Once collected, the MDSS data must be compared to ground-truth data, allowing for a spatio-temporal alignment and analysis. Figure 4 shows graphical representations of 4 different data sources used in this study with the horizontal axis representing time of day and the vertical axis representing mile marker location along the interstate route.

Figure 4(a) is a connected vehicle heatmap showing the average speed for roughly 0.1-mile-long interstate segments, updated every 5 minutes. Connected vehicle data is essential in seeing the impact on vehicles traveling along the selected interstate. Past research in the connected vehicle space has been applied to other winter and severe weather research [14] [21] . Connected vehicle heatmaps have proved vital in measuring and visualizing freeway traffic conditions for many conditions including inclement weather events [23] .

In this case, I-65 is the selected route, from MM 0 near Louisville, KY to MM 262 near Chicago, IL; see Figure 3(a) for 50-mile incremental callouts. Figure 4(a) , callout i indicates the main impact of the storm. This callout is constant through all four data sources in Figure 4 , pointing to the same mile marker and time. The main impact occurs at 4 pm, the beginning of Friday’s evening peak around MM 110, the center of Indianapolis. These factors compounded and resulted in a large impact, seen by the greatly reduced speeds.

Connected vehicle data allows for a unique perspective on near real-time traffic patterns, but does come with some limitations. These limitations include latency, penetration rate and the quantity of data needed for analysis. Latency for the segment-based data used in this study is approximately 1 - 5 minutes; other trajectory-based data has observed latency between 30 to 60 seconds [24] . The connected vehicle penetration rate has been estimated to be above 6% along Indiana interstates in 2022 [9] , an increase from 4.3% in 2020 [12] . This penetration rate, although less than 10%, is still representative of actual traffic conditions and can be leveraged for a variety of use cases [24] . Analysis of connected vehicle data can be difficult due to the large volume of data. Prior studies found that over 500 billion data records were available, covering all 50 US states, and amassed tens of TBs (terabytes) of data. This amount of data is not only difficult to manage and analyze, but also expensive to store in a readily available database [25] [26] .

Looking at the storm progress through the state from Figure 3(a) to Figure 3(g) , the Doppler radar shows the storm impacting the northwest corner of the state first, and then progressing southeasterly throughout the state. The progression can also be seen in Figure 4(b) , with the black (snow) precipitation impacting the northern end of the interstate more than 6 hours ahead of the bottom. This figure plots National Oceanic and Atmospheric Administration’s (NOAA) High-Resolution Rapid-Refresh (HRRR) data. HRRR data provides hourly precipitation type, intensity, temperature, visibility and wind speed information gridded by 3 km by 3 km boundaries [14] [24] . The progression through the state can also be seen in Figure 4(a) , as vehicle speeds are decreased due to impaired driving conditions.

One challenging aspect affecting maintenance for both INDOT and the MDSS software was the large decrease in temperature and uncertainty on when the temperature would fall below freezing. Figure 4(c) shows a temperature profile for I-65 during this winter storm, highlighting the nearly 30-degree drop in certain areas from Thursday to Friday. Temperatures dropped more than 20 degrees by Saturday, totaling a near 50-degree drop in temperature over the course of 48 hours.

Figure 4(d) shows a friction profile for the same storm along I-65 by time and mile marker. Callout i indicates the beginning of the main storm impact and is characterized by a sharp reduction in friction values. This reduction in friction is critical to avoid, as it is ultimately what leads to many slide-offs and crashes. The friction values decreasing during this storm indicates that the reduction in vehicle speeds may not necessarily have been caused by the storm but was amplified because of it. This friction data has been effectively utilized by past studies for winter storm after-action assessments and monitoring roadway conditions [27] [28] . Previous studies have utilized snowplow telematics data in conjunction with connected vehicle data to evaluate winter operations performance measures and provide tactical adjustment opportunities based on observed traffic impacts of winter maintenance activity [22] . This study utilized snowplow telematics data from devices onboard INDOT snowplows to provide contextual information on when and where snowplows were deployed during a winter storm to quantify the agency’s response to a winter event.

