MnDOT now has the full range of intersection signal control data used across the state. Researchers have determined it can be imported, stored and delivered through a cloud-based method. With these findings, the agency can begin to consider projects that use CTSCS for construction zone disruption mitigation and intelligent vehicle technologies.
Proprietary technologies, industry competition and federal regulatory concerns are slowing the advent of defined standards for connected and autonomous vehicles (CAVs). Researchers investigated the state of CAV implementation to help local agencies begin preparing for the infrastructure needs of these vehicles. CAV-friendly options are considered for eight infrastructure categories. Since truck platooning is the likely first application of this technology, and optical cameras appear imminent as an early iteration of sensing technology, researchers suggest that wider pavement striping and well-maintained, uniform and visible signage may effectively serve the needs of CAVs in the near future while enhancing infrastructure for today’s drivers.
What Was the Need?
For transportation agencies, which manage infrastructure in time frames of decades, the potential of connected and autonomous vehicle (CAV) technology influences infrastructure upgrade plans.
New pavements and overlays, traffic signal systems and signs may serve for decades, while pavement markings face shorter life cycles. Optimizing spending today requires anticipating future infrastructure needs, and the infrastructure requirements of CAVs may differ from standards currently in place.
It remains unknown how imminent the CAV future is, and competing technologies and designs for guidance systems, sensor formats and other facets of the developing vehicle technology keep outcomes unsettled. Enthusiasm in the technology and automotive sectors for this new model of road user tools nevertheless suggests that short-term preparations warrant consideration within the current limited-budget environment for infrastructure improvements.
How local agencies can best brace their roadway systems for a CAV-driven shift in road usage remains unclear, and public transportation officials cannot predict what the technology will look like if and when autonomous vehicles roll onto streets in significant numbers.
What Was Our Goal?
Researchers sought to create a toolbox for local road agencies to use in preparing for CAVs in the next five to 10 years. Recommendations would help agencies leverage ongoing infrastructure plans and expenditures to prepare for CAVs and the potential technologies for roadway navigation and travel the vehicles will deploy.
What Did We Do?
Researchers began by studying the literature, attending conferences and consulting with industry experts to describe likely CAV technologies and potential implementation timelines. Based on this research and discussions with the project’s Technical Advisory Panel, investigators developed recommendations in eight categories of infrastructure needs. The research team also prepared seven case studies showing how road agencies have addressed different aspects of preparing for CAV fleets.
What Did We Learn?
Industry competition and proprietary technologies make CAV outcomes difficult to project, and federal standards and regulations have yet to develop to meet potential forms of the technology.
“Connected and autonomous vehicles are further away than we think. Full integration of driver assistance technologies—which is where the real power in CAVs is at this time—may be a slow process.”
—Shauna Hallmark, Professor, Iowa State University Department of Civil, Construction and Environmental Engineering
Some consensus within the CAV industry suggests truck platooning, in which two or more CAV trucks follow one another at distances of 30 to 50 feet, seems the most promising initial implementation of CAV technology within the next five to 10 years. In addition, optical cameras will be a likely early iteration of sensing technology. Accommodating these technologies will impact two infrastructure categories—pavement markings and signage. Recommendations for these infrastructure needs follow:
Pavement Markings. Consider California’s plans to install 6-inch-wide lane lines (the current Minnesota standard is 4 inches) on highways and Interstates during regular maintenance and new construction within three years.
Signing. Ensure that signs are standardized, easily visible, and not blocked, damaged or faded.
The other six infrastructure categories impacted by CAVs entail less-specific recommendations:
Traffic Signals. Create space at signal control cabinets for additional hardware related to CAV technologies.
Consistency and Standardization. Install and maintain striping, signing and signals consistent with CAV algorithms and technologies.
Pavement Maintenance. Continue to keep road surfaces well-maintained.
Data Capture and Information Sharing. Begin or continue collecting and organizing data for bridge heights, speed limits, load restrictions, crosswalks, roadway curvatures and other infrastructure characteristics.
Communication Infrastructure. In new construction and information technology infrastructure built for agency use, ensure adequate conduits for power and fiber optic cables.
Intelligent transportation system features like work zone warnings may be incorporated in CAVs.
What’s Next?
Case studies about developments in Los Angeles and in Iowa, Michigan, Ohio, Virginia and Wyoming explain how agencies are preparing for the needs of a CAV-friendly infrastructure.
“Making sure that signing and striping are visible will be essential for accommodating autonomous cars. It’s also going to be good for all drivers, especially with an aging population.”
—Douglas Fischer, Highway Engineer, Anoka County
A pilot project in Anoka County, Minnesota, informed decisions about signage to ensure visibility and consistent placement. Pavement markings were also addressed; currently the county continues to place 4-inch edge lines, lane lines and centerlines after resurfacing projects, and painting lines to 10-foot lengths at 40-foot gaps. Conversion to 6-inch markings could be accommodated on existing pavements; however, if a new standard is required for skip stripe spacing, it may only be economically feasible to do so on new surfaces.
