Episode 153: MAINTENANCE DECISION SUPPORT SYSTEM - 2026 National Briefing

This National Briefing segment is presented by Dave Huft South Dakota DOT and Ben Hersey DTN and features the Maintenance Decision Support System Pool Fund Study.  This organization is advancing winter maintenance decision support through advanced forecasting and understanding of the winter weather system yielding an operational system to aid agencies in winter maintenance treatment strategies.

Check out their work and get more information about MDSS at their website at https://mdss.dtn.com/  or by emailing Dave Huft at dave.Huft@state.sd.us or Ben Hersey at ben.hershey@dtn.com

Watch the video and presentation along with all the other briefings on our YouTube channel https://www.youtube.com/@Talkinwinterops or listen on the Talkin Winter Ops podcast at https://transportation.org/winter-weather-management/sicop-talks-winter-ops/ or wherever you get your podcast content.

Sign up for email notifications on future episodes and other communications associated with winter maintenance and winter weather management by clicking on subscribe on the Talkin' Winter Ops website at TalkinWinterOps.com

Thanks for listening in and stay safe out there!

Transcript

Rick 0:11

Welcome back to Talking Winter Ops. It's that time of year again when we sit down with other organizations working in winter maintenance to catch up on what they've been doing this past year. I'm Rick Nelson, and I'll be your host for the 2026 National Winter Maintenance and Road Weather Briefing Series. These briefings give us an opportunity to catch up with our partners in winter maintenance and road weather, learn about what they've been working on over the past year, and hear about their plans for the future. More importantly, they give you a chance to see how these ideas, tools, and initiatives might fit into your own operation. So grab your favorite beverage, settle in, and let's catch up with our partners across the country to hear about what's new and what's coming up next.

SPEAKER_00 1:00

Hi, I'm Jud Falcon, State Maintenance Engineer for the Minnesota Department of Transportation. And for the last fourteen years, I've been actively involved in the snow and ice community at the state DOT. And over the years, I've seen no shortage of new technologies, new products, and ideas come along. The challenge often isn't finding information, it's figuring out what really works and what makes sense for your organization. As someone who's been involved with the program for these many years, I've come to appreciate the role the ASHTO Winter Weather Management Technical Service Program plays in helping agencies answer those questions. One of the things that I've always valued most is that this just isn't theory, it's practitioners helping practitioners. It's a chance to learn from the experience of others. Avoid reinventing the wheel, and tap into a network of people who understand the same challenges that we face every winter. I've seen firsthand how these ideas and relationships built through this program have helped agencies improve safety, efficiency, and service to our traveling public. And with a modest annual contribution, your agency can take advantage of the same resources, webinars, workshops, and peer networks that have made this program so valuable to me over the years. If you're not already involved, I really encourage you to take a look at the ASHO Winter Weather Management Technical Service Program. Visit transportation.org forward slash winter-weather-management. You may find the answers to a challenge you're dealing with today. Or the next idea that really helps you move your winter operations forward.

Rick 2:54

This segment focuses on the maintenance decision support system pool fund. And with us is Dave Huft of the South Dakota DOT and Ben Hershey with DTN. Now, before we get started, could you guys uh sort of explain your roles uh with respect to the MDSS project?

SPEAKER_03 3:14

I I guess I can start, uh Rick. I'm Dave Huft. I uh the ITS program manager at the South Dakota Department of Transportation, and uh I manage on uh on the state side the Pool Fund study, and that that is the MDS MDSS decision support system development.

SPEAKER_02 3:35

Yep, and I'm Ben Hershey. I'm a product manager at at DTN and um in regards to the MDSS Pool Fund State project. Uh now taking over the uh the project manager lead um since uh a recent uh departure of Sean Trulson, who is retired. So um uh taking back over the reins of the project and uh some big shoes to fill um but uh support uh the interaction between the agencies and and our internal teams as well.

Rick 4:04

Great, great. Thanks guys for joining us today and giving us uh giving us a briefing on what's happening with the maintenance decision support system. So I see you've got a slide deck set up, so whenever you're ready.

