Category Archives: Traffic and Safety

Collaboration with Minnesota Zoo aims to conserve wildlife

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.

Blanding’s and wood turtles are listed as threatened species by the Minnesota Department of Natural Resources.

A proactive approach to wildlife conservation will hopefully reduce small animal-vehicle collisions and prevent these species from becoming listed under the federal Endangered Species Act.

Reducing small animal collisions also improves driver safety. Hitting (or trying to avoid) a turtle on the road can cause significant damage and injuries to motorcyclists and bicyclists. It can also be unsafe for drivers to attempt to pull over and assist small animals across the road—especially in high-traffic areas.

Exclusion fences were installed along four Minnesota roadways this past year (Waconia, Highway 5, Scandia, Highways 97 and 7, Eagan, Highway 2) and will be evaluated over the next year.

The research project started in Sept. 2017 and is estimated to be completed in June 2021. The end goal is to develop a standard set of designs and recommendations for future installation along other Minnesota highways. Christopher Smith, MnDOT’s wildlife ecologist is the technical liaison leading this project.

Visit the MnDOT Office of Research & Innovation for project updates.

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.

What Was the Need?

Over one-quarter of fatal highway crashes occur at horizontal curves. In Minnesota, these areas are a contributing secondary factor in 49 percent of fatal crashes. Each year, accidents on two-lane, two-way highway curves injure over 4,000 people and result in 70 deaths, almost one-fifth of annual roadway fatalities in the state.

Research has shown that dynamic message signs with speed detection components work well in warning drivers to reduce speeds, but these systems require power supplies and cost approximately $14,000 per site. Minnesota’s Otter Tail County alone has over 400 reduced speed curve sites. The Local Road Research Board (LRRB) and MnDOT have been funding research that examines alternative approaches to speed warning systems for drivers approaching curves.

“This smartphone application stitches together existing technologies for GPS, GIS and mapping to provide an inexpensive, cloud-based warning system for drivers,” said Richard West, public works director, Otter Tail County.

In a 2015 study, researchers developed an in-vehicle, vibration-based warning system tested in a driving simulator that relies on data from highway sensors and other sources. Research in 2018 focused on the use of GPS signals to calculate and recalculate a vehicle’s trajectory on roadways to issue warnings. A new phase of this study is refining the approach to draw on vehicle-to-vehicle data.   

What Was Our Goal?

The goal of this research was to develop a dynamic curve speed warning system that would employ cloud-based data sharing. The system would not require significant infrastructure investment and would be applicable to all reduced speed curves in the MnDOT highway system.

What Did We Do?

Following a literature search, researchers focused on developing a proof-of-concept smartphone app that would warn drivers of upcoming curves and speed reduction requirements. They also created a database for county road agency managers to input curve locations within their jurisdictions, speed limits and sign facing direction for use with the smartphone app.

Researchers layered the database into their geographic roadway inventory tool, which draws on GPS and mapping data, and combined data from the sources into a cloud-based curve database. Then they developed a geofence system that triggers alerts as the tracking device crosses virtual geographic boundaries.

A smartphone app uses GPS and GIS to trigger a warning via the cloud to smartphone users traveling above the curve speed limit as they pass through a geofence, or virtual geographic boundary, before the curve.

An illustration of the system overview that includes a curved road with geofencing overlays marking two warning areas on the curve, a cloud-based warning database and a smartphone screen showing a “Reduce speed” message.
A smartphone app uses GPS and GIS to trigger a warning via the cloud to smartphone users traveling above the curve speed limit as they pass through a geofence, or virtual geographic boundary, before the curve.

The curve speed warning system was tested on roadways in Otter Tail and Pope counties. After county agencies input curve location and speed limit data into the system, researchers tested the system by running the app while driving a number of highways selected for a high density of reduced speed curves. They adjusted the system based on these field tests to accommodate GPS signal speed, travel speed and cloud data transfer bandwidth.  

Researchers then evaluated roadside dynamic speed warning system safety impacts to determine the potential safety and cost benefits from the cloud-based warning system.

What Did We Learn?

The curve speed warning system worked in proof of concept. GPS and cloud data can be drawn on fast enough to provide warnings in time for drivers to respond. Researchers refined geofencing parameters to only pull data for curves within 30 miles of the vehicle to keep data volumes to manageable levels within standard parameters for mobile phone data packages.

