Impact of In-Cab Alerts on Connected Truck Speed Reductions in Indiana

Abstract

Connected vehicle data have the potential to warn motorists of impending slowdowns and congestion in real time. Multiple data providers have recently begun providing in-cab alerts to commercial vehicle drivers. This study reports on one such deployment of in-cab alerts on 44 corridors in Indiana from April–June 2024. Approximately 20,000 alerts were analyzed, with 92% being Congestion alerts and 8% being Dangerous Slowdown alerts. Observations showed that 15% of trucks lowered their speeds by at least 5 mph 30 s after receiving a Congestion alert, while 21% of trucks reduced their speeds by at least 5 mph 30 s after receiving a Dangerous Slowdown alert. The analysis also showed that a majority of Congestion alerted trucks encountered slow-speed traffic about 3 min after receiving an alert, while a majority of Dangerous Slowdown alerted drivers had traveled through the zone of slow speeds 2 min after receiving the alert. Although these results are encouraging, the study also found that 8.1% of Congestion alerts and 8.3% of Dangerous Slowdown alerts were received by trucks when they were operating at speeds of less than or equal to 45 mph, indicating they were already in congested conditions. The study reports that 43% of trucks that received Dangerous Slowdown alerts never reduced their speed below 45 mph. The paper concludes that it is important to converge on a shared vision for these performance measures so that public agencies, in-cab alert providers, and trucking companies can agilely improve these systems and increase driver confidence in the alerts.

1. Introduction

The Federal Motor Carrier Safety Administration (FMCSA) reports that for 2021, an 18% increase was seen for large trucks involved in fatal crashes as well as an 8% increase in the large truck involvement rate in fatal crashes per 100 million miles traveled [1]. They also report a fatality increase of 23% for large trucks in 2021 compared to 2020. The percentage of fatal work zone crashes that involved at least one large truck increased from 31.5% in 2020 to 35.3% in 2021 [2]. Improving commercial vehicle safety continues to be an important priority for all stakeholders. The FMCSA’s Safe Speed Campaign is one measure adopted by the agency to communicate the importance of complying with speed limits to commercial vehicle drivers to improve safety for all road users [3].

Recently, connected vehicle (CV) data companies have partnered with providers of in-cab devices to deliver in-cab alerts to commercial vehicle drivers in areas of congestion, dangerous slowdowns, and work zone construction to increase driver awareness of potential hazards. While past studies have utilized multiple methods to gauge driver reaction and feedback to in-cab alerts such as driver surveys, there has been a lack of visibility on actual speed data recorded from trucks receiving such alerts. Current deployments of in-cab alerts have the potential to obtain waypoint information from trucks receiving these alerts, thus enabling accurate analysis of driver response to receiving these alerts by observing changes in speeds recorded directly onboard.

Data from connected trucks receiving alerts are now available from 30 s before receival and up to 5 min after the receipt of an alert. These before/after alert data provide an opportunity to assess driver response. An analysis of a pilot deployment in Ohio in the Fall of 2023 observed that nearly 1 in 5 trucks receiving a Congestion or Dangerous Slowdown alert had reduced their speeds by at least 5 mph within 30 s of receiving such an alert [4]. The FMCSA highlighted the valuable benefits of this technology at its April 2024 Safety Research Forum and reported that 70% of drivers receiving in-cab alerts slowed down by 8−11 mph [5].

It is important to ensure that commercial vehicle drivers are given sufficient advance warning, but not so early that drivers lose confidence in the alert, and not so late that drivers have already seen and reacted to slow or stopped traffic. In-cab alert technology can provide previously unavailable visibility into driver response using speeds recorded directly onboard the truck and thus has the potential to provide quantitative evidence on the effectiveness of these alerts at improving roadway safety, vital information for stakeholders at all levels looking to implement or accelerate the deployment of this technology on their roadways.

