Developing Safe and Efficient Driving and Routing Strategies at Railroad Grade Crossings based on Highway-Railway Connectivity
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Summary
This report details a research project conducted between 2019 and 2022 by Michigan Technological University, the University of Kentucky, and the Escanaba & Lake Superior Railroad, sponsored by the Federal Railroad Administration. The study addresses safety, mobility, and energy efficiency challenges at highway-rail intersections (HRIs) by leveraging Connected and Automated Vehicle (CAV) technologies. The research was motivated by the need to accurately assess the economic benefits of CAV applications in mixed traffic environments, understand driver responses to new warning systems, and develop strategies to reduce congestion and energy consumption caused by train crossings. The methodology comprised five primary tasks: a literature review, economic evaluation, on-road testing, and two simulation analyses. First, researchers developed the ddRailCAT tool to estimate the shift in safety benefits from conventional countermeasures to CAV technologies. Second, they conducted on-road testing of the Rail Crossing Violation Warning (RCVW) system at real-world HRIs in Michigan, collecting data from 15 drivers using sensors, cameras, and usability questionnaires. Third, they simulated CAV Eco-Driving control strategies to optimize speed profiles approaching HRIs. Finally, they simulated CAV Eco-Routing strategies within a simplified transportation network to evaluate route choices based on predicted travel times and energy consumption. The findings indicate significant potential benefits across all domains. Economically, the ddRailCAT tool estimated that deploying RCVW applications would yield benefits ranging from $207 to $350 million for active crossings in Kentucky and $253 to $428 million in Michigan. Nationwide, predicted benefits range from $8.70 to $14.7 billion for active crossings and $6.84 to $11.6 billion for passive crossings. In the field tests, drivers reported that the RCVW system was easy to use and provided helpful information, with speed and gaze behaviors responding consistently to alerts. Simulation results showed that CAV Eco-Driving strategies achieved an average energy saving of 11.6% for impacted vehicles under 100% CAV market penetration compared to human-driven vehicles. Additionally, CAV Eco-Routing strategies demonstrated a 10% reduction in travel time and a 3.96% reduction in energy consumption compared to non-CAV vehicles. The study concludes that highway-rail connectivity via CAV technologies can substantially enhance safety, mobility, and energy efficiency at HRIs. The development of the ddRailCAT tool provides a necessary framework for avoiding double-counting benefits in cost-benefit analyses as vehicle fleets transition to automation. Furthermore, the successful demonstration of Eco-Driving and Eco-Routing strategies suggests that integrating predictive train information into vehicle control systems can effectively mitigate congestion and reduce idle time, supporting the Federal Railroad Administration’s mission for safe and efficient transportation infrastructure.
Key finding
Connected and automated vehicle technologies at highway-rail grade crossings provide substantial economic safety benefits, improve driver usability and behavior through warning systems, and significantly enhance energy efficiency and mobility via eco-driving and eco-routing strategies.
Methodology
mixed_methods
Sample size: 15
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).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | rosap | — | — | 2 | 2026-05-23 |
| archive | success | — | — | — | 1 | 2026-05-23 |
| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | success | — | — | — | 1 | 2026-05-23 |
| promote | success | — | — | — | 1 | 2026-05-23 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 3 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 24 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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Information type
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- Applied Guidance: countermeasure evaluation
- Empirical Findings: crash risk outcomes
- Methodological Resource: tool software