SPEAKER_03 4:18

Okay, thank you, Rick. Um Ben and I will be presenting this uh this presentation. It's actually one that we shared at the FHWA Road Weather Management Stakeholder Meeting recently, and Sean Trillison, as Ben mentioned, uh collaborated on this presentation, so we want to acknowledge her. Um the Pool Fund study has uh been underway for a long time. It actually started in 2002. Indiana, Minnesota, North Dakota, and South Dakota uh initiated the study. Since then, about 21 states have participated uh in the study. It um South Dakota has been the lead state the whole time. There are eight current state DOT members, and they're highlighted in the in the darker blue. The people that are the states that have participated in the past uh along the way are highlighted in the lighter blue. Uh right now we have a $30,000 per year membership uh participation. That money is used to fund the work and also some travel. We meet twice a year in person, and uh our lead contractor is DTN. Um you might ask the question well, why do you need a maintenance decision support system? And so these reasons have existed since the inception of the study, really. Um there are rising demands of travelers and commercial carriers. People need to get from point A to point B, whether they're commuting for work or delivering freight that has to be uh delivered in a narrow window of time. We have uh high labor material, equipment and fuel costs, so anything that we can do to be more efficient in our winter maintenance is to our benefit. All of the states have constrained funding, and that's becoming even uh more of an issue uh with the rising costs of uh of everything. Reliable and timely condition reports are sometimes hard to obtain. And so one of the things MDSS is provides um very detailed and timely information on the weather conditions and what the road conditions are. Some weather conditions are difficult to anticipate, so we might we might just listen to general weather forecasts and assume that we know what's going to happen. But in MDSS, we're getting very detailed uh forecasts and have a much better idea of what's going to happen and when it's going to happen and where it's going to happen. Um winter maintenance is not a simple uh enterprise. There are all kinds of nuances, all kinds of challenges to it. And so even the pavement response to weather and maintenance treatments is very complex. And MDSS helps us to understand that. We're experiencing a lot of innovative de-icing chemicals and techniques now, especially the use of liquids and liquid-solid slurries. And so not everybody has experience using them. MDSS helps us to understand how those materials are going to behave on the road and how to best use them. We have environmental concerns. We don't want to put out huge amounts of salt or unnecessarily on the roadways. And by tailoring the treatment to the to the specific storm conditions, we can make sure that we're not wasting material and putting excess fluorides into the environment. And then something that's true of almost all of our organizations, that our size is limited of staff, we have staff turnover, and a lot of the newer people are not experienced in winter maintenance. Some of the people that have been working for 20 and 30 years aren't here anymore. And so we have people with one or two years of experience. So MDSS is valuable. The premise of MDSS is that if you know what the road is, what the characteristics of the road, and you know the current conditions on the road, and you can know what the predicted weather is, and if you understand the physics and chemistry of snow and ice and chemicals, and if you know what your available resources are, and that's material, equipment, even your work schedule, if you know those things, MDSS can recommend the treatment type and the application rate and the optimal timing to meet your level of service objectives. And it can predict what the future road conditions are going to become if you use the MDSS treatments, or if you don't, or if you use some other treatment. The cartoon diagram here illustrates what MDSS is considering. So the blue arrows represent material, the red arrows represent heat, and what you see is that material can come down from the sky, it can go up from the pavement into the into the air, it can be applied from the snow plow, it can be distributed through plowing or through traffic, it runs off the road just due to the kernel of the road and gravity. Heat also is uh travels from the sky to the pavement or from the pavement to the sky. And it can flow also up from the subgrade. In the fall, the subgrade is warm and you get a lot of heat flowing upward into the pavement. By the end of winter, the subgrade is cold, and you actually have heat flowing down from the pavement into the subgrade. And all of these factors together are considered by MDSS to predict what's going to happen when the weather uh unfolds and when the maintenance treatments are applied. Ben, I will turn it to you.

