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

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.

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Publication Details

Journal
Vehicles
Published
2024-10-31
DOI
https://doi.org/10.3390/vehicles6040090
Citations
2
Primary Topic
Traffic Prediction and Management Techniques
Type
article
Field-Weighted Citation Impact
0.53

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article

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

Enrique D. Saldivar-Carranza, Jijo K. Mathew, Rahul Suryakant Sakhare, Darcy M. Bullock et al.
2 citations
Vehicles
Traffic Prediction and Management Techniques
0.53
article

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

Enrique D. Saldivar-Carranza, Jijo K. Mathew, Rahul Suryakant Sakhare, Darcy M. Bullock, Jairaj Desai
article en
2 citations

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.

VehiclesVol. 6(4)
Purdue University West Lafayette (US)
Indiana Department of Transportation
Openalex Percentile: Top 34%
Traffic Prediction and Management Techniques
0.53
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