“Study results show that this system works accurately. If data from county and state roads were input, the application could be made available to everybody,” said Bradley Wentz, program director, Advanced Traffic Analysis Center, Upper Great Plains Transportation Institute, North Dakota State University.

The core of the system is the curve database, which requires accurate data input by county road agencies. Testing resulted in one performance error, which was traced to incorrect data for the facing direction of a warning sign.

Image of the secondary warning on a smartphone screen showing the message “Slow Down,” the recommended speed of 45 mph and the driver’s current speed of 51 mph in a red circle.
The smartphone app sends a second warning with this message and an audible signal to a driver’s phone.

When vehicles are traveling faster than the speed limit for an upcoming curve, the smartphone app issues a silent, on-screen warning of the approaching curve and speed limit. If the vehicle does not slow its speed sufficiently, the app flashes and issues another warning with an audible signal. 

The smartphone app sends a second warning with this message and an audible signal to a driver’s phone.

Safety implications may match crash and speed reductions identified in research on the safety benefits of dynamic sign warning systems. Researchers believe the cost to maintain the software and warning database roughly matches the cost to maintain a traditional dynamic speed warning sign system. But using a single cloud-based system for the entire roadway inventory offers a dramatic cost savings over installing expensive warning sign systems at every curve.

What’s Next?

Researchers have prepared presentations for local audiences and presented findings at the 2018 National Rural ITS Conference. County road agencies can easily update the database, and the system can accommodate not just reduced speed curve locations, but any reduced speed needs, such as seasonal bumps and cracks in pavement, work zones, special events and controlled intersections.

This post pertains to Report 2019-19, “Cloud-Based Dynamic Warning System,” published June 2019. For more information, visit the research project page.

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.

Speed Notification System Warns Drivers Approaching Urban Work Zones

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. 
A variable message sign, part of the SWZSN, displays the warning “Slow Traffic Ahead” in a construction zone.
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.

This Technical Summary pertains to Report 2019-21, “Evaluation of the Smart Work Zone Speed Notification System,” published June 2019. Visit the MnDOT research project page for more information.

Evaluating the Use of Central Traffic Signal Control Systems

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. 

A bird’s-eye view of a diverging diamond interchange in Bloomington, Minnesota. Two diamond-shaped formations of many converging and diverging lanes of traffic are seen on either side of a multilane highway.
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. 

A four-sided traffic signal hangs from a pole over an intersection. The traffic light illuminated on the visible side is red.
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.

This Technical Summary pertains to Report 2019-14, “Evaluation of a Central Traffic Signal System and Best Practices for Implementation,” published March 2019.  Visit the MnDOT research project page for more information.

Preparing Roads for Connected and Autonomous Vehicles

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. 
  • High-Resolution Mapping. Consider developing high-resolution mapping capabilities.
U.S. DOT image shows current work zone warning signals that may be adopted in connected and autonomous vehicles.
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.

This Technical Summary pertains to Report 2019-18, “Preparing Local Agencies for the Future of Connected and Autonomous Vehicles,” published May 2019. Visit the MnDOT research project page for more information.

New project: Effectiveness of Teenage Driver Support System

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.

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

New Project: Extreme Flood Risks to Minnesota Bridges and Culverts

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.

Affordable GPS-Based System Warns Drivers About Lane Departures, Approaching Curves

Researchers have developed an affordable camera-free curve and lane departure warning system that relies on consumer-level GPS, rather than sophisticated, expensive digital maps.

The technology uses cumulative driving trajectory data from GPS points detected every 100 milliseconds to predict driving path trajectories and compare these to mapped curves and lanes. With further development, the system can be used as an inexpensive smartphone app or retail device to warn drivers of lane drift and approaching curves.

“The goal of the project is to reduce lane departure crashes. We viewed this as a seed project and demonstrated that the system can be successful,” said Victor Lund, Traffic Engineer, St. Louis County.

What Was Our Goal?

The Minnesota Local Road Research Board sought research to develop a camera-free curve and lane departure warning system that uses consumer-level GPS capability without reliance on sophisticated, expensive digital maps.