SPEAKER_02 10:17

Yeah. Thanks, Dave. So again, Dave did a great job of giving kind of that overview of MDSS and the goal here is to plan for that entire winter maintenance operations cycle, right? Everything from preseason planning, using data to help drive that to once we're in operations from event to event. Um, and then ultimately on on the the backside of an event is trying to return to both level of service and then taking the data that has been collected and trying to do analysis and how well did uh the agencies perform, is there opportunity for improvement, um, and then ultimately being able to uh provide data to management to say this is the actions we took based on the data that we had at the time and being able to back up those decisions. And so uh while initially when MDSS and when I first started really getting involved, we were really looking at that, you know, the event readiness, the tactical decisions, crew deployment, and then you know, operations, it's grown from there and it has become a you know a holistic uh capability and tool for all levels of operations, ultimately almost year-round at this point, right? With the ability of looking at archival of data and looking at post-storm events and things like that, which um you know, you talk to any winter maintenance personnel, um, they they're always thinking winter, right? And so they need an application that can help them with that. So um yeah, Dave, if you want to go to the next slide. So to for the front-end user and for the operational user, uh obviously they need a way to be able to visualize the data that Dave just spoke about and and the uh the the software the the the data and the forecasts and the trucks and all of that. And so looking at it, we have an interface that uh most of the users use on an operational basis. Um, you know, so there's both the the web um side of that um as technology has grown, you know, growing from no cell phones to blackberries and now to, you know, we're we're at iPhones and and Androids. Um that obviously there's a mobile uh companion application that provides uh quick view data for those decision makers that are out in the field and not able to sit down in front of a computer, and then actually the ability of sending data directly within the cab of the snowplow itself. And so uh empowering that operator as necessary to make decisions, and then actually uh one of the activities that we have a research task is being able to get feedback directly from the operator in real time to understand um their operations and are the recommendations being issued by the MDSS solution meeting their expectations in a somewhat of an subjective type analysis process. So, again, many different agencies use this and be able to pull that data together for an operational decision-making solution. So if we kind of jump ahead and and you know, as David mentioned, this project's been around since 2002. So, you know, some of the low-hanging fruit type things that we had, you know, at one point aren't aren't always there. And so we've definitely been able to expand out and identify additional research activities that support the overall build design and research of the maintenance decision support system. So when we look at where we're at today, specifically in the middle of our current phase of work, is uh we've got some key different areas that we're looking at from an assessment of recommendations, as I just about spoke about, um, looking at the basic modeling and specific types of weather conditions that challenge winter maintenance operations. So I think from frost to freezing rain and other specific um conditions, blowing snow comes to mind in that that we've been looking at recently. Uh looking at the level of service, and when you define how a road is supposed to be managed, you know, level service is a key item there, especially for our DOT partners that have potentially multiple levels of service that they're trying to achieve out on the roadway, both to be able to configure that in the system, but then be able to communicate that back to the operational user. As Dave mentioned also on the leak with the ICERs, um, go into that in a minute, the performance measures and accuracy, and then ultimately the route configuration process. And there's a couple cool things that we've been working on there. So if we kind of dive a little bit more into uh some of these a little bit more specifically, we'll talk about the assessment of recommendations. And so we're doing kind of a dual track where we have both an objective process where we're collecting data, understanding the recommendations that have been issued. But as I mentioned, we actually are doing direct feedback. We've been doing this for many years, actually, where we give the opportunity for the operators right inside the vehicle itself or through the web application to be able to indicate are they accepting or declining that? And if they are accepting or declining, what are some of the key things that are are they noting in that situation? And so we give them a very quick um, you know, click this box, click this box, give us a little bit of feedback, and then do some analysis there. And on average, over the years, you know, we're consistently at about 80%. And people could say, well, that that sounds low, but ultimately I we you know feel feel pretty confident with that number at 80% that