1.1. In-Cab Alerts Background

Some large trucking companies have been utilizing in-cab alert technology for a number of years to advise truck drivers of upcoming delays at weigh stations through weigh station preclearance/bypass [6,7]. This in-cab technology has additional potential in being able to warn truck drivers of upcoming traffic slowdowns, and high crash risk locations among others. The Georgia Department of Transportation (GDOT) conducted a pilot program with one such in-cab alerts provider in 2020 to issue rollover notifications to truck drivers entering predetermined zones in the Atlanta region and reported fewer incidents in some locations [8]. More recently, a 2021 pilot study conducted by the Kentucky Transportation Cabinet (KYTC) in partnership with the Kentucky Transportation Center (KTC) and a private vendor showed the potential safety benefits of using in-cab alerts technology for warning commercial motor vehicles of upcoming roadway hazards [9]. The Virginia Department of Transportation (VDOT) has similarly partnered with an in-cab alerts provider to send out emergency weather and Congestion-related alerts to commercial vehicle drivers in an ongoing study focusing on evaluating the penetration levels of this technology and driver reaction to alerts since December 2022 [10]. These alerts are either issued through the provider’s smartphone application or directly through the electronic logging device onboard a commercial vehicle, thus reducing the need for additional equipment retrofitting requirements in the cabin.

These promising early results led to Indiana deploying this technology on its limited access corridors on 1 April 2024, and this study aims to evaluate the impact of this deployment using blackbox data collected from trucks receiving these alerts at 1-s frequency.

1.2. Motivation and Objective

The motivation behind this study was to utilize data from connected trucks that received in-cab alerts in the state of Indiana to evaluate the impact of these alerts on driver behavior as well as observe driver experience for up to 5 min after receiving such an alert. Truck speeds were used as a surrogate measure of roadway conditions experienced by the driver following an alert. The study also partitions the analysis to look at the impact of two types of alerts (Congestion and Dangerous Slowdown) on driver response. The objective of this analysis is to provide stakeholders with quantifiable data regarding the impact of in-cab alerts.

2. Indiana In-Cab Alerts Deployment

2.1. Study Location

The study location covered 44 limited access corridors in the state of Indiana including interstate routes, US routes, and selected state and local routes as shown in Figure 1. Five major primary interstates in Indiana, two running north-south (I-65, I-69) and three running east-west (I-64, I-70, I-74), have been called out on the figure for an additional geographical context. In addition to all Indiana interstates (namely I-64, I-65, I-69, I-70, I-74, I-90, I-94, I-265, I-465, and I-469), sections of selected limited access corridors such as US-20, US-24, US-31, US-231, State Route 641, Muncie Bypass, and Keystone Parkway were also included in the Indiana deployment of in-cab alerts.

2.2. In-Cab Alerts Data Summary

For this study, data obtained from commercial vehicles receiving in-cab alerts through a third-party alerts provider were utilized. These data comprised waypoint information for commercial vehicles receiving an alert at nominally 1-s frequency and the corresponding speed, bearing, geolocation, and timestamp attributes for each waypoint. The overall dataset contained approximately 5.8 million waypoints representing 20,000 instances of a truck receiving an alert for the period of 29 February to 30 June 2024.

• Approximately 91.8% of these alerts corresponded to Congestion alerts, which are often associated with recurring congestion;
• Approximately 8.2% of these alerts corresponded to Dangerous Slowdown alerts, which are generally associated with non-recurring congestion.

The deployment of these in-cab alerts was conducted by a third-party alerts provider on a corridor-by-corridor incremental basis starting 29 February through the month of March. Full deployment across the study location was in place starting 1 April 2024. Daily counts of alerts sent out categorized by the type of alert are shown in Figure 2 with an overlay text indicating periods of incremental and full deployment. In the interest of consistency, all analysis following this section only uses data for the 3-month period of April–June 2024 when the full deployment of in-cab alerts in Indiana was in effect. This reduces the total number of alerts analyzed from 20,000 to about 18,000. Since the commencement of full deployment, on average, about 237 Congestion alerts and 20 Dangerous Slowdown alerts were observed on weekdays, while about 43 Congestion alerts and 8 Dangerous Slowdown alerts were observed on weekends.

It is informative to compare these alerts with overall interstate congestion, defined as mile-hours of interstate speeds below 45 mph. Indiana has been utilizing CV data in various forms (segment-based, trajectory-based, driver events) for the past decade [11]. Figure 2 demonstrates the weekly trends in alerts sent to commercial vehicles, with weekdays seeing the highest alert counts and significant drops on weekends in line with low truck traffic utilizing the roads on weekends. Figure 3 shows a direct daily comparison between mile-hours of congestion on Indiana interstates and alert counts categorized by type from 1 April–30 June 2024. Light grey vertical lines on each plot indicate Saturdays and Sundays over the analysis period. Corresponding lower numbers in alerts as well as mile-hours of congestion are evident on the weekends on both plots. Wednesdays and Thursdays appear to show the highest congestion numbers on a weekly basis, a trend also observed in the number of alert counts in Figure 3b.

