Research Reports

This Research Summary is part of the final deliverable for Research Report 2024-33, "Development and demonstration of a novel Red Light Running Warning System using connected v2i technology.'

Running red traffic signals is a major cause of traffic collisions and resulting injuries and fatalities. Despite extensive prior work on systems to reduce red light violations, they continue to be a major problem in practice, partly because existing systems suffer from the flaw of providing the same guidance to all drivers. As a result, some violations are avoided, but other drivers ignore or respond inappropriately to red light running systems, resulting in safety issues overall. We present a novel method of providing accurate warnings to individual drivers to avoid the broad guidance approach of most existing systems. Recognizing if a driver will run red lights is highly dependent on signal phase and timing, traffic conditions along the road, and individual driver behavior, the proposed warning system contains three parts: a traffic prediction algorithm, an individual warning signal optimizer, and a driver warning display. The traffic prediction algorithm predicts future traffic states along the road towards the signalized intersections using the latest traffic conditions obtained through vehicle-to-vehicle and vehicle-to-infrastructure communications. Then, an optimization problem is formulated to compute the optimal warning signal based on predicted traffic states and driver reaction model. Finally, the optimal warning signal is shown on the display screen to advise driver on how much braking is needed to avoid running the red light. The results of both simulated driving scenarios and real-world road tests show that the proposed system provides more effective and accurate warning signals to drivers, helping them avoid running red lights.

Managing MnDOT’s network of roadways to a satisfactory level of performance requires pavement investment selection methods and activities that consider performing the “right fix” at the “right place” at the “right time” and the “right way.” Although these investment selections consider a broad assortment of investment methods and activities beyond traditional maintenance, maintenance investments and activities play a significant role in roadway pavement performance, often affected by the availability of localized and regionalized resources. To optimize roadway pavement maintenance investment methods and activities, MNDOT Maintenance has determined that the Smooth Roads Project Management Team should be re-established to address this topic.

To support snowplow operators in low-visibility conditions, a snowplow driver-assist system was developed to provide the driver with lane guidance and forward obstacle detection feedback. The guidance system used a real-time kinematic global navigation satellite system receiver and high accuracy digital maps of the roadway to communicate lateral positions of the snowplow to the driver.

The Passive Pedestrian Detection Analysis project reviewed a variety of commercially available passive detection systems. Vendors that were selected for this study included: • Flir, Miovision, Econolite, Gridsmart, and Iteris After vendor selection, the project went through the following phases: • Ground-truth Testing: o Verified the rate at which the systems accurately recognized a pedestrian at the intersection. • Pushbutton Compliance Testing: o Determined the rate at which pedestrians activated the pushbuttons when they intended to cross the street. o Verified the accuracy of each system compared to pushbutton compliance. • Vendor Result Summarization

This project focused on utilizing high-resolution signal data, crash data, and volume data to analyze safety impacts associated with the three left-turn phasing modes that flashing yellow arrows could operate in and determine when the various modes should operate. The three modes were as follows: • Protected only. • Protected-permissive. • Permissive only. The project team needed to analyze the data in two different methods to better understand the results. The analysis was completed for 9 scenarios with different combinations of: • Speed (Low / High) • Lateral Offset (Positive / Negative) • Left-Turn Lane (Single / Dual) The result of this project was a methodology that could be used for future analysis and incorporation of safety data into FYA operations decisions. No specific updates were made to the existing FYA phase mode decision spreadsheet.

MnDOT’s Connected Corridor project in 2020 contributed to progress in the development and deployment of technology for vehicle-to-infrastructure communications. Using signal phasing and timing (SPaT) data from signalized intersections to control driving speed, this project completed a cost benefit analysis for use of SPaT data in connected vehicles (CVs) and its effect on fuel efficiency. The analysis was performed by: • Outfitting four vehicles with communications and tools to record SPaT data, geometric lane data, vehicle trajectories, and speed / acceleration profiles. • Developing a traffic flow prediction model and speed control method from the previously collected data which could predict upcoming traffic and calculate the vehicle’s optimal speed to minimize fuel consumption. • Re-driving the test corridors to refine the model and speed control method. • Performing laboratory testing to predict and evaluate fuel savings from CVs driving the corridor. The analysis was completed for diverse traffic scenarios at different CV market penetrations ranging from 10% to 90%

The Connected Vehicle Traveler Alert System project was implemented to increase traveler awareness and subsequently increase safety of the traveling public by notifying them of upcoming maintenance vehicles or snowplows that may not have been in the line of sight. This was tested by sending maintenance vehicle location data gathered by an automatic vehicle location (AVL) system to a MnDOT server where the data was sent to: • The Transportation Management Center (TMC) which allowed for the display of a dynamic message sign (DMS) message to the traveling public that a maintenance vehicle was ahead. • An application running on a smart phone/mobile device of drivers who were approaching the maintenance vehicle at a specified distance.

The Bear Tracks Automated Shuttle Pilot was a research project that included the 12-month operation (August 2022-July 2023) of a Level 3 Automated Vehicle (AV) Shuttle along a 1.5-mile-long route in the city of White Bear Lake. The shuttle itself was a self-driving, electric, multi-passenger vehicle that drove at a speed between 10-12 miles per hour. The shuttle could hold up to 11 seat-belted passengers and one wheelchair passenger at a time. The shuttle's route along a residential street connected a day program for adults with intellectual and developmental disabilities, affordable housing, and the community YMCA. The demonstration was concluded before the projected end date due to the restructuring of the vehicle manufacturer and resultant lack of technical support.

To perform the assessment of pedestrian safety and driver behavior near automated vehicles (AVs), the research team: • Observed the surrounding environment as passengers on the Med City Mover shuttles (low speed, level 4, automated vehicles) and as passengers in a human-driven vehicle following the same route. • Completed two loops or circuits around the demonstration route for each observation period. • Completed a total of 22 loops of observations within the Med City Mover vehicle and a total of 14 loops of observations in the human-driven vehicle. • Recorded data about observed driver behavior as well as locations that said driver behavior occurred.