they're accepting those recommendations. Obviously, though, you want to we what we want to dive into the 20%. And so we've been looking at those 20%. And if we go to the next slide, it kind of lays out some of the things that we we have identified in that process, and we're looking to try and continuously improve. And and ultimately, this comes back to good data in comes good forecast out. And so current road conditions, you know, if the model is struggling with current road conditions either because of you know bad input weather data, it got some missed data from somewhere along the way. You know, that's a huge part of that. You can see that you know, the most of the declines come from that. Also, the rel configurations, we'll speak about that in a minute. The road can the weather conditions and the forecast. Obviously, if there's a miss in the forecast, that definitely can definitely lead to uh misses in the recommendation, just the overall design of how the data is being uh managed and displayed back to the end user, some of the input parameters, such as if we're getting inrant data from RWIS locations or that the vehicles themselves, we've seen that at times. Um, and then ultimately user perspective interpretation. So, what that means is that this is subjective, right? So if something comes up and all of a sudden they don't agree with it, that can be a challenge. Um, while it still might have been the right decision, um, if the operator didn't feel like it was the right decision, they still might decline it. So it's been a really cool project. We've had a lot of great interaction directly with the operators and supervisors in the field and continue to do that today, and we'll continue to do that going forward as part of the project. Um, so if we look ahead, we talked about some of the modeling. I'm not going to go into a lot of the detail here, but we we definitely have highlighted a couple of those that are key items here. And so we've been addressing some freezing rain situations, some frost situations, and then also looking at improving the blowing snow modeling. Um, and for for some of our agencies, you know, blowing snow can be the one of the biggest challenges they have throughout the winter season itself. Um, if we jump ahead again, Dave, um kind of cover a couple of a few of the other items here. We look at the level of service and being able to analyze that. And we've found that over the years, one of the biggest challenges that an agency has is we set up routes in the system. So the plow routes that are defined is how do you define what is the level service? What is that goal? And while um an agency might have a stated goal, that may not always be the actual goal that the operators are performing, right? And so understanding that that dynamic, and so we've built some tools initially to be able to better communicate that data uh to the end user. Um, and we've also just released some data to each of the agencies that has compared their operational data on their salt usage compared to what MDSS would be recommending on those routes to actually do some comparisons. So to understand, is MDSS trying to make recommendations greater than what the um operations are doing out in the field or potentially less than that, and to be able to fine-tune that. So that data was just released to the states here within the last two weeks, and we look forward to uh further communication with them as we move forward. If we jump ahead as well, we've got the liquid de-icing side of things, and as Dave mentioned, you know, there's a lot going on with liquid de-icers, and we've seen a huge change in the industry. Um, you know, when I first started my career when working with the DOT operations, right, the the migration was away from sand, and there was a huge use of using sand in operations, and it still has its place in certain places. But you talk to a lot of the agencies, and they don't even have a place for it in any of their garages anymore. Um so we've gone through that transition, and now you're seeing more and more operations focusing on using liquids and being able to keep that de-icing chemical using less and keeping it more effective on the roadway. But obviously, there's challenges there, right? There are times when using liquids isn't the right solution, right? And so building that decision-making process and making sure that when we issue recommendations from MDSS, they are the most effective for that type of use case, right? So are you dealing with rain on the front side of it? Is freezing rain gonna be a problem in there? You know, are temperatures gonna drop very quickly? Are you gonna get too much dilution because there's too much liquid within uh the snow and ice on the roadway? So done a lot of of research and using other research out there papers and whatnot. We even went through a process of issuing a um NCHRP uh request for research. And so there's actually a secondary project that that the the states will probably be stakeholders in and even potentially DTN, um, but being done by uh other research facilities to study the impacts of liquid deacer. So pretty exciting work that's been going on within the on the liquid side of things. Um and so that this slide here kind of covers that and and those that are involved on the technical panel of that project, uh including Dave here and then others from several other states that