Furthermore, a more granular comparison between alert counts and mile-hours of congestion is demonstrated in Figure 4. Figure 4a shows the average interstate mile-hours of congestion observed for every 5-min period of the day between April and June 2024 with clear peaks visible during the morning and evening commuting times around 6:30 AM–10 AM and 4 PM–6 PM, respectively. These peaks align near identically with times for which the highest Congestion alert counts are seen in Figure 4b. Due to the non-recurring nature of Dangerous Slowdown events, their corresponding alert counts are dispersed throughout the day with no clear trends emerging. For the morning peak periods, 7:55–8:00 AM showed the highest alert counts and correspondingly, the same time period also showed the highest overall average mile-hours of congestion on Indiana interstates. For the evening peak periods, 3:50–3:55 PM showed the overall highest number of alerts being sent out for the three-month analysis period from April to June 2024. Correspondingly, 4:55–5:00 PM showed the overall highest average mile-hours of congestion on Indiana Interstates for the three-month analysis period.

While the visuals shown herewith represent in-cab alerts and congestion summaries at a statewide level, practitioners could easily replicate these visualizations down to the route or even local level to determine if alert timing trends align with their local observations of congested roadways. The close alignment seen between times of day with heavy congestion and corresponding higher Congestion alert counts further inspires confidence in the timeliness and validity of these in-cab alerts.

Figure 5 depicts a summary visualization of the total number of alerts sent categorized by the Indiana corridor they were sent on. I-70 westbound from the Ohio state line to I-65 in downtown Indianapolis witnessed the highest number of alerts being sent (24.5% of total) followed by I-69 southbound from the Michigan state line to I-465 near north-east Indianapolis (15.4%) and I-70 eastbound from the Illinois state line to I-65 in downtown Indianapolis (10.0%). Overall, interstate corridors in Indiana accounted for 99.5% of all alerts that were sent, with the remaining 0.5% occurring on US routes. A number of roadway construction-related closures on I-465 around Indianapolis this summer have seen significant traffic diversions onto sections of I-65 and I-70 passing through downtown Indianapolis that might be the cause for the I-70 corridor seeing a high number of Congestion alerts. Directional impacts of corridors on alert counts are also discernible from Figure 5. Alert counts are higher on corridors leading into Indianapolis than their complementary directional corridor leading out of Indianapolis. For example, I-70 W OH to I-65 sees significantly higher alert counts than its complementary corridor I-70 E I-65 to OH. This may be indicative of daily commuter traffic in the greater Indianapolis region’s road network causing high counts of Congestion alerts.

The preceding text presented a high-level summary of the in-cab alerts deployment in Indiana at a statewide as well as corridor level along with time of day and day of week trends. The text that follows analyses nearly 18,000 alerts and corresponding waypoints in further detail to observe driver response to alerts as well as driver experience in the immediate aftermath of an alert.

3. Results

Using the raw waypoint data from each truck that received either a Congestion or Dangerous Slowdown alert, the speed at which a truck received an alert was documented, as well as the change in speed with reference to the speed at alert for every truck waypoint before and after the alert using established methodologies [4]. This allows for a longitudinal system-level analysis of the percentage of trucks operating above or below the speed at which they received the alert from 30 s prior to almost 5 min after receiving the alert. Summary statistics are derived as the change in truck speed compared to speed at the time of alerting (henceforth referred to as delta speed). Due to gaps in data reporting, there are instances when a truck does not report waypoint information every second. For the results that follow, data from only those alerted trucks are utilized where the speed at alert (T = 0) is available. This filter brings down the total number of analyzed alerts from 18,031 for April–June 2024 to 14,585 (19.1% reduction).

• Figure 6a includes a summary visual for 13,525 (out of 14,585) unique Congestion alerts;
• Figure 6b includes a similar summary visual for the remaining 1060 unique Dangerous Slowdown alerts.

