Confederated Tribes and Bands of the Yakama Nation
Contact:
HollyAnna DeCoteau Littlebull
509-830-6639
HollyAnna_Littlebull@yakama.com
Courtesy: Confederated Tribes and Bands of the Yakama Nation
What was the challenge?
The Confederated Tribes and Bands of the Yakama Nation is one of the federally recognized tribes in Washington state. Within the Yakama Nation reservation, there are approximately 1,200 miles of public roads. Most of the roads are in rural agricultural settings and crashes happen every day on these roadways. Of the land governed by Tribal Governments, Yakama Nation has both the highest number of pedestrian and vehicle fatality rates in Washington State. Yakama Nation Department of Natural Resources (DNR) Engineering program has been deeply concerned by these crashes and high fatality rates and determined to work on solutions addressing the safety issues and reduce and eliminate serious injuries and fatal accidents. One of the significant challenges faced by tribal and rural communities is the lack of real-time traffic and safety data, particularly on low-volume rural roads. This data scarcity hinders effective planning and decision-making processes. Moreover, when applying for grants or funding opportunities, the absence of comprehensive data undermines the credibility of their proposals and reduces the chances of securing financial support.
How did you develop and implement your solution?
To achieve this objective, we have initiated collaborative efforts with organizations including University of Washington, Washington State Department of Transportation, and the private sectors, such as AIWaysion, a University of Washington spin-off company. Leveraging the power of advanced sensing and computing technologies, we developed and implemented an innovative solution to monitor traffic and roadway conditions, driving environments, and enhance real-time warning systems.
The Mobile Unit for Sensing Traffic (MUST) device was originally developed in the University of Washington STAR Lab. Through our close collaboration with the STAR Lab, we have been actively involved in various research projects focused on developing the MUST device as a comprehensive roadside sensing and communication solution tailored specifically for tribal and rural roads. The MUST device is equipped with multi-sensing (i.e., camera, environment sensors, etc.), computing, and communication capabilities, making it ideal for monitoring traffic, detecting dangerous events, and providing real-time warning messages to road users. One of the key advantages of the MUST device is its integration of edge computing capabilities. This unique feature allows the MUST device to operate independently without relying on extensive infrastructure support, such as a broadband connection. This aspect is particularly advantageous in tribal and rural environments where access to reliable and high-speed internet connectivity may be limited or unavailable. The MUST device can perform advanced processing and analysis of data directly at the roadside. This eliminates the need for continuous and costly data transmission to a centralized server or cloud infrastructure, reducing the dependence on a broadband connection and minimizing latency issues. The device can efficiently process and analyze the collected data in real-time, enabling immediate detection of hazardous events and timely dissemination of warning messages to road users. Since the data processing and analysis occur locally on the device itself, there is no need for transmitting sensitive or personal data to external servers or cloud platforms. This ensures the privacy and security of the communities, as their data remains localized and within their control.
The US 97 - Toppenish to Union Gap - Corridor has a history of severe and fatal collisions, with the intersection of US 97 and Larue Rd. standing out as the deadliest intersection along this stretch. We have installed one AIWaysion’s MUST device at the US 97 & Larue Rd. intersection to:
What labor, equipment, plans, or materials did it take to make the solution work?
With funding from the research projects, we have collaborated closely with the University of Washington STAR Lab to customize their MUST devices specifically for implementation on rural roads with limited infrastructure support, including limited internet connectivity. Our collaboration has resulted in new features and adaptations to meet the unique data collection needs of tribal and rural communities, such as capturing data related to agricultural vehicles, heavy fog/low visibility, animals, wildfires, and more.
One crucial aspect we prioritized in the development of the MUST devices is Tribal sovereignty and privacy. To ensure the protection of community privacy, data processing is performed within the device itself, minimizing the need for external data transfers and reducing the risk of unauthorized access.
By tailoring the MUST devices to address the specific needs of tribal and rural communities, we have created a solution that not only enhances roadway safety but also empowers the community to have full control over the data generated by the devices.
To ensure the effectiveness and feasibility of our solution, we conducted a pilot deployment of the AIWaysion MUST device at the intersection of US 97 and Larue Rd. This allowed us to assess its performance in a real-world setting, gather feedback from users, and make any necessary refinements.