What Was the Need?

Lane departures and run-off-road crashes cause more fatalities and serious injuries in Minnesota than any other accident type.

Many current warning technologies rely on cameras that identify lane position based on pavement markings. In inclement weather, stripes and pavement markings can be difficult or impossible to identify; markings also wear off over time, reducing visibility even in clear conditions. Camera-based lane departure warning systems are also expensive and generally restricted to newer luxury vehicles, making them inaccessible to the general driving public.

Though in-vehicle technology for the public usually falls outside the research interests of the Minnesota Department of Transportation and the Minnesota Local Road Research Board, the agencies have been funding development of lane departure warning technologies to improve driver safety. GPS technologies offer an intriguing path to consumer-level lane departure warning systems.

High-level GPS can be accurate to the centimeter level, but access is restricted and use is expensive. These systems also rely on accurate, lane-level roadway mapping, an elusive data set with high access costs.

What Did We Do?

Researchers began with a literature search of the uses of standard GPS receivers in lane departure and navigation. The research team then developed an algorithm for travel direction that uses standard GPS in a straight road lane departure system to determine driving trajectories at accuracy levels suited to safe driving needs.

Investigators adapted a publicly available digital mapping platform to the same algorithm to identify navigational points along curves and develop the curve lane departure warning system. The team enhanced standard safe distance methods to consider driver reaction time in determining when approach warnings should be issued.

Researchers then brought the two developmental stages of the system together with a warning system that identifies vehicle speed, curvature characteristics and safe speed limits, and calculates distance for driver response times to issue an audible warning to drivers on lane drift and a text warning of when and how much to reduce speed as the vehicle approaches a curve.

Two figures, each with a photo of a road segment an a graph that plots roadway curve distances with warning times.
The advanced curve warning system issued audible lane departure warnings when cumulative trajectories showed lateral drift within a curve.

For project testing and demonstration, investigators programmed the algorithm into a device with a built-in GPS receiver, connected it to a laptop for messaging and conducted driving tests on Rice Lake Road and on Interstate 35 near Duluth.

“From a technical point of view, this approach works. We developed a warning system with standard GPS that everyone has in a phone or vehicle. This is a lifesaving technology in a sense,” said Imran Hayee, Professor, University of Minnesota Duluth Department of Electrical Engineering.

What Did We Learn?

Finding no research on development of consumer-grade GPS for lane departure purposes, the research team adapted previous work on the relative accuracy of GPS readings from a MnDOT study on wearable GPS for work zone safety.

Researchers adapted a consumer-level GPS device to acquire data at 10-hertz frequency, which yields a GPS position point of 2.7 meters if a vehicle is driven at 60 mph.

The system calculates lane trajectory from cumulative readings and detects turns or drift. The curve warning system plots trajectories and compares these with open-source digital maps with road-level (rather than lane-level) accuracy to anticipate curves.

Illustrations show how the warning system uses shape points from maps with driving path averages to determine lane departures.

In road testing, the system issued audio warnings for every one of the approximately 200 lane changes, including curves. For curve warnings, the system scanned for curves at least half a mile ahead and calculated the vehicle’s speed and the distance to a curve to issue a timely text warning of the curve ahead and an advisory speed limit. Additional messages were issued when the vehicle was on the curve and when the curve had ended.

False alarms—warnings issued when the vehicle was not departing its lane—occurred in 10 percent of the tests, usually on sharp curves. Further adjustment of the algorithm and additional testing reduced false alarms significantly as the system accumulated data over multiple uses of the same roadway.

What’s Next?

Investigators filed a patent for the technology and will continue to develop the system. Further refinement of reference road direction information will improve accuracy and safety; the research team has developed a new project to employ vehicle-to-vehicle dedicated short-range communication technology to expand road direction reference data. The system will then need to be adapted for a consumer-level device or a smartphone app for use in any vehicle.

This post pertains to the LRRB-produced Report 2018-34, “Development and Demonstration of a Cost-Effective In-Vehicle Lane Departure and Advanced Curve Speed Warning System,” published December 2018.