Summarizing the data is important for obtaining an overall understanding of the storm’s impact quickly. Figure 5 shows two plots summarizing the overall storm impact for motorists and snow removal agencies (INDOT). Figure 5(a) shows a “ticker” plot, commonly referred to as a “ticker tape” or “stock ticker” plot, for the total interstate miles under 45 MPH in 5-minute intervals, summarizing the impact on motorists. This plot is colored by INDOT district, showing that the Greenfield district suffered the greatest impact to motorists during the peak of the storm, callout iii. Figure 5(b) summarizes the impact on INDOT, totaling the number of snowplows deployed per hour. This plot is colorized by the same INDOT districts and gives insight into when plowing, chemical application, and/or patrolling operations began, and how many trucks INDOT deployed. Callout i points to the initial deployment of trucks in both the Greenfield and Crawfordsville districts at 4:00 PM on Friday, February 16^th. This deployment comes over 12 hours before the storm’s main impact, showing that INDOT was proactive in patrolling and possibly applying chemicals well before the storm. Callout ii indicates the peak impact where nearly 500 trucks were deployed across the state. Seymour and Greenfield districts had the most snowplows deployed during this time, each with nearly 100 trucks.

Once captured, MDSS data can be plotted and compared, as seen in Figure 6 . The data for this figure correlates to the I-465 MDSS plowing segment from MM 30 to MM 46. Figure 6(a) plots the MDSS predicted and actual snow accumulation values. The predicted snow is plotted in red, and the actual snow is plotted in blue. Snow accumulation is a critical part in the overall analysis as it serves as a proxy for estimating storm impact. For most of this storm, MDSS predicted more snow than was observed, indicating a conservative approach.

It is also possible to see the rate at which the snow accumulated. Figure 6(b) combines INDOT truck deployment, INDOT solid application rate and MDSS maintenance recommendations. The background of this graph is colorized by MDSS maintenance recommendations. These maintenance recommendations are at the 6-hour interval, resulting in the 6-hour “lag” in data on the leftmost side. The first gray box, callout i, on Thursday at 6 AM represents the forecasted recommendation for that time that was released Thursday at 12 AM. In the foreground of the maintenance recommendations is a bar chart representing the number of snowplows deployed across the MDSS plowing segment per hour. These bars are colorized by the solid application rate and scaled by the number of snowplows. This combined visual ( Figure 6(b) ) allows for a quick analysis of when the MDSS software suggested maintenance, when INDOT deployed their trucks and how aggressively they applied chemicals.

Figure 6(c) and Figure 6(d) are segments of a similar connected vehicle heatmap to Figure 4(a) , but for the MDSS plowing segment on I-465. These sub figures have additional information that represents INDOT truck deployment. The blue lines indicate snowplow trajectory paths and solid black dots indicate the presence of automated brine tankers, equipment that pre-treat bridge decks and underpasses (callout iv) 24 hours before the main impact of the storm [18] [21] [29] . Figure 6(e) , Figure 6(f) and Figure 6(g) correlate to cameras located along I-65 at callouts i, ii and iii, respectively. These images are captured by roadside ITS cameras operated by INDOT. These images help to obtain visual confirmation of the actual conditions along the interstate at various locations and times.

Having collected all of the data for each MDSS plowing segment ( Figure 1(a) ) during each winter storm ( Figure 2(b) ), it is possible to compare combined visuals on a route-by-route basis. Figure 7 visualizes all data for the I-65 MDSS plowing segment. Figure 7(a) and Figure 7(b) are similar to Figure 5(a) and Figure 5(b) , respectively, but the miles under 45 MPH are classified by direction (I-65 N and I-65 S) rather than by district. Figure 7(c) is the snow accumulation for the I-65 MDSS plowing segment between MM 49.55 and MM 68.29. This segment of I-65 is highlighted by two black lines in Figure 7(d) through Figure 7(g) Callouts point to the time when the precipitation began in both the actual accumulated snow plot ( Figure 7(c) , Callout ii) and precipitation plot ( Figure 7(e) , Callout iii). The snow would take some time to accumulate, making the slight lag between the precipitation ( Figure 7(e) , Callout iii) and accumulated snow ( Figure 7(c) , Callout ii), a powerful fact-check for both data sources. This is also around the same time that the majority of snowplows are deployed across the state ( Figure 7(b) ). These figures are very powerful for agencies to analyze the overall response and determine if plows are deployed early, late or on-time and be able to adapt future protocol.