Building on the success of the pilot deployment, we are now actively pursuing funding opportunities to expand our solution to cover the entire US 97 corridor from Toppenish to Union Gap. Additionally, we are working on integrating the devices with existing Traffic Management Centers (TMCs) and traffic control devices, such as variable message signs, to facilitate real-time information dissemination and countermeasures.
What was the cost of implementation?
The cost of implementation primarily includes the installation of the MUST device on roadside poles, typically ranging from 20 to 30 feet in height. The installation process is carried out using a bucket truck, and it takes approximately 15 minutes for a bucket truck operator to complete the installation.
In addition to the installation, there may be costs associated with providing power to the pole if it does not already have an existing power source. Once the pole has access to power, the MUST device can operate and be controlled remotely for various functions such as data collection, software updates, etc.
Each MUST device is equipped with a cellular SIM card, enabling Internet connectivity for data transmission and communication purposes.
While the exact cost of implementation can vary depending on factors such as the number of devices deployed and specific installation requirements, the main expenses typically revolve around the installation process and the necessary power supply.
What was the positive impact/results/outcome of your efforts?
The successful deployment of our systems has yielded invaluable safety data that supports our traffic planning efforts and enhances our ability to secure grants for future projects. With the expansion of our system along the entire corridor, featuring real-time monitoring and warning capabilities, we are confident that we will achieve a substantial reduction in crash and fatality rates within the tribe.
Moreover, this project marks the beginning of a comprehensive approach to enhance transportation and infrastructure within the tribal and rural community. We firmly believe that the system implemented through this project has the potential to benefit other communities facing similar safety challenges.
New Jersey Department of Transportation
CONTACT: Gerald Oliveto
E-MAIL: Gerald.Oliveto@dot.nj.gov
TELEPHONE: (609)462-6229
Courtesy: New Jersey Department of Transportation
What was the challenge?
The bridge was closed to both marine traffic and roadway traffic for 24 hours while the emergency situation was assessed. NJDOT’s Movable Bridge Unit was able to rectify the emergency and restore the bridge to full operation without compromising safety, but a long- term solution was needed to keep the bridge in operation into the future.
Full repairs to the damaged structural steel in order to restore the bridge to its original condition would cost millions of dollars and require the bridge to be closed for extended periods of time, to both roadway traffic and marine traffic. Engineers also had concerns with extensive steel repairs on a ninety-year-old drawbridge. Given the bridge’s high-profile location in a tourist area, economic impacts of a sudden extended closure would be felt in both Avon-By-The-Sea and Belmar.
How did you develop and implement your solution?
As damage to the structural steel was isolated to the center of the bridge, the solution of a road diet was settled upon as the preferred alternative. By reducing the roadway to one-lane in each direction, NJDOT was able to balance the traffic load traveling over the drawbridge. The distribution of traffic included a ten-foot-wide striped center median that moved the travel lanes away from the damaged center-section of the bridge.
NJDOT held a meeting with affected parties, presented the need for the road diet, and took into consideration suggestions and comments from town officials. Upon completion of the project, every suggestion given to NJDOT was implemented.
With the lane configuration reduced to one lane in each direction, NJDOT was able to extend bicycle lanes that previously terminated in Avon-By-The-Sea across the drawbridge into Belmar. Previously, bicyclists needed to dismount and walk their bicycle across the bridge. The extended bicycle lanes were accomplished using an innovative fiber-reinforced-polymer mat on the bascule span. The mat is the first of its kind in New Jersey and provides a safe crossing of a steel-grid deck for bicycles. The extended bicycle lanes provide connectivity between both downtown areas and area heavily utilized by bicycle traffic year-round.
NJDOT was able to improve traffic flow at the Fifth Avenue intersection with the road diet project, as signal timings were changed and merges were eliminated. Careful monitoring of traffic throughout the year, and especially during bridge openings, have shown that the road diet lane configuration greatly improved traffic flow.