Both show second-by-second percentage summaries of observed speed reductions compared to the speed at the time of alerting across all alerted trucks. The horizontal axis represents the time from alerting, ranging from half a minute before the alert to 5 min after the alert. The vertical axis represents a stacked percentage visualization of the distribution of truck delta speeds for alerted trucks with an available data point in that second. Delta speeds of −5 to + 5 mph are colorized by a white band as a control group to account for minor braking or data outliers and are not considered as a significant change in truck speeds. Positive delta speeds are grouped into two broad bands of 5 to 15 mph and over 15 mph. Negative delta speeds are grouped into four bands of −5 to −15 mph, −15 to −30 mph, −30 to −45 mph, and under −45 mph. As a reference, the 0 s slice will have 100% of records in the −5 to 5 mph band to represent speeds at the time of alerting (hence zero delta speed).

Approximately 2 min after receiving a Congestion alert, the majority of trucks were observed operating at speeds lower than 5 mph of the speed at which they received the alert, with these numbers increasing to nearly 80−85% at 3 min after the alert. Of particular note are the following:

• Approximately 15% of trucks receiving a Congestion alert had reduced their speeds by at least 5 mph 30 s after receiving an alert (Figure 6a);
• Approximately 21.2% of trucks receiving a Dangerous Slowdown alert had reduced their speeds by at least 5 mph 30 s after receiving an alert (Figure 6b);
• However, following the 30-s mark, each alert type shows distinctive characteristics of driver experience;
• The percentage of Congestion alerted trucks who reduce their speeds by at least 5 mph consistently increases and stabilizes up to about 80−85% within 5 min;
• The percentage of Dangerous Slowdown alerted trucks who reduce their speeds by at least 5 mph increases to a maximum of about 55% by 2 min after the alert and then steadily decreases to about 30% at the 5-min mark after receiving an alert.

Traditional methods of gauging driver response to such in-cab alerts such as driver surveys lack the continuous monitoring capability provided by granular 1-s data obtained from in-cab alerted trucks. This lookahead period allows for a detailed analysis of the type of prevailing traffic speeds truck drivers experience after receiving these alerts and will help stakeholders finetune alerting algorithms to determine if the alerts are being sent too far out in advance of congested traffic or too late after trucks encounter congested traffic. A speed threshold of 45 mph has been widely used to indicate roadway congestion [11,12]. This same threshold was utilized to test the number of occurrences for which alerts were delivered at a time when their speeds were under this congestion threshold, indicating that the truck was already driving in congested conditions and the in-cab alert may have been redundant in warning the driver of upcoming congestion. This analysis found that 8.1% of Congestion alerts and 8.3% of Dangerous Slowdown alerts were received by trucks when they were operating at speeds of less than or equal to 45 mph, indicating they were already in congested conditions. While not ideal, these are relatively low percentages of redundant alerts and provide a performance metric for agencies to monitor alert relevance in contrast to prevailing roadway conditions. Route-by-route level analysis of this metric may yield different results as this 45 mph congested speed threshold for freeways and US routes may not hold true for state or local routes.

Summary visualizations such as these can be utilized to monitor the effectiveness of in-cab alerts at reducing truck speeds on a monthly, weekly, or even daily basis and at the systemwide or even individual route level to see if alerts sent on a particular corridor (or a particular construction zone) are more effective at reducing speeds than at other locations (or corridors). Although more than 14,000 alerts were analyzed by this study, these alerts are broadly distributed across the entire state and do not present any concentrated clusters in particular locations. With enough alerting history built up in specific locations or corridors, statistical analysis may be performed to derive insights into localized factors on alert effectiveness at reducing speeds.

4. Detailed Analysis of Alerts Using Individual Trajectories

4.1. Congestion Alerts

Figure 7a shows the recorded geolocations for one truck traveling eastbound on I−70 that received a Congestion alert around mile marker (MM 74.4) (callout i). The recorded speed as it travels through this location is shown by the color of each waypoint with free flow speeds shown in green and congested speeds (under 45 mph) indicated by the orange, red, and purple colors. Figure 7b shows a speed profile visualization of this truck pivoted off the timestamp at which the truck received an alert (T = 0 on the horizontal axis). The vertical axis indicates the corresponding speed for each second. Callout i indicates the alert location, and callout ii indicates a time instance 30 s after the alert when the truck had traveled about half a mile and was still operating at near free flow speeds. Callout iii indicates the position of the truck 165 s after the alert when its speed had just dipped below 45 mph (entering congestion). Callout iv indicates a time instance three and a half minutes after the alert when the truck had fully entered the zone of congestion with traffic operating at speeds under 15 mph.

