Back of Queue Warning and Critical Information Delivery to Motorists

Li, Lingxi; Chen, Yaobin; Tian, Renran; Li, Feng · 2019 · ROSA P / Purdue University. Joint Transportation Research Program

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Summary

This study addresses the safety issue of back-of-queue crashes, which account for approximately 13% of fatal accidents on U.S. highways. These incidents are often caused by low visibility, slippery surfaces, and driver distraction or drowsiness during highway cruising. While the Indiana Department of Transportation (INDOT) possesses real-time data on traffic queues via probe vehicles, there was no existing method to effectively distribute this hazard information to drivers. The research aimed to develop and evaluate a prototype in-vehicle alerting system to improve driver situational awareness before approaching traffic queues. The researchers developed a Java-based Android application that accesses INDOT’s web service to fetch real-time delta-speed events every minute. The algorithm matches the vehicle’s GPS coordinates and driving direction with interstate data to identify nearby speed events and calculate the distance to the nearest hazard. Alerts are delivered via an Android smartphone and an Android Auto device (Kenwood DDX9704S) using two methods: text/voice notifications via Google Assistant or screen mirroring. The system was evaluated using a high-fidelity driving simulator (TASI DriveSafety DS600c) and limited on-road tests. Five subjects participated in simulator studies, performing six tests each under normal, distracted, and drowsy conditions, both with and without the alert system. Results from the driving simulator indicated that the alerting system reduced intensive driving behaviors. Specifically, the data showed decreases in extreme steering angles, extreme braking percentages, and extreme deceleration when drivers approached traffic queues with the alerts active compared to those without. The system functioned effectively in both simulated and real-world highway environments, successfully delivering notifications through the Android Auto interface. The study confirmed that the prototype could mitigate harsh maneuvers associated with sudden queue encounters. The significance of this work lies in its demonstration of a feasible, low-cost method to distribute critical traffic safety information to motorists using existing smartphone and Android Auto infrastructure. By leveraging real-time probe vehicle data, the system offers a practical solution to enhance driver awareness and potentially reduce the frequency and severity of back-of-queue crashes. The findings support the integration of such alerting mechanisms into broader transportation safety strategies, highlighting the potential for improved safety outcomes through enhanced situational awareness.

Key finding

The end-of-queue alerting system reduced intensive driving behaviors, including extreme braking and steering angles, when drivers approached traffic queues.

Methodology

simulator

Sample size: 5

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).

Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

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• in vehicle coaching

Information type

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• Methodological Resource: tool software, dataset resource
• Theoretical Contribution: computational model