To explain the benefits of the Route 71 Road Diet Project, NJDOT kept the public informed of the project before and during the work. The Office of Communications filmed an educational video to provide the public with an advanced alert of the work and explain the reasoning for implementing the road diet at this location. This was shared on all NJDOT social media pages. Regular social media posts kept the public aware of the progress of the project. Following completion of the work, a second video showcasing the completed project was filmed and again shared on all NJDOT social media pages.
Lastly, all crosswalks on Route 71 in both Belmar and Avon-By-The-Sea were given high-visibility hatching in an effort to improve pedestrian safety in both towns.
Overall, the results have shown that the road diet configuration across the Shark River not only preserved the lifespan of the drawbridge but has had an overwhelmingly positive impact on the surrounding community.
What labor, equipment, plans, or materials did it take to make the solution work?
The work was completed in phases and included significant internal coordination with different divisions of NJDOT. The initial plans were drafted by NJDOT Movable Bridge Unit with assistance from NJDOT’s on-call consultant and reviewed by NJDOT Traffic Engineering Unit. The Moveable Bridge Unit is also responsible for monitoring of traffic flow. We engaged in coordination with government officials and stakeholders which was undertaken by the NJDOT Office of Community Relations.
We fabricated signs in-house and they were installed by NJDOT’s highway sign crew, and upgraded traffic signals and timings were enhanced by NJDOT’s highway electrical crew. Any additional striping of the pavement, installation of high-visibility crosswalks, and installation of the bicycle-safe mat was completed by NJDOT’s on-call contractor.
The GPS coordination was completed by NJDOT Transportation Operation’s Systems & Support Unit, and all social media posts and updates were completed by NJDOT Office of Communications.
What was the cost of implementation?
Option 1: Maintain structure in existing condition - 0 Initial cost, $1 million annual cost, benefit $36,000.
Option 2: Restore structure to pre-failure condition - $3.6 Million inital cost, $36,000 annual cost, benefit $4.1 million.
Option 3: Reduce lane configuration across bridge (Road Diet) - $150,000 inital cost, $36,000 annual cost, benefit $7.9 million.
What was the outcome?
After analyzing these options, NJDOT pursued Option 3 and moved forward with the Road Diet as it was the best option to keep the bridge in safe operation while maintaining roadway, pedestrian, and marine traffic.
The total cost for this project was $150,000 and was implemented in stages over the course of one month. Taxpayer funds were effectively and efficiently used to preserve the existing drawbridge while also enhancing bicycle, pedestrian, and motorist safety on the roadway.
City of Walnut Creek, CA
Contact:Matt Redmond
(925) 844-4907
Courtesy: City of Walnut Creek, CA
What was the challenge?
Regular inspection and assessment of traffic signal visibility is necessary to ensure safe and efficient transportation on our roads. Traffic signals are crucial in helping drivers navigate and follow the rules of the road. They provide visual cues that help drivers understand when to stop, go, and yield, as well as provide sufficient decision-making time for drivers to make informed decisions. When traffic signals are obstructed, it can lead to limited reaction times, confusion, collisions, and other safety hazards.
Awareness of obstructed traffic signals are typically reactionary, often being identified by citizen roadway users who call in and report the issue. The challenge in maintaining traffic signal visibility is it requires regular inspections that are labor intensive and involve trained personnel physically measuring a few hundred feet from the stop bar of each intersection approach and verifying the visibility of at least two traffic signal lights from each lane.
This process is well documented in MUTCD Section 4D-12: "The two primary signal faces required as a minimum for each approach should be continuously visible to traffic approaching the traffic control signal, from a point at least the minimum sight distance provided in Table 4D-2 in advance of and measured to the stop line."
Verifying this for all approaches to a traffic signal is both time consuming and potentially dangerous for the workers involved. When the sight distance check is done from a safer place like the sidewalk, it can be less accurate because workers typically make educated guesses from the edge of the roadway to avoid being in the lane of traffic.
To overcome these challenges, a more proactive approach is necessary. Using off-the-shelf hardware components such as a cell phone with a built-in camera and GPS receiver, a windshield phone mount, and a laptop computer, a software application can provide a cost-effective solution that is accessible to agencies of all sizes. An automated process vastly improves the safety and frequency of sight visibility checks for traffic signals by agency staff and can help maintain an unobstructed traffic network.
