Assessing Crash Risks of Evacuation Traffic: A Simulation-based Approach
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Summary
This study addresses the critical safety challenges associated with mass evacuations during hurricanes, specifically examining crash risks and traffic behavior during Hurricane Irma in Florida. Motivated by the significant congestion and 221 crashes recorded on Interstate 75 (I-75) during the evacuation, the research aims to assess the impact of evacuation on crash risks, identify changes in traffic flow behavior compared to regular periods, and evaluate the potential safety benefits of adaptive cruise control (ACC) systems. The authors argue that current traffic management strategies are insufficient for reducing crashes caused by the irregular, stop-and-go traffic patterns and driver perception errors inherent in evacuation scenarios. The methodology combines empirical statistical analysis with microscopic traffic simulation. First, the authors utilized a matched case-control approach using real-world traffic and crash data from the Regional Integrated Transportation Information System (RITIS). They analyzed 63 crashes during the evacuation period and 78 crashes during a regular period, matching each crash with non-crash traffic conditions at the same location and time. Conditional and unconditional logistic regression models were employed to identify significant traffic state variables, such as occupancy, volume, and the coefficient of variation of speed, derived from microwave vehicle detection systems. Second, to assess driver behavior and ACC impacts, the researchers developed a microscopic simulation model using SUMO software for a 9.5-mile segment of I-75. The model was calibrated using evacuation data, employing the Krauss collision-free car-following model to replicate the abrupt acceleration and deceleration characteristic of evacuation traffic. The empirical results indicate that evacuation significantly increases crash risk even after controlling for traffic characteristics. During regular periods, high upstream occupancy and speed variation were significant predictors of crashes. In contrast, during evacuations, high upstream traffic volume combined with high downstream speed variation significantly increased crash likelihood, suggesting queue formation under oscillatory speed conditions. The simulation calibration revealed that drivers exhibited higher maximum acceleration (4.5 m/s²) and deceleration (6.5 m/s²) rates during evacuation compared to typical conditions, reflecting abrupt speed changes. Regarding ACC systems, the simulation demonstrated that a 25% market penetration of ACC-equipped vehicles could reduce traffic collisions by approximately 49% during evacuation periods, as measured by surrogate safety indicators like time to collision and deceleration rate to avoid collision. The significance of this research lies in its contribution to proactive evacuation management strategies. By quantifying the specific traffic dynamics that lead to crashes during evacuations, the study provides a basis for developing real-time crash prediction models tailored to emergency conditions. Furthermore, the findings highlight the substantial safety benefits of in-vehicle driving assistance systems, suggesting that promoting ACC technology could be a viable solution to mitigate the high crash rates associated with mass evacuations. The study underscores the need for advanced traffic management strategies that address driver perception errors and traffic instability, rather than relying solely on infrastructure-based solutions.
Key finding
A 25% market penetration of adaptive cruise control vehicles reduced traffic collisions by approximately 49% during evacuation conditions.
Methodology
mixed_methods
Sample size: 66
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.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- naturalistic crash near crash
- incidence prevalence
- induced exposure
- driver post crash behavior
- evacuation egress
- telematics crash prediction
Information type
What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).
- Empirical Findings: crash risk outcomes
- Methodological Resource: dataset resource
- Theoretical Contribution: computational model