Using an independently available dataset of commercial vehicle dash camera images, images of prevailing traffic conditions at each of the locations of callouts i, ii, iii, and iv were obtained from a commercial vehicle that passed through the same zone within 8−10 min of the Congestion alerted truck passing through. Images i and ii in Figure 7c clearly indicate no visible queues at the location the truck received an alert as well as half a mile downstream. However, image iii in Figure 7c shows the first signs of slow moving traffic, aligning well with the alerted trajectory’s speeds dipping below 45 mph in the same vicinity. At the location shown by image iv in Figure 7c, fully congested traffic is visible in the images, indicative of speeds under 15 mph. This independent validation provides further confidence in the observation that most trucks receiving a Congestion alert appear to end up in slow moving traffic within 2−3 min after receiving an alert. Future research in this space will involve the use of such dash camera images and ITS camera images among others to provide validation on traffic conditions when an alert is delivered and to isolate false positives to improve the accuracy of alerts.

4.2. Safety Impact of a Dangerous Slowdown Alert on Hard Braking Instances

Out of a total of 1060 Dangerous Slowdown alerts represented in Figure 6b, only 604 alerts recorded a minimum speed of less than or equal to 45 mph (threshold for indicating congestion similar to prior sections). The remaining 43% of alerts associated with trucks whose speeds never dipped below 45 mph may be indicative of slowdowns that either may not have required significant driver response in terms of speed reductions when passing through or false positives wherein the connected vehicle data driving the alerts was overrepresenting slow traffic speeds and thus flagging a Dangerous Slowdown alert. Using the reduced set of 604 alerted trucks that entered the congested zone at one point in their trajectory, further refining was conducted to only analyze trucks that arrived at the back of queue (BoQ) only after receiving an alert. As indicated earlier, there were instances when Dangerous Slowdown alerts were received by trucks when they were traveling at or under 45 mph (already in congested conditions). By removing such trucks where the alert could have been assumed to be redundant, a final count of 494 alerted trucks was prepared for further analysis.

Figure 8a depicts a time-space diagram of the 494 trucks that received a Dangerous Slowdown alert, pivoted off of the location when the truck was assumed to have reached the BoQ, chosen as the point where the truck’s speed first dips below 45 mph. The time at which a truck received an alert is indicated by a solid blue circle, while the time at which the maximum deceleration event for the entire truck trajectory is recorded (computed using pairwise speed differentials between waypoints that are generally 1-s apart) is indicated by a solid red circle along the trajectory. The vertical axis is limited to only run from about 5 miles (26,400 ft) upstream to 4 miles (21,120 ft) downstream of the BoQ location while the horizontal axis is limited to span from 5 min prior to about 5 min after BoQ.

Using this time-space trajectory level visual, which is extensively used in evaluating traffic signal performance measures [13,14,15], the corresponding locations and times (both with respect to BoQ) where the maximum deceleration was recorded for each truck were obtained. Negative values of time to BoQ and positive values of distance to BoQ indicate times and locations upstream of BoQ, respectively. A number of truck trajectories in Figure 8a indicate red dots close to and preceding BoQ representing trucks that possibly received the alert but did not heed it in time and had to brake just before hitting the BoQ. Red dots downstream of BoQ possibly indicate trucks having to hard-brake due to the congested queue dynamics they are in (stop-and-go traffic). Overall, 49% of all maximum deceleration events are observed to have occurred downstream of the BoQ (pointing to queue dynamics causing braking rather than the in-cab alerts themselves) while 34% of maximum deceleration events are observed within a quarter mile upstream of BoQ, pointing to queue visibility resulting in hard-braking activity. Most of the blue circles (indicating alert locations) in Figure 8a are observed to be within 2 miles of the BoQ, a fairly sufficient advance warning to trucks before they arrive at the BoQ.

Figure 8b shows a cumulative frequency distribution of the actual values of maximum deceleration across all 494 alerted trucks. The emerging consensus among various stakeholders seems to be that acceleration events with values lower than −0.25g or −2.45 m/s² constitute a hard-braking event, which may point to a possible safety concern. The callout in Figure 8b clearly shows that only 16% of trucks recorded a maximum deceleration value lower than this threshold, indicating that 84% of truck trucks did not have to hard-brake during their entire trajectory. Such trajectory level analysis methodologies, accompanying visuals, and performance measures will be vital for agencies for continuous monitoring of in-cab alert deployments to ensure the alerts are having a desired impact on calming deceleration profiles as trucks approach a congested BoQ to ultimately reduce hard-braking and back of queue crashes and thus improve road safety.