are involved in that process. Um if we and then ultimately looking to try and improve our understanding of how those liquids play into that, and I'm not gonna go into the all of the different um uh properties there, but you can tell this is beyond just, hey, does it melt the snow and ice? That we're looking at some pretty uh scientific type uh variables to be able to make sure that we have the right configuration. Because there's so many different chemicals, we want to make sure that we do the right way or get the right data for each of those chemicals. So we've been working with quite a few of the uh agencies to uh the companies that have these different de icing chemicals to get them configured properly with MDSS, which will only drive to better recommendations from there. And then if we look ahead, um again, so there's again a lot on the de icer side, so we're gonna continue that work. We've actually been looking at um how. How do we adjust potential ratios when we look at um the slurries or the the mixtures of liquid and granular at the same time? We're seeing more and more agencies add more liquid capacity on their trucks that also have granular. And so giving guidance, potential guidance on should you have more liquid or less liquid in this situation. And there's not a lot of research out there. So we've been doing a lot of communication with uh operators as well. What they have found in the best use case situation of using either more or less liquid in certain types of events, so either blowing snow events, uh freezing rain events, you know, wet snow or more drier snow type snow events or uh you know winter events that might drive their operations. When all this data comes together, right, there's a there's a need to to measure it, right? And so there's been a process of understanding uh what is MDSS data driving to and what does management need to be able to justify the use of MDSS in their operations. And so we've had some really good discussions about how do we use the data that's being collected, and then how do we measure that, and then how do we provide guidance to to MDSS management within the agencies to prove that the usage of the application is providing value. And so we've also started looking at the impact of you know artificial intelligence and and as much machine learning to be able to understand what are those performance guidelines operationally. So um we'll continue to drive through that process as well. The other um main research item is the route configuration process, and this is very technical to the individual agencies that are involved, um, is making sure we get the the data right for the individual routes, and so each one of the plow routes goes through a vast process of configuration. Um and there are times when when that's missed and there's inaccurate data. There was some data that was presented just a couple weeks ago that we're pretty excited about, and we're going through some review with the agencies, but ultimately being able to take um snowplow AVL data to be able to calculate cycle times. And I know that that's been something that's been discussed for uh many years. There's been ultimately other projects that have been issued to try and determine that. Um, and we're looking forward to being able to present some data that was presented recently on some um machine learning and our artificial intelligence work that was completed by the the research team to be able to identify cycle times based on ABL and MDC data, which will the only drive to the improvement of the route configuration process. And what we found, and we we've known this for a while, but has continuously comes up, is that if we can get the right cycle time and the right level of service, that the accuracy of our recommendations grow grow, they become better, and ultimately we get to a better percentage of acceptance when we go back to that first project we talked about in the assessment of recommendations. So there's a lot going on with the upgrades. So this the agencies that are part of the project just got access to that AVL cycle data within the last two weeks as well, and so we're excited about what that's gonna come as we continue to move forward in the current phase of work. So ultimately, you know, the MDSS project is is is no different than what you're hearing from all the other other projects out there, right? That our AI and machine learning are beginning to become more involved in the actual operational process. Um, but rest assured that there is still very much the fundamental science behind the winter operations process, right? So it's not leaving behind that core science the fact that water freezes at 32 degrees and that the de-icing chemicals have you uh set eutectic temperature. But is there ways that we can take the data that we are collecting and be able to provide even more insights, quicker, faster, and better for the operational decision maker? And we're starting to begin to see that impact within the project as as well. And so we're excited about where that's gonna go in the future, um, but by staying true to the the science of winter maintenance operations. So ultimately, you know, um I'll turn it back over to Dave to kind of talk about the benefits from the agency standpoint, but definitely, you know, the DTN has been a proud partner in this uh for a very long time and uh look forward to continuing that.