By automating the assessment of traffic signal visibility, our software application saves time, increases accuracy, and significantly reduces the risk of error and danger to agency personnel. This cost-effective, proactive approach offers an easier path to monitoring traffic signal visibility, ultimately resulting in safer roads for all roadway users.
How did you develop and implement your solution?
Knowing the desired end result was going to be a picture of a traffic signal taken at a specific distance, I was able to slowly work toward that goal. To obtain a sight-distance image from a video file, three input files are required.
The first file is an asset location spreadsheet with descriptions and roadway information needed to understand where the traffic signals are located, as well as the compass bearing and posted speed limit of each approach. The second file is recorded latitude and longitude data from a Global Positioning System (GPS) device, and the third a video file from a vehicle moving through each intersection of interest. All of these are processed by the SSOSS program to extract the labeled sight distance images.
To build this solution, readily available off-the-shelf hardware was used, including a modern smartphone with built-in camera and GPS antenna, a windshield phone mount, and a laptop. Implementing this solution required setting up a smartphone on the vehicle dash to record GPS points (free mobile apps are available) and video. The vehicle is then driven through as many intersection approaches as desired, ensuring data is being recorded the entire time.
Once the route is completed, the collected data/video is transferred to a computer and processed using the SSOSS program to save images of each of the driven intersection approaches.
Developing the software consisted of developing a heuristic to determine which intersection is nearest and which approach leg of the intersection is going to extract a sight distance picture from the video. Using the previously captured input files, this can all be calculated and the precise distance for the sight image can be calculated between each point in the GPS recording. That calculated sight distance uses the GPS timestamp to know exactly when the vehicle was traveling across that distance.
While the captured GPS points know when and where the vehicle is (timestamp and location coordinates), the video file is not aware of that information, so developing a video synchronization process is an important component of the SSOSS software. By extracting still frames from the video that line up with each GPS point, the time of that frame can be correlated to a timestamp on the GPS logs. By applying the video frame rate (frames per second), a start time can be calculated from which any timestamp collected from GPS point calculations can be extracted from the video. In this way, a sight distance travelled at a specific time can be extracted from the video file as a sight distance image.
The idea behind this solution is original because it automates the time-consuming aspects of manually checking traffic signal sight distances by extracting individual images from a video and saving them with detailed filenames to know exactly which intersection and approach has been captured.
What labor, equipment, plans, or materials did it take to make the solution work?
The majority of the labor required for this solution included collecting video and GPS data by mounting the equipment in a vehicle and driving through intersections of interest.
All of the hardware used to collect the data is considered commercial "off the shelf" equipment, including a basic smartphone with sufficient storage to record video from multiple traffic signal approaches, a free GPS application installed to log positional data, and a secure vehicle phone mount to capture a clear view of the roadway.
Once data is collected, GPS and video recordings are saved on a computer and the software program uses those files as well as an intersection database file to process traffic signal sight distances. The intersection database file is a simple CSV (Comma Separated Value) file that describes the intersection name, number, cross streets, each leg approach speed and compass bearing.
What was the cost of implementation?
For this solution, a smartphone, $25 phone mount, and existing laptop computer was used.
Ongoing costs include driving a vehicle through each approach of intersections of interest. Compared to traditional methods of checking sight distances, this method allowed us to easily verify 50 approaches in less than an hour of driving, providing at least a 4-times gain in productivity over manually checking these signals.
What was the positive impact/results/outcome of your efforts?
This software-based solution has resulted in significant time savings and increased productivity for our city staff. Previously, conducting sight distance checks could take 15 to 45 minutes per intersection depending on location, which includes parking and measuring the required sight distance for each of the four approaches.
With SSOSS, all 100 of our agency’s intersections-about 350 approaches-can be checked for sight distance in a single day without anyone getting out of their car. Moreover, this solution has promoted a proactive approach to ensuring traffic signals are visible to drivers, rather than a reactive approach that may leave traffic signals obstructed for longer than necessary.
We were also able to leverage existing signal timing projects by collecting video and GPS data with the floating car travel-time runs. This allowed us to use SSOSS to check the visibility of traffic signals without the need for additional personnel or equipment, resulting in a streamlined, all-encompassing process for updating our traffic signal timings and ensuring their visibility simultaneously.