5. Conclusions

As recently as June 2024, the Texas Department of Transportation [16] partnered with providers to deploy in-cab alert services to semi-trailer truck drivers with goals to extend the deployment to more than 3000 miles of interstate roadway in the state. With more and more states adopting this technology and deploying it to truck drivers passing through the state, it is important for state departments of transportation as well as commercial motor vehicle stakeholders to be able to quantitatively evaluate the effectiveness of these alerts at reducing truck speeds and improving safety.

This study reported on the observed impacts of deployment of in-cab alerts to commercial vehicle drivers along 44 limited access corridors in Indiana for the months of April–June 2024 (Figure 3). Nearly 20,000 alerts were sent out with 92% of them being Congestion alerts and the rest being Dangerous Slowdown alerts. A two-fold analysis presented in this study looked at driver response and driver experience to receiving these alerts and the subsequent conditions encountered by them on Indiana roadways. Driver experience from 30 s prior to receiving an alert to about 5 min after receiving an alert was analyzed for this study. Observations showed that a majority of trucks receiving Congestion alerts encountered slow speed traffic up to and after about 3 min from being alerted (Figure 6a and Figure 7); whereas, on the other hand, Dangerous Slowdown alerted drivers who had passed through the zone of slow speeds by about 2 min after receiving the alert (Figure 6b). The study showed promising results wherein nearly 1 in 5 drivers receiving a Dangerous Slowdown alert had reduced their speeds by at least 5 mph within 30 s of receiving an alert (Figure 6b). While the study only reports on data received from trucks that received an in-cab alert, these observed speed reductions potentially carry inherent traffic calming impacts for the mix of traffic operating in the same vicinity as an in-cab alerted truck, thus helping increase overall driver awareness to upcoming roadway hazards such as a congestion or dangerous slowdown event.

Although these results are encouraging, the study also found that 8.1% of Congestion alerts and 8.3% of Dangerous Slowdown alerts were received by trucks when they were operating at speeds of less than or equal to 45 mph, indicating they were already in congested conditions. The study also found that 43% of trucks that received Dangerous slowdown alerts never reduced their speed below 45 mph. It will be important to improve these systems to further increase driver and fleet provider confidence in these systems and performance measures such as these that are essential to provide independent evaluations of in-cab alert deployments. These observed percentages may be attributable to a number of contributing factors including but not limited to latency and accuracy of connected vehicle data powering the alerts, rapidly changing traffic conditions, driver response, or compliance to alert notifications. Future scope may cover analysis to flag such cases to improve alerting accuracy. Furthermore, results from this analysis show that only about 16% of trucks that encountered congested speeds after receiving a Dangerous Slowdown alert (Figure 8b) experienced a hard-braking event (deceleration higher than −0.25g). With access to granular 1-s data such as that analyzed by this study, stakeholders can look to set their own safety thresholds of deceleration to highlight zones of concern that may require additional alerting.

Table 1. Summary comparisons of in-cab alerts and truck speed reductions in Indiana and Ohio.

State	Study Period	Congestion Alerts		Dangerous Slowdown Alerts
		Total Alerts	% Showing ≥ 5 mph Speed Reduction 30 s After Alert	Total Alerts	% Showing ≥ 5 mph Speed Reduction 30 s After Alert
Ohio	28 September–13 November 2023	2350	19.1	599	21.9
Indiana	1 April–30 June 2024	13,525	15.0	1060	21.2

below shows an overall summary comparison of the number of Congestion and Dangerous Slowdown Alerts as well as the percentage of trucks that reduced their speeds by at least 5 mph 30 s after an alert for a prior study on Ohio corridors in 2023 [4] as well as this Indiana study for the summer of 2024. These comparable results across states demonstrate the scalability and repeatability of the methodologies and performance measures developed by this study to other states and corridors to observe the effectiveness of in-cab alerts in reducing truck speeds.

As this area of in-cab alerts continues to evolve, it will be important to converge on a shared vision and common targets for these safety and mobility performance measures (possibly including metrics on spatial and temporal latency of alerts as well as false positive alerting rates) so that public agencies, in-cab alert providers, and trucking companies can work closely together to agilely improve these systems and increase driver confidence.