SPEAKER_03 26:43

There are numero numerous benefits of participating in the pool fund study. Um I should mention that not all uh states that use MDSS are part of the pool fund study, but a good number of them are. And those that are get to lead the research and development in MDSS and set the direction for how the MDSS evolves. Um it's jointly led by uh by the group of states. Um it's an easy funding mechanism. Pool fund studies are pretty easy in the sense that you just dedicate a portion of your uh research federal dollars from one state to another. There's no writing checks or anything involved. It's a low-risk deployment opportunity. So if you want to try MDSS and evaluate it and understand how it works, um being involved in the pool fund study is a great way to do it. There's a lot of learning uh in the pool fund study, um, first of all, from state to state. So you get an opportunity to uh learn from others who have used MDSS before you, um, learn from others who are using MDSS with you, because it's it's not as though you learn and you're done. It's it's a constant learning process. And there's learning from the contractor to the state, there's learning from the states to the contractors, and it is very collaborative. Um there is intellectual property involved, and uh that has been jointly owned by the contractor and the states. Um MDSS does provide a forum to advance MDSS technology. So the having a body of states who are unified in purpose and direction and contribute there intellectually to the uh to the development means that you really have um something useful to contribute to the world. Um I would invite anyone who's interested uh in the pool fund study um to contact Ben or me. Um as Ben said, Sean is retired now. Don't contact her. But uh Ben and I would welcome a conversation with you to discuss this project more. And if you have questions about what MDSS is, what it does, how the pool fund study operates, we would be really happy to talk with you. Um thank you.

Rick 29:18

Thanks, Dave. Thanks, Ben. Say, uh so I I have a question for you. The the MDSS um that we're talking about here, correct me if I'm wrong, grew out of that FHWA project way back in the late 1990s, early 2000s, um, and has been and has been maturing that technology.

SPEAKER_03 29:45

Is that right? That was the motivation for starting the pool fund study. But there was kind of a big departure early on. Um one of the first activities was to evaluate the federal prototype and then uh from there identify, well, what else would we want to do? And so it was, as Yogi Barra said, when you reach a fork in the road, take it. You know, there was a fork in the road and we took it.

Rick 30:13

And and so how I I know there are some other MDSS-like products out there commercially available. Um how is this product that you've got here through the MDSS pool fund study different from some of those other commercially available MDSS-like products?

SPEAKER_03 30:35

Well, I'll I'll let Ben answer too, but in my in my mind, the big difference is the sophistication of the modeling of the pavement and weather system. Um that that to me is the distinction. Uh um the the the depth that goes into modeling how the pavement, the snow, the ice, the chemicals, the plowing all interact is I'm not aware of any other uh system that does that.

SPEAKER_02 31:08

We do I agree with what Dave said, and that we're very much uh proud of the efforts that we do from the the pavement modeling, the integration of many different data sources, and to ultimately drive towards a a very um specific uh recommendation for the individual routes, right? So it's very specific down to the to the roadways and trying to support the individual operators because there is so much of a difference from even roadway to roadway, which we find, or even along a roadway, you take an interstate roadway and how how different that can be. Um, even in a very homogeneous land terrain type situation, right? Even the difference in weather characterizations or operational decision-making processes. So I'm very proud of the effort that we've done and trying to make it very specific to that operator so that they can own that roadway when they're out there doing their winter maintenance activities.

Rick 32:04

Okay, so so correct me if I'm wrong, but being part of the MBSS pool fund study, and you you mentioned it earlier in the presentation, gets you a seat at the table. And and I think the fact that this product, the project that y'all are working on, is continues to be a work in progress to meet the needs of the folks that are part of the pool fund. So it's it's like uh uh an evolving uh uh an evolving living program that is growing to match the needs of the winter maintenance programs of those states that belong as they evolve as well. Is that is that a fair statement?

SPEAKER_03 32:49

I think that really is a good way to put it, Rick, because and Ben's alluded to it a couple of times in the presentation, even the techniques we use have changed over the years. And and so it's not as though MDSS is hitting a s a fixed target. Well, the target has been advancing and and the state of art has been advancing all this time, and MDSS has been moving in in response to that.

Rick 33:17

All right. You guys have got a great product there, and uh appreciate you participating in the briefing this year. Rick, thank you. Thank you, Rick. Thanks for listening in on this year's national briefing. You can catch them all and other talk in winter ops episodes on our website, talkin'winterops.com, at the Ashto Winter Weather Management website at transportation.org forward slash winter-weather-management, or wherever you get your podcast content. Video versions of this briefing are available on our Talk and Winter Ops YouTube channel. Now, if something in this briefing sparked your interest, you can email me or our guests to keep the conversation going. Until next time, thanks for tuning in and stay safe out there.