The Minnesota Local Road Research Board (LRRB) has funded a follow-up study to determine whether a monitoring system it field tested for new drivers, called the Teen Driver Support System (TDSS), affected teenagers’ long-term driver behavior.
Background
Motor vehicle crashes are the leading cause of teen fatalities. Because of inexperience and risk-seeking propensity, new teenage drivers are more prone to behaviors such as speeding and harsh maneuvers, especially during their first few months of licensure.
In an effort to reduce risky driving among new teenage drivers, in 2011, the LRRB funded a one-year field operational test of a prototype system developed by the University of Minnesota’s ITS Institute, which enabled parents to monitor their child’s driving behavior.
The software ran on a teen’s smart phone, which was mounted to the dashboard and provided instant feedback about risky behavior to the teen and communicated to parents if the behavior continued.
The system didn’t allow incoming or outgoing phone calls (except 911) or texting while driving. It provided visual and auditory warnings about speeding, excessive maneuvers (e.g., hard braking, cornering), and stop sign violations. It also monitored seat belt usage and detected the presence of passengers, two known factors that increase the risk of fatalities among teen drivers. The system could also be programmed to monitor if the teen was driving after the curfew set by parents or required by Minnesota’s graduated license requirements.
In January 2013, the University of Minnesota launched a 300-vehicle, 12-month field operational test in Minnesota to determine the effectiveness of the TDSS in terms of its in-vehicle information and feedback to parents.
Research results indicated an overall safety benefit of TDSS, demonstrating that in-vehicle monitoring and driver alerts, coupled with parental notifications, is a meaningful intervention to reduce the frequency of risky driving behaviors that are correlated with novice teen driver crashes. In particular, the system was shown to be an effective strategy for reducing excessive speeds when used with parental feedback and potentially even without parental involvement.
Project Scope
The TDSS study was cutting-edge at the time. Today, there are many systems in the marketplace which families may seek out to provide added support for their novice teen drivers. However, the long-term effectiveness of these systems is largely unknown. Furthermore, the extent to which the TDSS reduced crashes, injuries, and citations among those who participated in the study is unknown.
This new study will collect information on study participants’ self-reported driving behaviors and driving attitudes, as well collect traffic violation and crash history records from the Minnesota Department of Public Safety.
This study proposes to not only provide a follow-up to the TDSS study to further explore the benefit it may have had on participants, but also determine to what extent families, schools, and other organizations should continue to invest in in-vehicle coaching systems similar to the TDSS. Ultimately, the TDSS is a low-cost system, which, if found to have long-term efficacy beyond what was demonstrated in the original study, could help guide cost-effective implementations to reduce crashes among teen or other driver groups.
Extreme flooding is a threat to Minnesota’s transportation infrastructure and the safety and economic vitality of its communities. A spate of recent flooding events around the state has demonstrated this and heightened the level of concern. Furthermore, climate change — a factor not traditionally accounted for in the design of the state’s infrastructure — is projected to enhance precipitation and the threat of flooding in coming decades.
Given this, MnDOT is undertaking an effort to better predict the threat flooding poses to its bridges, large culverts and pipes, which may be increasingly called upon to convey higher, more frequent flood flows than they were designed for.
The state transportation research program recently launched a two-year extreme flood vulnerability analysis study, which will develop a methodology for characterizing the vulnerability of the state’s bridges, large culverts, and pipes to flooding.
The effort builds upon the previously completed Flash Flood Vulnerability and Adaptation Assessment Pilot Project (2014), which scored bridges, large culverts, and pipes in MnDOT Districts 1 and 6 for flood vulnerability, allowing detailed assessments of adaptation options for each of their facilities to be prioritized.
This new study, which will be conducted by WSP, aims to develop and test ways to enhance the vulnerability scoring techniques used in the previous study and ensure their applicability throughout the state. Researchers will not actually undertake the statewide assessment, but specify an approach that could be used for it. They will also explore how the outputs of the analysis can be incorporated into MnDOT’s asset management systems. The results of this work will be a clear path forward for MnDOT to use for prioritizing adaptation actions — a key step towards enhancing agency resilience and maintaining good fiscal stewardship.
Project scope
The primary intent of this study is to develop a methodology for characterizing the flood vulnerability of bridges, large culverts, and pipes statewide. As part of the development process, the methodology will be tested on a limited, but diverse, set of assets across the state. Following a successful proof of concept, recommendations will be made on how the outputs (i.e., the vulnerability scores) can be incorporated into the state’s asset management systems.
By determining which facilities are most vulnerable to flooding through the techniques developed on this project, MnDOT can prioritize where adaptation measures will make the biggest impact, ultimately decreasing asset life-cycle and road user costs. Without the development of assessment techniques, adaptation measures run the risk of being implemented in a more reactive and/or ad-hoc fashion, with less regard to where the biggest “bang for the buck” can be realized.
This project will produce several technical memorandums, and is expected to be completed in early 2021.