By utilizing this open software solution, agencies can replicate this process and achieve similar results in terms of time and cost savings, increased road safety, reduced collision risk, and reduced injuries caused by obscured or blocked traffic signals.
Overall, SSOSS has the potential to improve road safety, increase productivity, and save valuable resources for agencies responsible for maintaining the visibility of traffic signals. Our SSOSS open-source code is available free to any agency upon request.
St. Louis County Public Works Department
Contact: Brian Boder
(218)625-3859
What was the challenge?
Courtesy: St. Louis County Public Works Department
St. Louis County Public Works Department is responsible for the maintenance and snow removal of on approximately 3,000 miles of County-State Aid Highways (CSAH), County Roads, and Unorganized Township Roads. The road network covers a vast 6,800 square mile area where road conditions vary greatly at any given time during winter storms. The problem lies within trying to monitor conditions and manage responses over this vast area. It is imperative to gather accurate information on current road and site conditions in a timely and cost-effective manner. Traditional methods of evaluating road conditions involve expensive Road Weather Information Systems (RWIS) units and field verification by staff. A single RWIS unit will cost $50K-$100K to install which makes system wide deployment over 6,800 square miles impossible on a county budget.
How did you develop and implement your solution?
St. Louis County Public Works discovered an inexpensive solar powered remote camera that includes a user-friendly operating system can capture still images day/night and can provide short video clips. The camera system is manufactured and distributed under the company name Barn Owl. St. Louis County deployed 51 Barn Owl camera systems in true field conditions and monitored their performance. The cameras are designed to be powered by 12 AA batteries or the optional solar panel that contains a rechargeable internal battery. St. Louis County chose to utilize the optional solar panel to decrease service intervals and improve cold weather performance. The cameras are compatible with virtually any cellular network currently available and are capable of being remotely commanded/triggered from the Barn Owl website. Once triggered, the camera captures an image of the current conditions along with the current air temperature. This image is then uploaded to the Barn Owl website making it possible to group the image and archive if later reference is desired.
The Barn Owl company has grown and expanded its operations and interface extensively in the short time St. Louis County has been a client. Some of the enhancements have been a direct result of this OPERA funded project. St. Louis County has helped worked through a variety of issues with the Barn Owl team. Because St. Louis County has such a large network of cameras it became apparent that efficiently sorting/classifying the cameras was going to be an issue that need to be addressed. Barn Owl has since enabled St. Louis County the ability to classify each camera by location/district which in turn, provides the opportunity to create groups making retrieving the desired image/location as efficient as possible. User roles was another issue that Barn Owl has worked to improve, providing the opportunity to have multiple users logged in under the same account and the ability to select the level of access on a case-by-case bases. This has been a huge help rolling out this program to our supervisors.
What labor, equipment, plans, or materials did it take to make the solution work?
What was the cost of implementation?
Cost breakdown: $19,074 cameras & solar panel ($374 each X 51 units)
$4,488 additional mounting hardware ($102 each X 44 each)
$2,250 bucket truck rental
$1,800 access fee & data ($300 per month X 6 months)
Total project cost = $27,612
What was the positive impact/results/outcome?
Our initial project scope began to evolve right from the start of the project. Initially we planned to install 4 cameras on crank down/folding poles at predetermined locations around St. Louis County. This concept proved to be nearly impossible due to supply chain issues and rapidly rising costs for the crank down/folding poles. While trying to secure the pole order, St. Louis County took delivery of 4 cameras at which time Public Works installed them at convenient locations to get a feel for their operation. The cameras were installed on wooden utility poles, luminaires (metal light poles), trees, and an existing crank down/folding pole. We realized during these installations that the crank down/folding poles would not be required and there were more cost-effective methods of installation. This discovery made it possible for St. Louis County to purchase an additional 47 Barn Owl camera systems, affording us the opportunity to install cameras county wide.
This camera system has the potential to assist any agency or municipality with the decision-making process regarding level of response and deployment timing to winter storm events, in turn this will ultimately lead toward safer roads for the traveling public at significantly less cost.
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