Figure 8 contains the same plots as Figure 7 , but for I-465. The I-465 MDSS plowing segment is between MM 30 and MM 46, highlighted by callout i pointing to two black lines in Figure 7(d) through Figure 7(g) . Due to the nature of I-465 being a relatively small-radius beltway, the storm impact was fairly instant across the entire route. This is characterized by Figure 8(d) , Figure 8(e) , Figure 8(f) and Figure 8(g) having vertical changes, compared to the corresponding sub-figures in Figure 7 . The importance of keeping I-465 safe and operational during the weekend of this storm was exponentially emphasized due to the NBA All-Star Game taking place. This coinciding event bringing above average vehicles can be seen in the snowplow deployment plot ( Figure 8(b) ) as the maintenance deployment began more than 6-hour prior to the storm. During the storm peak, the connected vehicle heatmap ( Figure 8(d) ) shows vehicle speeds under 45 MPH for nearly the entire 53-miles of I-465. This correlates well to the total interstate miles under MPH plot ( Figure 8(a) ) as the total sum of miles under 45 MPH is slightly greater than 100 during the storm peak.

The first case study is located along the previous I-465 MDSS plowing segment ( Figure 9 ). This segment is between MM 30 and MM 46 on the northeast corner of I-465 ( Figure 9 , Callout i). This route was selected for the February 16, 2024 storm as it had the greatest number of snowplows deployed and vehicle speeds observed to be operating under 45 MPH.

The data from this MDSS plowing segment, along with a connected vehicle heatmap produce a powerful visual aid ( Figure 10 ) to track MDSS maintenance recommendations for the 24-hour leading up to and during the winter storm. Figure 10(b) through Figure 10(g) follow the same schema as Figure 6(b) , and reduce in forecasting differential as they progress. In theory, the most accurate recommendations should come at the current hour differential, but agencies often plan multiple hours in advance to be able to mobilize operators. In this case, the maintenance recommendations are quite consistent throughout the progression, key for stakeholders to have advanced information on truck mobilization and material application. It is clear the trucks are mobilized far before the MDSS recommendations, but this is as expected. The software can make suggestions for the storm but is not suggesting any pre-treatment options. It is also ingesting AVL data, which indicates to the program that the route is already being maintained and does not suggest any further maintenance until the precipitation begins.

The second case study is located along I-69 between MM 277.54 and MM 293.06 ( Figure 11 ). This MDSS plowing segment is located just south of Fort Wayne, IN, near the southern I-69 - I-469 interchange ( Figure 11 , Callout i). This route was selected for the January 6, 2024 winter storm.

Looking at the combined MDSS recommendation figure ( Figure 12 ), it is apparent that the MDSS maintenance recommendations are inconsistent throughout the progression from 23-hour out ( Figure 12(b) ) to the current hour ( Figure 12(g) ). The 23-hour out ( Figure 12(b) ) forecast shows a suggested chemical application for the morning of Saturday, January 6th, but the subsequent forecasts do not until 1-hour out ( Figure 12(f) ). This original recommendation aligns very closely with the predicted and actual snow accumulation, validating its legitimacy.

The final case study is located along I-94 between MM 22.36 and MM 45.77 ( Figure 13 ). This MDSS plowing segment is located near Michigan City, IN, near the southwestern Michigan border ( Figure 13 , Callout i). This route was selected for the January 13, 2024 winter storm.

Figure 14 shows a very consistent and thorough maintenance recommendation trend. The 12-hour forecast ( Figure 14(c) ) is very similar to the subsequent forecasts for Saturday, January 13th. The 6-hour forecast ( Figure 14(d) ) is also similar to the subsequent forecasts for Friday, January 12th. At these forecast intervals, district stakeholders would have ample time to mobilize their operators and begin to plan for the storm, if they had not already done so. These consistent recommendations are very promising, as the proximity to Lake Michigan created a large lake effect and can often lead to abnormal storm patterns.