Freight Flows and Incident Management
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Summary
This study, funded by the Tennessee Department of Transportation (TDOT) and the Federal Highway Administration, addresses the significant economic and safety costs associated with large-scale traffic incidents, particularly those involving freight. With incident-induced congestion costing billions annually and Tennessee ranking ninth in national truck freight flows, the research investigates how Advanced Traveler Information Systems (ATIS) can mitigate these impacts. The primary objective was to model and simulate vehicle behaviors to determine how effectively en-route diversions can help truck and passenger vehicle drivers avoid unexpected congestion, reduce delay costs, and decrease the likelihood of secondary incidents. The methodology combined a comprehensive literature review of Traffic Incident Management (TIM) practices with empirical data analysis and simulation modeling. Researchers utilized data from TDOT’s Locate/IM and E-TRIMS systems, as well as the Fatality Analysis Reporting System, to analyze incident characteristics, injury severity, and duration. Statistical models, including recursive bivariate ordered probit and multilevel mixed-effects regression, were employed to correlate driver behavior with incident and roadway attributes. To validate simulation parameters, the team conducted a stated preference survey of truck drivers to understand their diversion preferences, noting factors such as route familiarity, notification methods, and value of time. These insights informed a freight en-route diversion analysis approach using the TransModeler simulation platform, which tested various scenarios involving different levels of ATIS penetration and connected and automated vehicle (CAV) technology. The findings indicate that increased penetration of traffic information and the adoption of connected and automated technologies significantly improve traffic outcomes. Simulations demonstrated that providing timely, customized travel updates allows drivers to make informed diversion decisions, resulting in higher average speeds and reduced overall travel delays. Specifically, the study found that strategies enhancing information delivery can lower both passenger and freight delay costs. The survey data revealed that truck drivers are interested in safety improvements through diversion but rely heavily on familiar routes and specific notification channels, such as smartphones. Furthermore, the models showed that diverting traffic during large-scale incidents not only reduces primary delays but also mitigates the occurrence of secondary incidents. The significance of this research lies in its provision of a quantitative framework for evaluating the benefits of Intelligent Transportation Systems (ITS) in incident management. By demonstrating that ATIS and emerging technologies can substantially reduce freight delay costs and improve network efficiency, the study offers TDOT evidence-based recommendations for policy and infrastructure investment. The simulation tools developed allow for the assessment of future scenarios involving infrastructure betterments and automation, supporting more effective project evaluation and strategic planning for managing large-scale disruptions in high-freight corridors.
Key finding
Increased traffic information penetration and connected and automated technology can increase speeds and decrease overall travel delay and costs.
Methodology
simulator
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 | — | — | 19 | 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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- Empirical Findings: crash risk outcomes, observational prevalence
- Methodological Resource: dataset resource