 

New System Measures Travel-Time Reliability to Reduce Traffic Delays

Researchers for the Minnesota Department of Transportation have developed a new travel-time reliability measurement system that automates the process of gathering and managing data from multiple sources, including traffic, weather and accident databases, to generate travel-time reliability measures and reports for the metropolitan freeway network.

What Was the Need?

Improving traffic efficiency has become a key goal of traffic operations managers. In heavy traffic periods, MnDOT’s Regional Transportation Management Center (RTMC) coordinates with Minnesota State Patrol and MnDOT Maintenance Services to detect and quickly respond to freeway incidents in the Twin Cities. The RTMC works with the Freeway Incident Response Safety Team to assist and remove stranded vehicles using MnDOT emergency road service trucks. RTMC also updates real-time road condition information on its 511 traveler information system.

Overhead view of RTMC operator monitoring multiple screens
RTMC engineers use travel-time reliability data to plan for and respond to accidents, event traffic,
bad weather and road construction that cause freeway congestion.

MnDOT and RTMC measure delay and congestion on the metropolitan freeway system, reporting the data in annual reports like the 2017 Congestion Report. While useful, this data offers little predictive value on its own. MnDOT’s metropolitan freeway system features 4,000 loop detectors that transmit traffic data every 30 seconds; this data informs the congestion and delay reports.

Correlating this data with locations on the freeway system and various operating conditions, such as weather and traffic incidents, is time- consuming. But the data could be used to systematically evaluate traffic delays and develop strategies to mitigate congestion.

What Was Our Goal?

In this project, investigators sought to develop a system for automatically accessing weather, crash and traffic data to assess travel-time reliability—the variability in travel times for any given route. Travel-time reliability measures are becoming the key indicators for transportation system operations and management.

What Did We Implement?

Investigators developed a new travel-time reliability measurement system (TTRMS) that integrates different types of data (such as weather, traffic, incident, work zone and special event) acquired from multiple sources and automatically produces various types of travel-time reliability measures for selected corridors following user-specified operating conditions and time periods.

“Travel-time reliability is another way of looking at congestion and at strategies for making it more tolerable. It used to take several hours, even days, to process travel-time reliability data. The TTRMS processes it in minutes,” said Brian Kary, Director, MnDOT Regional Transportation Management Center.

How Did We Do It?

Investigators began by developing a detailed design of the TTRMS architecture—its modules, their functions and their interactions. The team then developed a work-zone data input module, where detailed lane configurations of a given work zone can be specified.

Developers designed a travel-time reliability calculation module as the core of the new system that can automatically access MnDOT’s traffic data archive, its incident database and the National Oceanic and Atmospheric Administration’s weather database. It can also accept a set of input data for work zones, such as lane-closure periods and locations. The reliability calculation module was then integrated with user interfaces and reporting modules. Finally the integrated system was tested with the real data gathered in 2012 and 2013 from Interstates 35E and 35W, U.S. Highway 169 and State Highway 100.

What Was the Impact?

The system generated accurate travel-time reliability measures for the test periods and given operating conditions. In particular, the output measures were automatically generated in both table and graphical formats, thus saving traffic engineers significant amount of time and effort.

The TTRMS includes map-based interfaces, which provide administrators and general users with substantial flexibility in defining corridors, specifying operating conditions and selecting types of measures depending on the purposes of applications.

To test the new system’s performance, the research team used the TTRMS to evaluate traffic strategies deployed for the February 2018 Super Bowl in Minneapolis. Two weeks before the event, reliability was low for the freeway system serving the football stadium. During the week of the Super Bowl, MnDOT and the Department of Public Safety aggressively managed traffic incidents to keep traffic moving, and reliability rose substantially despite the increase in tourist traffic. In the days immediately after the Super Bowl, operational strategies returned to normal levels, and reliability fell to previous levels. Results suggest that aggressive incident management during this exceptionally high-volume regional event enhanced traffic efficiency.

What’s Next?

Further enhancements to the TTRMS should include automating inputs for work zone data, such as lane closures, changes in work zone locations and time periods. Future research could help traffic operations prioritize resources and develop short-term and long-term freeway improvements, including studies of bottlenecks and the freeway network’s vulnerability and resilience for natural events and large-scale incidents.

This post pertains to Report 2018-28, “Development of a Travel-Time Reliability Measurement System,” published September 2018.